# Patent

## Title
Actuation system for an agent oriented architecture

## Abstract

Roughly described, the invention addresses the above problems by separating an actuation subsystem from the natural language interpretation system. The NLI develops “interpretation result commands” in response to user input, and transmits them to the actuation subsystem using a predefined interpretation result command format that is independent of the requirements of the particular back-end application. The actuation subsystem, which is the only component that is specific to the back-end application, converts the interpretation result command into one or more “application commands” and communicates it (them) to the back-end application in the form required by the specific back-end application. In some embodiments the actuation subsystem also can take results from a back-end application in application-dependent form, and convert it to a common predefined internal format that is application-independent.

## Background
CLAIM OF PRIORITY
This application claims the benefit of U.S. Provisional Application No. 60/342,229, filed 20 Dec. 2001, which is incorporated herein by reference in its entirety.


Computer program listings and Table appendices comprising duplicate copies of a compact disc, named “DEJI 1009-2 CPLA,” accompany this application and are incorporated by reference. The appendices include the following files:














AASAPAgent.java.txt
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Aug. 27, 2002


Actuation.dtd.txt
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ActuationAgent.java.txt
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ActuationListener.java.txt
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ActuationMessage.java.txt
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Agent.java.txt
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Command.java.txt
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created Oct. 1, 2002


CommandGenerator.java.txt
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InteractionAgent.java.txt
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InteractionListener.java.txt
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InteractionMessage.java.txt
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InteractionResultMessage.java.txt
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interpretation.dtd.txt
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created Oct. 4, 2002


MessageContent.java.txt
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Aug. 27, 2002


opal.txt
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Jul. 15, 2002


SfaAdapter.java.txt
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SfaAdapterInterface.java.txt
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SfaCommand.java.txt
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SfaCommandGenerator.java.txt
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StandardAgentjava.txt
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XmlAdapter.java.txt
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XmlCommand.java.txt
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XmlCommandGenerator.java.txt
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Nov. 7, 2002


XmlCreateAccountCommand.java.txt
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XmlCreateCommand.java.txt
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created Dec. 2, 2002


XmlCreateContactCommand.java.txt
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created Oct. 1, 2002


XmlCreateEventCommand.java.txt
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created Oct. 1, 2002


XmlCreateOpportunityCommand.java.txt
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created Dec. 2, 2002


XmlCreateTaskCommand.java.txt
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created Oct. 1, 2002


XmlDeleteAccountCommand.java.txt
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XmlDeleteCommand.java.txt
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XmlDeleteContactCommand.java.txt
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XmlDeleteEventCommand.java.txt
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XmlDeleteOpportunityCommand.java.txt
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XmlDeleteTaskCommand.java.txt
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XmlEditAccountCommand.java.txt
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XmlEditCommand.java.txt
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XmlEditContactCommand.java.txt
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created Oct. 1, 2002


XmlEditEventCommand.java.txt
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created Dec. 2, 2002


XmlEditOpportunityCommand.java.txt
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created Dec. 2, 2002


XmlEditTaskCommand.java.txt
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created Oct. 1, 2002


XmlReportAccountCommand.java.txt
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Aug. 28, 2002


XmlReportCommand.java.txt
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created Dec. 2, 2002


XmlReportContactCommand.java.txt
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Nov. 8, 2002


XmlReportEventCommand.java.txt
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Nov. 19, 2002


XmlReportInformationCommand.java.txt
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created Sep. 4, 2002


XmlReportOpportunityCommand.java.txt
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XmlReportTaskCommand.java.txt
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XmlReportUserCommand.java.txt
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Nov. 8, 2002


XmlRpc.java.txt
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XmlUpdateCommand.java.txt
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created Dec. 2, 2002









COPYRIGHT DISCLAIMER
A portion of the disclosure of this patent document contains material which is subject to copyright protection. The copyright owner has no objection to the facsimile reproduction by anyone of the patent document or the patent disclosure as it appears in the U.S. Patent and Trademark Office patent file or records, but otherwise reserves all copyright rights whatsoever.
FIELD OF THE INVENTION
The invention relates to natural language interpretation systems, and more particularly to techniques for supporting natural user interaction with one or more arbitrary back-end applications.
BACKGROUND AND SUMMARY OF THE INVENTION
U.S. Pat. No. 6,144,989, incorporated by reference herein, describes an adaptive agent oriented software architecture (AAOSA), in which an agent network is developed for the purpose of interpreting user input as commands and inquiries for a back-end application, such as an audiovisual system or a financial reporting system. User input is provided to the natural language interpreter in a predefined format, such as a sequence of tokens, often in the form of text words and other indicators. The interpreter sometimes needs to interact with the user in order to make an accurate interpretation, and it can do so by outputting to the user an inquiry or request for clarification. In addition, the back-end application also needs to be able to provide output to the user, such as responses to the user's commands, or other output initiated by the application. AAOSA is one example of a natural language interpreter; another example is Nuance Communications' Nuance Version 8 (“Say Anything”) product, described in Nuance Communications, “Developing Flexible Say Anything Grammars, Nuance Speech University Student Guide” (2001), incorporated herein by reference.
Of the target applications that can be made to work with a natural language interface, it has been found that many solve similar problems. There are multiple word processors, multiple banking web-sites, multiple stock researching applications. Each of these applications are similar in their domain or area of expertise, but are quite different in their publicly available programming interface. The API (Application Programming Interface) that each banking web-site presents has many different variables, representing the same concepts, but named differently. Likewise, the commands are similar, but are performed via different URLs, with subtle differences in behavior.
This relates to natural language interpretation engines because the concepts they need to interpret for a given domain can be similar while the mechanics they need to use to control different back-end applications might be vastly different. That is, the language that humans use to relate concepts about banking need to be understood by the natural language interpreter no matter which banking application is being used at the back end, while each banking application has its own API which must be used to receive commands and output results.
In the past, natural language interpretation (NLI) engines were designed to communicate with the back-end application via the specific mechanism required by the application. Different NLIs were required for different back-end applications, even in the same application domain. The different communication mechanisms could differ in their transport mechanisms (e.g. web vs. email vs. interprocess communication on the local machine), in their command structures, paradigms and syntax, and/or in the formats with which they provide results. The communication mechanism for each particular back-end application typically had to be built into the NLI engine.
Built-in communication mechanisms was problematical because, among other things, it was often necessary to re-program parts of the NLI engine whenever it was desired to support a new or different back-end application. In addition, as the NLI was reprogrammed to support new applications, it was difficult to maintain a consistent user feel for all the applications supported in the domain. A consistent user feel for the entire application domain would afford a comfort level to the user, with both the application and the natural interaction platform, thereby increasing productivity and shortening the user learning curve.
Built-in back-end communication mechanisms were problematical also for another reason related specifically to the nature and purpose of natural language interfaces. An important goal of natural language interfaces is to enable a user to communicate with application programs in a natural way, as nearly as possible to the way the user would communicate with another human being. But sometimes the task that a user wants done is not limited to a single command in the parlance of the application. For example, a user might ask an assistant to “Pay the $200 utility bill on the fifteenth and transfer the money from savings.” For a typical banking application, that might require two separate commands: one creating an electronic check payment to the utility company for $200 to be paid on the fifteenth, and a second for transferring $200 from savings into checking on the fifteenth. Sometimes, in fact, the task is not even limited to a single application domain. This might be the case for a request like “Pay the $200 utility bill on the fifteenth and send an email to John telling him it is coming,” which might call for commands to both a banking application and an email application.
Roughly described, the invention addresses the above problems by separating an actuation subsystem from the natural language interpretation system. The NLI develops “interpretation result commands” in response to user input, and transmits them to the actuation subsystem using a predefined interpretation result command format (such as XML strings that obey a predefined Document Type Definition (DTD)) which is independent of the requirements of the particular back-end application. The actuation subsystem, which is the only component that is specific to the back-end application, converts the interpretation result command into one or more “application commands” and communicates it (them) to the back-end application in the form required by the specific back-end application. In some embodiments the actuation subsystem also can take results from a back-end application in application-dependent form, and convert them to a common predefined internal format that is application-independent and may be defined, for example, in another DTD. In this way all of the development for generating a natural language interpretation system optimized for a particular back-end application can be re-used for different applications in the same application domain simply by substituting in a different actuation subsystem. Similarly, all of the complications of interacting with particular back-end applications also can be concentrated in one module that is separate from the module(s) performing the natural language interpretation tasks.
In an embodiment, the system is designed so that various target applications can be “plugged” into the system without altering any components related to user interaction or interpretation. Creating the “plug” is a matter of creating the mapping from the interpretation system to the target application API for commands, and the mapping from the target application API to the user interaction subsystem for command results.
To map the interpretation system's output to the target API, one can take many approaches. The Command Design Pattern, however, is preferred. By using command objects, the target API can be decomposed into efficient modules, clarifying the mapping process. Each command object has a specific task to be performed in the target application's space. For one application, that might be a matter of a simple method call, while another application might require a few method calls and several calculations in between. The command pattern hides those details from other developers, exposing only the behavior/responsibilities for that specific command.
Also, commands can be combined to generate more complex behavior, allowing a natural language front-end to present features that might not be accessible through the application's standard interface.
The banking example above, for example, though quite natural when expressed in language, is complex when approached with the bank's API. However, the mapping from the NLI's interpretation to command objects on the actuation side breaks the complex command down into manageable commands to be executed in a particular sequence.
Another example of this would be an opportunity management system, which actuates SalesForce.com for some commands related to sales efforts and other applications for commands relating to inventory or contact information. Thus the framework not only provides the ability to swap back-end applications easily (switch from SalesForce.com to FrontRange's GoldMine sales force automation application, for example), but it also allows a single NLI to drive collections of back-end applications in different domains as well. For example, the suite of SalesForce.com and Microsoft Exchange could be replaced by Siebel's much larger application, which handles both the opportnnity management and contact management features found in the combination of SalesForce.com and Exchange.
The Command design pattern also provides a convenient model for implementing undo functionality. Each Command, as it is built, is able to build its undo equivalent. All the current state information can be stored alongside the new state information, recording enough information to revert from the new state back to the original state if need be. Although not all systems must implement undo, this framework provides a clean solution if/when the need arises.
The following description, the drawings, and the claims further set forth these and other aspects, objects, features, and advantages of the invention.



BRIEF DESCRIPTION OF THE DRAWINGS
The invention will be described with respect to specific embodiments thereof, and reference will be made to the drawings, in which:
FIG. 1 is a functional block diagram overview of a system incorporating the invention.
FIG. 2 is a diagram of an example natural language interpreter of FIG. 1.
FIGS. 3-8 illustrate sample agent networks.



DETAILED DESCRIPTION
1. System Overview
In a typical embodiment, the problem of natural language interaction with back-end applications is decomposed into three main sections. The interpretation subnetwork translates natural language into its semantic essence, as it relates to the domain of the target application at hand. The actuation subnetwork is responsible for performing any actions related to the request made by the user, as interpreted by the interpretation subnetwork. Finally, the interaction subnetwork is responsible for presenting the actuation subnetwork's results to the user.
FIG. 1 is a functional block diagram overview of a system 100 incorporating invention. It includes one or more natural language interpreters (NLIs) 110 running under one or more Java virtual machines (JVM's), which can themselves be running on one or more different processors. Each natural language interpreter 110 communicates bidirectionally with a user 112 via an I/O subsystem 114. It receives commands from the user 112 via the I/O subsystem 114, and in some embodiments and in some situations, it can also generate replies back to the user 112 such as for requesting clarification or continuation of the user's input. The purpose of the natural language interpreter 110 is to attempt to discern the intent of the user 112 as evidenced by the input, and to generate actuation strings toward one or more back-end applications 117 that describe the functionality that the user is requesting. If a back-end application 117 has a response, or if it desires to communicate with the user 112 on its own initiative, it transmits its communication back to the user 112 via the I/O subsystem 114. As used herein, user input can include input of all kinds, including commands, queries, inquiries, and so on. All of these terms are used interchangeably herein. In addition, the user input can be transmitted to the I/O subsystem in any form (e.g. sounds or text) and over any transport (e.g. analog audio, computer bus, LAN, Internet, email, SMS, Instant Messaging, HTTP, etc.)
The I/O subsystem 114 receives input from the user 112 and communicates it in the form of XML token sequences to the natural language interpreter 110. The I/O subsystem 114 also receives output from the natural language interpreter 110 and/or the back-end application 116 for communication to the user 112. All communication between the system 100 and the back-end application(s) 117 take place through an actuation subsystem 116 which is specific to the particular back-end application(s) 117 being used.
Interpretation result commands are communicated from the NLI 110 according to a predefined format that is independent of the particular back-end application(s) being used, and the actuation subsystem 116 converts the interpretation result commands into one or more application commands. In an embodiment intended for a sales opportunity management system, for example, the predefined application-independent interpretation result command format might be XML strings prepared according to the DTD set forth in the file named interpretation.dtd.txt in the accompanying Computer ProgramListing and Table Appendices.
The actuation subsystem 116 transmits each of the application commands to the appropriate back-end application 117, in an application-specific form as required by the particular application 117. The application-specific form is typically one defined by the vendor of the application. The application command format required by one application can be very different than that required by another application from a different vendor in the same application domain, but the application-independent actuation command format is the same.
As an example, in the sales opportunity management system above, the application-specific form is the one defined by salesforce.com's XML API, which provides a programmatic means to access and change data for certain entities represented in the back-end system. According to this API, Each API call is initiated by a request from a client such as actuation subsystem 116, and has a response from the salesforce.com server. API calls are encoded using XML-RPC, a remote procedure call (RPC) mechanism encoded in XML. XML-RPC is a well known standard, and is described at http://www.xml-rpc.org/spec incorporated herein by reference. Each API call has a method name, which is one of a predefined set of verbs such as insert, update, delete, query, login and logout, plus an ordered list of exactly one argument, which is a struct of name-value pairs appropriate for the particular verb. An XML-RPC response is either a single value (which could be a struct or an array), or a fault response. XNL-RPC calls are made to a salesforce.com application server hostname, with a particular servlet name.
The system 100 runs on a Natural Interaction Platform that includes natural language interpreters 110, the actuation subsystem 116, the I/O subsystem 114 and also one or more interaction servers 120. Each instance of these components executes inside its own JVM, communicating with each other through RMI (Remote Method Invocation). Alternatively, in various installations some or all of the various components can execute together inside a single JVM.
The servers and components of the platform can be created or restarted independently with no particular sequence required. The I/O and interpretation components are launched with their RMI addresses and the RMI address of one or more interaction servers specified in the program argument list. The interaction servers 120 are launched with their RMI address and “Opal” configuration file specified in the argument list.
When an I/O or interpretation component is launched it binds itself to the RMI registry and starts a thread (PingServer) to check the state of servers 120 specified in the launch parameters. If a server 120 is available, the thread immediately registers the component with the server. If no server is available, then the thread periodically checks for an available server. When it finds one it will register the remote object component with the server. When an interaction server 120 is launched, it binds itself to the RMI registry and waits for remote components to register themselves. When a component registers, the server 120 creates a remote object property class that it stores to track and administer the components. This class starts a thread (PingRemoteObject) that periodically checks the existence of the remote object so that it can unregister the object from the server 120 if the object process terminates.
The remote components invoke methods in the interaction server through a NaturallnteractionServerlnterface. These methods include mechanisms whereby components can register themselves with the server 120 and interaction blocks can request new interpretation sessions and register existing sessions. The interaction server 120 invokes methods in the I/O subsystem 114 through an IOModeInterface. The interaction server 120 and I/O subsystem 114 invoke methods in the interpretation components, including requests by the I/O subsystem 114 for natural language interpretations, through an InterpretationNetworkInterface.
The interaction server 120 therefore manages the sessions with which the I/O subsystem 114 works with a natural language interpreter 110. When the I/O subsystem 114 receives input from a new user, it requests an interpretation session from the interaction server 120 with which the I/O subsystem 114 is registered. The I/O subsystem 114 can recognize a new user by methods that depend on the I/O modality, such as by email address for email modalities, phone number for SMS modalities, or HTTPSessionID for web modalities. The interaction server 120 accesses a user database 124 to validate the user and retrieve any required login information and session information for the back-end application(s) 117, and then assigns a unique ID for the requested interpretation session. The interaction server 120 returns to the I/O subsystem 114 with, the interpretation session ID, the application login information and (in embodiments that support more than one natural language interpreter instantiation) an identification of an NLI 110 toward which the interaction block 118 should route the user's requests. The interaction block 118 stores this information in a table and uses it to forward queries into the assigned natural language interpreter 110 originating from the particular user.
The system 100 is designed to recover automatically if any one of the processes fails, so long as there is at least one more instance of that process still running. If a natural language interpreter 110 fails, then the I/O subsystem 114 will receive a remote exception when it tries to forward a user query to the interpreter 110. At this point the I/O subsystem 114 returns to the interaction server, requests a new interpretation session, and submits the query to the new interpreter 110 assigned. If an interaction server 120 fails, pinging threads in the remote object components will detect this and try to register with another interaction server 120 or wait for a new one to be started. If the I/O subsystem 114 fails, then it is up to the actuation subsystem 116 to detect this and route any application command results via other available I/O components.
2. Natural Language Interpreter
The natural language interpreter 110 can be any functional unit that attempts to discern from user input, the intent of the user relative to the back-end application 117. It receives commands from the user 112 (via the I/O subsystem 114), and outputs toward the actuation subsystem 116 an indication of the function or functions that the NLI believes the user intends. Many NLIs operate in part by attempting to spot concepts in an incoming token sequence, typically by reference to specific keywords or classes of keywords. Some of the keywords are the concepts themselves (like “Monday” in the phrase, “I'll be there on Monday”), and some of the keywords are indicators of where the concept is likely to appear (like “on” in the same phrase). The NLI 110 can include, for example, Nuance Communications' Nuance Version 8 (“Say Anything”) product or an AAOSA agent network from Dejima, Inc.
In an AAOSA agent network, agents contain policy conditions which either do or do not apply to the incoming text string, and if they do, they make a claim to at least a portion of the incoming text string. Such claims imply a tentative interpretation of part or all of the input string. For example, if the back-end application subsystem 116 includes an airline reservation system, then an agent network in NLI 110 might be designed to include policy conditions to look for any of the words “depart”, “departing” or “leaving”, earlier in the text string than the word “from”, which in turn is earlier in the text string than a city name.
FIG. 2 is a diagram of an example AAOSA agent network 202, used for implementing a natural language interface to a back-end application 117 that is designed for financial reporting. In particular, the back-end application 117 in this example is an Internet-queryable database provided by EDGAR Online, Inc. More information about the database can be found at http://www.finsight.com, visited Feb. 5, 2002, and incorporated herein by reference. This product permits a web client to retrieve desired financial information automatically using URL, based queries. The interface to this back-end application, therefore, includes the formulation of the appropriate URL for satisfying the user's inquiry.
Only a partial representation of the agent network 202 appears in FIG. 2. A more complete version of the network is described in the Opal file named opal.txt in the accompanying Computer Program Listing and Table Appendices. An Opal file is an XML document which defines certain properties of each of the agents in an agent network. The agents themselves are implemented as instances of java classes and subclasses, and the Opal file specifies, for each agent and among other things, the specific class or subclasses from which the agent is to be instantiated, which other agents each particular agent listens to for each particular kind of message, as well as (for most agents) a set of one or more “interpretation policies” which implement the interpretation task for which the particular agent is responsible. The Opal file is used by an Opal converter program at system startup time to instantiate the entire agent network such as network 202. The Opal file in opal.txt can be understood by a person of ordinary skill, especially with the assistance of the Dejima Policy Reference, Platform 3.0, a copy of which is attached hereto in Appendix A. In addition, it will be understood that the Opal file uses standard XML coding, in which (among other things):
![CDATA[ . . . ]] indicates that “ . . . . ” is to be interpreted as Character data;
&letter; means “<” (left angle bracket)
> means “>” (right angle bracket)
& means “&” (ampersand)
' means “′′” (apostrophe)
" means “ ” (quotation mark)
As described in the Dejima policy reference, an interpretation policy contains, among other things, a policy condition and a policy action. When an agent receives a message from another agent to attempt to interpret and input string, it compares the input string to each of the agent's policy conditions in sequence. If a condition does apply to the input string, or to part of the input string, then the policy makes a “claim” on the applicable portion of the input string, and returns the claim to the agent that requested the interpretation. A claim identifies (among other things) the agent and policy which is making the claim, the portion of the input string to which the claim applies (called the claim “focus”), the priority number of the agent or policy, and also a confidence level which indicates how well the input matches the policy condition. The priority and confidence levels, and the focus, all can be used subsequently by upchain agents for comparison with other claims made by other downchain agents, so as to permit the upchain agent to select a “best” one among competing claims.
Policy conditions are written as expressions made up from operators and operands. The various operators include unary operators such as <exists>, <exact>, <substring>, <accent>, <accent-substring>, REPEAT and RECURSIVE. They also include binary operators such as OR, AND, ORDERED, ADJACENT and COMBO. The operands on which an operator can act include tokens (words, strings, numbers, symbols, delimiters), text files (which can contain their own policy conditions), databases, and claims made by other policies. If a first policy condition (the “referencing policy condition”) refers to a second policy (the “referenced policy”) previously evaluated in the same agent, then any claim made by the referenced policy can be figured into the evaluation of the referencing policy condition in the manner specified by the operators. If a policy condition refers to another agent (the “referenced agent”) downchain of the current agent (the “referring agent”), then the claim or claims returned by the referenced downchain agent are figured into the evaluation of the referencing policy condition in the manner specified by the operators. Note that a policy condition that references a downchain agent cannot be completely resolved until the input string is passed to that other agent for comparing to its own policy conditions. In one embodiment, the referencing agent passes the input string to each downchain agent only upon encountering the agent's name while evaluating a policy condition. In the present embodiment, however, the referencing agent passes the input string to all downchain agents mentioned in any policy condition in the referencing agent, before the referencing agent begins evaluating even its first policy condition.
Returning to FIG. 2, loading of the interpretation network is initiated by the interaction server 120 when it is initialized. The interaction server has the knowledge of which network (opal file) the system is using, and when this value is initialized or changed the server 120 tells all interpretation systems to load this network. The system then reads the Opal file (such as that in opal.txt) and instantiates all of the agents designated therein. This includes the actuation subsystem 116, which as discussed below, is subclassed from the same classes from which other agents of the NLI are subclassed. Subsequently, when an input token sequence is ready, the I/O subsystem 114 passes it to the interpretation network 202 by calling the Process method of an Interaction agent 122 of the I/O subsystem 114. Whenever the Interaction agent receives a token string, it initiates an interpretation attempt into the agent network by communicating the input token sequencer in an object of class “InitiateInterpretationMessage”, to the Top agent of the network. In the network of FIG. 2, the Top agent is System agent 212. The Top agent contains one or more interpretation policies whose policy conditions, in a typical network, do very little aside from referencing one or more other agents deeper in the network. System agent 212, for example, contains a single interpretation policy whose policy condition does nothing more than reference the Finance agent 214. Such a policy condition applies to the input token string if and only if the Finance agent can make a claim to at least part of the input token string. When System agent 212 encounters this policy condition, therefore, it forwards the input token string to the Finance agent 214 in an object of class “IntepretItMessage”. The Finance agent 214 is thus considered to be “downchain” of the System agent 212, and the System agent 212 is considered to be “upchain” of the Finance agent 214.
When the Finance agent 214 receives the input token sequence, it first looks in its policies for policy conditions that make reference to further agents downchain of the Finance agent 214. If there are any, then the Finance agent 214 forwards the input token string to each of the further downchain agents in an “IntepretItMessage” and awaits replies. In the embodiment of FIG. 2, the Chart, Report and Companies agents 216, 218 and 220, respectively, are all referenced in the Finance Agent's policy conditions and are therefore downchain of the Finance Agent 214. Each agent downchain of the Finance agent 214 does the same upon receipt of an InterpretItMessage. When an agent has received all replies (or in certain embodiments, times out on all replies not yet received), the agent tests the input token sequence against the agent's policy conditions. The agent processes the input in order from the agent's first policy to its last policy. Each policy makes all the claims it can on the input. Subsequent policies in the agent can make reference to claims made by previously processed policies in the agent, as well as to claims made by downchain agents. After all policies have made their claims the agent uses a predetermined algorithm to select the “best” claim. If the best claim is one made from a non-grammatical condition (e.g. combo operator), then the sub-claims are also selected. The agent then returns the selected claim or claims to the agent's upchain agent in an object of class ClaimMessage. If the agent is not able to make any claims on the input, then the agent passes upchain an object of class NoClaimMessage.
Thus in the embodiment of FIG. 2, the Finance agent 214 eventually will receive any claims made by its downchain agents and will refer to such claims in the evaluation of its own policy conditions. The Finance agent 214 then will respond to the System agent 212 with either a ClaimMessage or a NoClaimMessage. If the System agent 212 receives a NoClaimMessage, then the System agent's single policy does not apply and no user-perceptible action is taken in response to the input utterance. If the System agent 212 receives a ClaimMessage, then the System agents policy does apply.
The System agent 212 evaluates its own policy conditions in the same manner as other agents in the network, and each such policy again makes as many claims as it can on the input. But because the System agent 212 is the Top agent, it does not transmit any resulting claims (or NoClaims) to any further upchain agents. Instead, as the Top agent of a network, after selecting one or more “best” claim(s) in the manner described above, System agent 212 has the responsibility to delegate “actuation” to the agents and policies that made up the claim(s). This process, which is sometimes called “executing” the wining claim, takes place according to the “action” part of the winning policy or policies in the Top agent. The action part of a policy builds up an interpretation result command, in the form of an interpretation result string in the present embodiment, in a manner similar to that in which policy conditions build up the result of the condition. That is, it builds up the interpretation result string by string operators and operands that can include words, numbers, symbols, interpretation result sub-strings already created by other policies within the same agent, and interpretation result sub-strings created by other downchain agents. Typically the downchain agents referred to in the action part of a policy are the same agents referred to in the condition part of the policy. Also typically, the interpretation result string built up by this process is an XML string that satisfies a DTD that has been predefined for the subject application domain. In other embodiments the interpretation result command might not be XML, and might for example be one or more binary objects, or of any other predefined format.
In order to fill in the interpretation result sub-strings defined by downchain agents, the Top agent sends an object of class DelegationMessage to each downchain agent referenced in the action part of the winning policy(ies). In the embodiment of FIG. 2, the System agent 212 contains only one policy, the action part of which does nothing more than delegate to the Finance agent 214. The interpretation result sub-string returned by the Finance agent 214 therefore will be the interpretation result string output of the network. The DelegationMessage received by an agent includes a reference to the particular policy or policies of that agent which formed part of the winning claim. Upon receipt of such a message, therefore, the agent executes the action part of each of its policies that formed part of the winning claim, issuing DelegationMessages of its own to its own downchain neighbors as called for in the action part of the such policies, and building up an interpretation result sub-string for returning to the agent's upchain caller. Interpretation result sub-strings are passed to upchain agents in objects of class ActuationMessage, ultimately once again reaching the Top agent of the network (System agent 212). This agent then forwards the interpretation result message in the form of an XML string, in an object of class ActuationMessage, toward the actuation subsystem 116 (FIG. 1). An example ActuationMessage class definition, in java, is set forth in the file ActuationMessagejava.txt in the accompanying Computer Program Listing and Table Appendices. The ActuationMessage class extends a MessageContent class whose definition is set forth in the java file named MessageContentjava.txt in the accompanying Computer Program Listing and Table Appendices.
Thus it can be seen that interpretation of the user's intent takes place in an agent network in a distributed manner. Each of the agents in agent network 202 can be thought of as having a view of its own domain of responsibility, as defined by its interpretation policies. Typically the application domain is organized by the designer into a hierarchy of semantic sub-domains, and individual agents are defined for each node in the semantic hierarchy. In the embodiment of FIG. 2, for example, the Finance agent 214 is responsible for all semantics that relate to finance (i.e., all queries in the entire application domain in this example). The Chart agent 216 is responsible for detecting and acting upon parts of user queries that have to do with financial charting, whereas Report agent 218 is responsible.for detecting and acting upon parts of user queries that have to do with financial reporting. Companies agent 220 is responsible for detecting and acting upon parts of user queries that have to do with company names. The Companies agent 220 has downchain thereof a Symbol agent 222, responsible for detecting and acting upon parts of user queries that involve company names provided in the form of an trading symbol; a Name agent 224, responsible for detecting and acting upon parts of user queries that involve company names provided as a company name; an Exchange agent 226, responsible for detecting and acting upon parts of user queries that involve companies that are trading exchanges, and so on.
It can also be seen that the Top agent of a network is responsible for receiving input and initiating queries into the network, and the agents representing the functionality of the system (the agents constructing their interpretation result sub-strings without reference to further agents) are the lowest order nodes (leaf agents) of the network. The network operates in two main phases: the interpretation phase and the delegation phase. In the interpretation phase, an initiator agent (such as the Top agent) receives the input token sequence and, by following its policy conditions, queries its downchain agents whether the queried agent considers the input token sequence, or part of it, to be in its domain of responsibility. Each queried agent recursively determines whether it has an interpretation policy of its own that applies to the input token sequence, if necessary further querying its own further downchain agents in order to evaluate its policy conditions. The further agents eventually respond to such further queries, thereby allowing the first-queried agents to respond to the initiator agent. The recursive invocation of this procedure ultimately determines a path, or a set of paths, through the network from the initiator agent to one or more leaf agents. The path is represented by the claim(s) ultimately made by the initiator agent. After the appropriate paths through the network are determined, in the delegation phase, delegation messages are then transmitted down each determined path, in accordance with the action parts of winning policies, with each agent along the way taking any local action thereon and filling in with futher action taken by the agents further down in the path. The local action involves building up segments of the interpretation result string, with each agent providing the word(s) or token(s) that its policies now know, by virtue of being in the delegation path, represent a proper interpretation of at least part of the user's intent. The resulting interpretation result string built up by the selected agents in the network are returned to the initiator agent as the output of the network. This interpretation result string contains the fields and field designators required to issue a command or query to the back-end application subsystem 116, to effect the intent of the user as expressed in the input token string and interpreted by the agent network.
3. Messaging Interface
In one embodiment, the NLI 110 communicates directly with the I/O subsystem 114 and directly with the actuation subsystem 116. In the embodiment of FIG. 1, however, the I/O subsystem 114 processes information internally by way of method calls, whereas the interfaces to the NLI 110 and actuation subsystem 116 are by way of messages. The embodiment of FIG. 1 therefore includes an interaction agent 122, which acts as a bridge between the messaging interface of the agent network in the NLI 110 and the method calls internal to the I/O subsystem 114. As a bridge, it can be viewed either as part of the interpretation subsystem, or as part of the I/O subsystem 114, or both, or as a separate independent module. The main function of interaction agent 122 is to convert method calls from inside the I/O subsystem 114 into messages that it sends into the NLI 110, and to convert messages that it receives from either the NLI 110 or the actuation subsystem 116, into information returned by a method call made previously from within the I/O subsystem 114. An example interaction agent class definition, in java, is set forth in the file InteractionAgentjava.txt in the CD-ROM appendix hereto.
InteractionAgentjava implements class InteractionListener, which is set forth in the file InteractionListenerjava.txt in the CD-ROM appendix. InteractionAgentjava also extends a StandardAgent class whose definition is set forth in the java file named StandardAgentjava.txt in the accompanying Computer Program Listing and Table Appendices. The StandardAgent class extends an Agent class whose definition is set forth in the java file named Agentjava.txt in the accompanying Computer Program Listing and Table Appendices. The Agent class is the base class for all the agents in FIG. 2. This class defines the naming mechanisms and assigns the unique agent identifiers. The communication methods are defined in this class—listeners are registered and un-registered, and the method of transport/broadcast is set. The StandardAgent class builds on the messaging implementation defined in the Agent class, handling a few basic messages that are part of the basic agent messaging protocol. Log messages and Done messages are examples of these types of messages—essentially all the bookkeeping work needed to support functions at a higher level is done within the StandardAgent. All agents in the network of FIG. 2 are instances of subclasses of the StandardAgent to guarantee that the proper messaging protocol is adhered to. Also, the StandardAgent maintains the history of messages sent to the agent and sorts them according to chain identifiers. It also provides a hook so that related (same chain identifier) data can be stored by the agent (or its subclasses).
The messages sent by the NLI 110 or the actuation subsystem 116 to the interaction agent 122 for forwarding toward the user 112 are objects of class InteractionMessage. A message of this class can contain any object within it, though the I/O subsystem 114 must know how to handle the data within the InteractionMessage. Facts (data from the system), questions (when more information is needed), and lists (when a choice must be made between several items—for ambiguities—did you mean A or B?) are all different types of data that can be contained within an InteractionMessage. An example InteractionMessage class definition, in java, is set forth in the file
InteractionMessage java.txt in the CD-ROM appendix hereto. The InteractionMessage class extends a MessageContent class whose definition is set forth in the java file named MessageContent.java.txt in the accompanying Computer Program Listing and Table Appendices.
The messages sent by the interaction agent 122 into the NLI 110 are objects of class InteractionResultMessage. A message of class InteractionResultMessage generally encapsulates a user query, which is processed into a command by the agent network in the interpretation subsystem. Occasionally, a message of class InteractionResultMessage contains followup information from the user 112 and prior context. For example, when the user has answered a question, the answer to the question and the context of the question is maintained. An example InteractionResultMessage class definition, in java, is set forth in the file InteractionResultMessagejava.txt in the CD-ROM appendix hereto. The InteractionResultMessage class also extends the MessageContent class mentioned above.
4. Actuation Subsystem
The actuation subsystem 116 is an object of class ActuationAgent, an example of which is set forth in the java file named ActuationAgentjava.txt in the accompanying Computer Program Listing and Table Appendices. The ActuationAgent class extends the AASAPAgent class, which is defined in the java file named AASAPAgentjava.txt in the accompanying Computer Program Listing and Table Appendices. The AASAP (also called AAOSA) agent class defines the methods for performing the natural language parsing and interpretation of a user query, and is therefore also a superclass for all interpretation agents in the NLI 110. The AASAPAgent class in turn extends the StandardAgent class discussed above.
The actuation subsystem 116 is responsible for converting the output of the NLI 110 into one or more commands to be executed by the back-end application(s) 117. It is also responsible for creating a message for the user with any results of the command execution. The message for the user can be data, a question, a list of choices, or an error message. The actuation subsystem 116 can accomplish its tasks in a variety of ways, mostly dependent on the application being created.
In a home theater system, for example, the actuation subsystem 116 is responsible for creating IR signals to control audio/visual components in response to the NLI output. In the finance application, the actuation subsystem 116 retrieves stock data from a local or remote database and creates URLs for stock graphs. In a sales opportunity management system, the actuation subsystem 116 manipulates a web based opportunity management application for the user, allowing the user to create and edit information, and run reports on their information, at that web site.
The API that the actuation subsystem 116 exposes to the NLI 110 is simple, which facilitates its plug and play nature. At an abstract level, the actuation subsystem 116 transforms application-independent “interpretation result commands” that it receives from the NLI 110, into application-specific “application commands” that it issues to the back-end application(s) 117. In one embodiment, each interpretation result command transmitted by the NLI 110 is specified as an XML string satisfying a predetermined interpretation DTD, such as that in interpretation.dtd.txt, and is contained within an object of class ActuationMessage. The application commands issued by the actuation subsystem 116 to the back-end application(s) 117 are in whatever form and are transmitted by whatever transport mechanism is required by the back-end application 117. In the sales opportunity management application, for example, application commands are transmitted to the SalesForce.com server via HTTP to salesforce.com's XML-RPC interface. Example software to encapsulate interpretation result commands for the XML-RPC interface is set forth in the file named XmlRpc.java.txt in the accompanying Computer Program Listing and Table Appendices.
The actuation subsystem 116 also transforms application-specific “application results” that it receives from the back-end application(s) 117, into application-independent application results that it communicates to the I/O subsystem 114. The application results returned by the back-end application to the actuation subsystem 117 are in whatever form and are transmitted by whatever transport mechanism is used by the back-end application 117. In the sales opportunity management application, for example, the application results are communicated from the application 117 to the actuation subsystem 116 by HTTP and are formed according to the salesforce.com API. And each application result communicated from the actuation subsystem 116 toward the I/O subsystem 114, in one embodiment, is specified as an XML string satisfying a predetermined internal “common interaction format” DTD and contained within an object of class InteractionResultMessage. The file named actuation.dtd.txt in the accompanying Computer Program Listing and Table Appendices sets forth an example of such an internal common interaction format DTD for the sales opportunity management application.
The above high-level functions of the actuation system 116 can be performed in a variety of ways. The command pattern architecture discussed herein is one of the many ways that the functions can be handled. According to the command pattern architecture, each command that is available in the interpretation result DTD is instantiated as a separate java object, which is defined within a class hierarchy. The class hierarchy is specific to a particular back-end application 117, so that the instantiated command objects will perform the proper conversions from interpretation result commands to application commands, and from application-specific command results to application-independent command results, that are appropriate for the particular back-end application 117. Separate (though parallel and often overlapping) class hierarchies are defined for different applications in the same application domain, and frequently also for different available interfaces to a single application (e.g. http, XML and SQL interfaces). The particular class hierarchy from which to instantiate command objects is part of the particular actuation agent that is called for in the Opal file for the system 100. The command objects are refefred to in the specific adaptor. Each command object includes an “execute” method that the actuation agent calls in response to receipt from the NLI 110 of that interpretation result command.
An advantage of using the command pattern architecture is that the underlying implementation of the objects can be changed without changing their interface to the remainder of the actuation agent, or indeed to the remainder of the agent network. For instance, a change from an http API to an XML API for a particular back-end application 117 requires changing only the bridge code within the command object classes. Since all the commands share a common API to the actuation agent, the actuation agent notices no difference between using the http commands or the XML commands. Likewise, a salesforce automation system written to use the SalesForce.com back-end application can be ported to use a database created by another salesforce automation application vendor (e.g. Pivotal), again without changing the API to the actuation agent. The collection of Commands are part of the plug and play aspect of the Actuation subsystem 116.
Indeed, vendors of back-end applications 117 can be mixed and matched. There is no requirement that a set of interpretation result commands from the NLI 110 all be executed by the same back-end application. And some commands can be issued to a back-end application using an http API while others use an XML API.
In addition, the results of several commands can be combined by the actuation agent, to thereby implement complex actions on behalf of a user who might have thought the request to have been simple (what is easy to phrase in English might result in many commands to the back-end application). The composite nature of the Command elements allows them to be combined in novel ways, providing more flexible functionality than the API of a given back-end application.
An example command class hierarchy for an XML interface to the SalesForce.com back-end application 117 is now described. At the bottom of the hierarchy, commands are needed to Create, Edit, Report and Delete each of the objects in a sales opportunity management application: Accounts, Contacts, Events, Tasks and Opportunities. So 20 command classes are required. However, the XmlReportAccount command has much in common with the XmlReportContact command, motivating a generic XmlReport superclass. Similarly, the Create, Edit, Report and Delete commands all use the same XML API at salesforce.com, so they have some common functionality as well, that can be encapsulated in a generic XmlCommand superclass. And Create and Edit commands have some common functionality that can be encapsulated in an Update superclass. Accordingly, the following class hierarchy is defined. Each class is set out in a file in the accompanying CD-ROM appendix, and the name of the file is set forth below adjacent to the class name. Similar rationales can be used to define other possible class hierarchies for this or a different application API.













 sfaCommand
sfaCommand.java.txt


  XmlCommand
XmlCommand.java.txt


   XmlUpdateCommand
XmlUpdateCommand.java.txt


    XmlCreateCommand.java
XmlCreateCommand.java.txt


    XmlCreateAccountCommand.java
XmlCreateAccountCommand.java.txt


    XmlCreateContactCommand.java
XmlCreateContactCommand.java.txt


    XmlCreateEventCommand.java
XmlCreateEventCommand.java.lxt


    XmlCreateOpportunityCommand.java
XmlCreateOpportunityCommand.java.txt


  XmlCreateTaskCommand.java
XmlCreateTaskCommand.java.txt


 XmlEditCommand.java
XmlEditCommand.java.txt


  XmlEditAccountCommand.java
XmlEditAccountCommand.java.txt


  XmlEditContactCommand.java
XmlEditContactCommand.java.txt


  XmlEditEventCommand.java
XmlEditEventCommand.java.txt


  XmlEditOpportunityCommand.java
XmlEditOpportunityCommand.java.txt


  XmlEditTaskCommand.java
XmlEditTaskCommand.java.txt


XmlDeleteCommand.java
XmlDeleteCommand.java.txt


 XmlDeleteAccountCommand.java
XmlDeleteAccountCommand.java.txt


 XmlDeleteContactCommand.java
XmlDeleteContactCommand.java.txt


 XmlDeleteEventCommand.java
XmlDeleteEventCommand.java.txt


 XmlDeleteOpportunityCommand.java
XmlDeleteOpportunityCommand.java.txt


 XmlDeleteTaskCommand.java
XmlDeleteTaskCommand.java.txt


XmlReportCommand.java
XmlReportCommand.java.txt


 XmlReportAccountCommand.java
XmlReportAccountCommand.java.lxt


 XmlReportContactCommand.java
XmlReportContactCommand.java.txt


 XmlReportEventCommand.java
XmlReportEventCommand.java.txt


 XmlReportInformationCommand.java
XmlReportlnformationCommand.java.txt


 XmlReportOpportunityCommand.java
XmlReportOpportunityCommand.java.txt


XmlReportTaskCommand.java
XmlReportTaskCommand.java.txt









In operation, the actuationAgent uses the XML structure of the actuation string to determine which command to build. In this embodiment, fortuitously, there are some convenient coincidences which allow easy development of the command generator. For instance, one could combine the verb/noun XML tags together, prefix the result with “Xml”, tack on “Command” to the end, and one is left with a valid class name in the above list. Each command object's constructor takes the XML content from the noun node on, and parses as much of it as needed to properly execute the desired command. For other systems, the command objects' constructors might be more complex.
Each command object is expected to do whatever back-end application API calls are necessary for the task, which might mean multiple API calls. Depending on the application, one might need multiple API calls to validate information beforehand, to create or assign default values, and/or to perform calculations necessary for handling natural language inputs (such as converting “next Friday” into a Date object representing a particular date).
An advantage of the Command design pattern is the ability to implement “undo” in a clean fashion. While each Command object is responsible for encapsulating the appropriate API calls to perform its action, it is also responsible for encapsulating all the API calls necessary to undo its action. Undo is always handy, but it is especially useful in the wireless space, where every interaction is expensive due to the latency of the devices. Here the undo feature gives the user the safety net that allows confidence to move the application forward boldly. Delete, which is usually paired with an “Are you sure?” prompt, could cost users an extra 15 seconds on a wireless device. With undo, the extra prompt can be omitted, saving the user time, yet offering the same confidence and control over the application. Instead of always costing 15 seconds, this cost is now required only when the user wants to undo a previous action. In one embodiment, only a single undo is retained, and only delete and edit actions are undo-able.
Thus broadly speaking, when the actuation agent 116 receives an ActuationMessage containing an interpretation result command, the agent will call the process method of its adapter with the input, along with the user's login information:
adapter.process(actuation, getusername( ), getPassword( ));
The adapter gets a session for the user (which could be cached) and creates the appropriate command, via the CommandGenerator:
SfaCommandGenerator commandGenerator=getCommandGenerator(input, inputXML, sid);
Command command=commandGenerator.createCommand( );
Then the command is executed and the results of the command (plus some extra data corresponding to feedback about the command just executed) are represented as the results of actuation:
result=command.execute( );
result+=getExplanation(command);
For undo, the “result=” line would be replaced by
command Result=command.undo( )
When an interpretation result command produces more than one application command, are they divided up in the CommandExecute( ) function, although in a different embodiment, they could be divided up in the GenerateCommand( ) method or at a higher level.
The actuation agent 116 then encapsulates any such command result into an InteractionResultMessage and communicates it back to the I/O subsystem 114.
Examples of the following classes are each set out in a file in the accompanying CD-ROM appendix, and the name of the file is set forth below adjacent to the class name.

















CommandGenerator.java
CommandGenerator.java.txt




SfaCommandGenerator.java
SfaCommandGenerator.java.txt



(extends CommandGenerator)



XmlCommandGenerator.java
XmlCommandGenerator.java.txt



(extends SfaCommandGenerator)










As mentioned, the particular class hierarchy from which to instantiate command objects is part of the particular actuation agent that is called for in the Opal file for the system 100. In one convenient but not essential organization, each application domain has an actuation agent, and the actuation agent uses several adapters each specific to a respective vendor within the application domain. The command objects are referred to in the specific adaptor. For example, a sales force automation domain actuation agent might have separate (possibly overlapping) adapters specific to salesforce.com's or Pivotal's system, respectively. In addition, different adapters might be defined for different interfaces for the same vendor application. As an example, the “process” method called by the actuator in a sales force automation system might be included in an SfaAdapterInterface class such as the one set out in the file SfaAdapterlnterfacejava.txt in the accompanying CD-ROM appendix. SfaAdapterInterface is extended by SfaAdapter class, such as the one set out in the file SfaAdapterjava.txt in the accompanying CD-ROM appendix. SfaAdapter includes most required methods, but is further extended by an XmlAdapter class, such as the one set out in the file XmlAdapterjava.txt in the accompanying CD-ROM appendix.
It will be apparent from the above that a natural language interface can now be designed for a domain of back-end applications by the following steps:

    1. determine common set of functionalities of back-end applications of a common domain (including messages that originate from the application);
    2. define a generic command set and syntax for interpretation result messages, encompassing some or all of such functionalities, and a generic result set and syntax for results of such commands;
    3. create one or more bridges between the generic command set and generic result set, on the one hand, and the API of each target application on the other hand;
    4. create an interpretation subsystem that issues interpretation result commands toward whichever bridge or bridges are instantiated, using the generic command set and syntax; and
    5. create interaction subsystem that receives results from instantiated ones of the bridges using the generic result set and syntax.


As used herein, a given signal, event or value is “responsive” to a predecessor signal, event or value if the predecessor signal, event or value influenced the given signal, event or value. If there is an intervening processing element, step or time period, the given signal, event or value can still be “responsive” to the predecessor signal, event or value. If the intervening processing element or step combines more than one signal, event or value, the signal output of the processing element or step is considered “responsive” to each of the signal, event or value inputs. If the given signal, event or value is the same as the predecessor signal, event or value, this is merely a degenerate case in which the given signal, event or value is still considered to be “responsive” to the predecessor signal, event or value. “Dependency” of a given signal, event or value upon another signal, event or value is defined similarly.
As used herein, an item is “identified” if sufficient information is provided to distinguish the item from other items. For example, information identifies an item if the information can be used to access data relating to the item rather than to access data relating to other items.
The foregoing description of preferred embodiments of the present invention has been provided for the purposes of illustration and description. It is not intended to be exhaustive or to limit the invention to the precise forms disclosed. Obviously, many modifications and variations will be apparent to practitioners skilled in this art. In particular, and without limitation, any and all variations described, suggested or incorporated by reference in the Background section of this patent application are specifically incorporated by reference into the description herein of embodiments of the invention. The embodiments described herein were chosen and described in order to best explain the principles of the invention and its practical application, thereby enabling others skilled in the art to understand the invention for various embodiments and with various modifications as are suited to the particular use contemplated. It is intended that the scope of the invention be defined by the following claims and their equivalents.
APPENDIX A
Dejima Policy Reference
Platform 3.0
1 Introduction
Policies are written in a language called OPAL. This reference intends to describe the OPAL language. The current version of the platform can run in backward compatibility mode. In this mode some of the features described in this document would not be available. The comment “(not available in backward compatibility mode)” is written in front of each of these features. Set the system property USE_AMBIGUITY_XML to alse to run the platform in the backward compatibility mode. Example: java-DUSE_AMBIGUITY_XML=false IDE.
2 Policy
An AAOSA agent uses a set of policies to make claims on the input and to make actions based on those claims.
2.1 Overview of Claim
A claim is the part of the input that an agent policy recognizes as part of its domain. A claim may be built by putting together claims made by other policies or other agents. For example a HOME_ENTERTAINMENT policy may make claims based on claims made by a TV policy and a VCR policy. We would refer to TV and VCR claims as sub claims in the context of the HOME_ENTERTAINMENT claim.
2.2 Propagation of Claims
An AAOSA agent will use its policies to process the input when it receives an initiate interpretation, interpretation or reinterpretation message. Agents process the input in order from first policy declared in the agent to the last policy. Each policy makes all the claims it can on the input. Following policies can access these claims. After all policies have made their claims the agent selects the best claim. If the best claim is made from a non-grammatical condition (e.g. combo), then the sub-claims are also selected. The selected claims are handed up to up chain agents (i.e. using claim message).
2.3 Actuation of Claims
An Actuation is a standardized output. The actuation phase begins when the top AAOSA agent selects its best claim. The top agent will actuate the best claim using the action part of the policy that made that claim. The policy's action dictates whether the actuation of the claim should be delegated to other policies or agents. If the actuation is delegated to another agent a delegation message is sent to that agent. When an AAOSA agent receives a delegation message for actuating a claim, it will use the policy responsible for making that claim to actuate it. Each policy will either actuate the claim entirely or delegate parts of the actuation to other policies or agents responsible for making its sub claims.
2.4 Overview of Policy Components
A policy can have several components:
Name: Makes it possible to refer to the policy in OPAL language.
Title: Makes it possible to refer to the policy in the interactions and menus.
XML tags: Makes it possible to wrap the action in XML tags
Priority: Prioritizes the claim made by this policy.
Continuation: Sets the dependency of the claim to the claims made on previous input
Condition: Makes claims by processing the input A condition could be:
Terminal: Atomic operands of OPAL language.
References: Operands referring to claims made by other conditions.
Unary: A condition with only one operand.
Binary: A condition with two or more operand.
Action: Creates an action from a claim
3 Claim
A claim quantifies how well an input matches a policy condition. The information associated with a claim includes:
3.1 Owner
The name of the policy making the claim and the agent to which that policy belongs.
3.2 Focus
The parts of the input matched by the policy condition. Focus is represented using the character indexes of the input. (Characters in the input are indexed from 0). For example, focus
represents the word ‘book’ in the input ‘Sell book’. The characters of the input that correspond to the token separators (for example, white space) are never considered to be part of the focus. Therefore, a policy condition that matched ‘Sell’ and ‘book’ would have focus [0 . . . 3,5 . . . 8]. However, when there is no chance of confusion, for convenience this focus would be written as [0 . . . 8].
3.3 Priority
The precedence of the claim. (For details on assigning priorities to claims, see the Policy Components section.) Priorities are local to an agent. When an agent sends a claim to an up chain agent, the priority is reset to 0. The exception is priorities that are assigned for adding dialoging focus. These priorities are passed up-chain with the claim.
A vector is kept of all different priorities that were associated to sub-claims building up a claim. The focus size of the sub-claims with the same priority will be added up and would be kept in the vector.
EXAMPLE 1
The priority vector of claim on ‘aaa bb’ made by policy P3
P1: ‘aaa’ {priority:+1}
P2: ‘bb’ {priority: 0}
P3: P1 & P2
User-Input: ‘aaa bb’

















Priority
1
0



Focus size
3
2










The priority criterion of two claims will be compared in the following manner:

    
    
        1. The claim with highest priority will be chosen as the better claim
        2. If both claims have the same highest priority then the coverage percentage of that priority is compared. The coverage percentage is the focus sie of that priority divided by the total focus size of that claim. The claim that has the higher coverage percentage will be chosen as the better claim.
        3. If both claims have the same coverage on their highest priority then that vector will be compared again from step 1 disregarding the highest priority.

3.4 Ambiguity

    
    


A claim is said to be ambiguous if it contains two or more sub-claims with overlapping focuses. The ambiguity of the claim is equal to the number of ambiguous sub-claims that are found in the claim, including the claim itself.
3.5 Connection Weight
The total sum of connection weights assigned to operators making the claim. Each operator and operand has a predefined grammatical weight. The operators and operands in descending order of grammatical weight are:
1. Combo (,) and Recursive (+). Weight =8. (loose connection)

    
    
        2. Inexact matches. Weight=10000.
        3. And (&). Weight =100.
        4. Ordered (<). Weight =1.
        5. Adjacent (and all other operators and operands). Weight =0.

3.6 Loose Connections Count

    
    


The number of Combo (,) or Recursive (+) operators used to make a claim.
3.7 Unknown
Used to identify the number of items missing in the user input (deprecated). See Section 3-3-4 on <unknown if missing>condition.
3.8 Adjacency Score
Indicates the relative closeness of the claimed parts of the input. Adjacency score is calculated by adding up the number of tokens between claimed parts. If a claim has marked ‘Siamak’ and ‘Hodjat’ on the input “Siamak is a Hodjat”, its adjacency score is 2. A claim marking ‘Sia’ and ‘Hodjat’ on input “Siamak is a Hodjat”, will also have adjacency score of 2, because ‘Sia’ is part of the ‘Siamak’ token, but the same claim will have an adjacency threshold of 5 on the input “Siamak's family name is Hodj at” (Assuming the default USTokenizer is used). The following table shows the how adjacency score is calculated for different inputs claimed by the policy: ‘aaaaa’ & ‘bbbbb’.














Input
Adjacency score









aaaaa bbbbb
0



aaaaa bbbbb
0



aaaaas bbbbb
0



Aaaaa cc bbbbb
1



Aaaaa cc dd bbbbb
2



Aaaaa's bbbbb
2







Note that adjacency is handled slightly differently in the case of the combo operator. See the section on the combo operator for details.






3.9 Variable

Variables mark parts of the input that is not claimed by other conditions in a policy. Variables are adjustable and the part they mark may vary as the claim is manipulated (see variable condition).
4 Claim List
All claims made by policy conditions are added to an ordered list. The default criteria used to order the claims is as follows, listed in the order in which they are considered when comparing two claims. Each criterion is checked only if none of the criteria above it would apply. If two claims are equivalent with respect to all of the criteria, the claim that was made by the policy listed later in the Agent will win.
4.1 Coverage
A claim whose focus includes another claim's focus wins. If the focuses are equivalent, neither is considered to cover the other. For example, a claim made on ‘Being John Malkovich’ with focus [0 . . . 4] [6 . . . 9] [11 . . . 19] will win over a claim with focus [6 . . . 9] [11 . . . 19]. However, if two claims both had focus [0 . . . 4], the claims would be equivalent in terms of the coverage criterion.
4.2 Priority
A claim with higher priority wins. If the priorities are the same, the claim with the higher priority score will win. (See the Policy Components section on Priority for details on how priority and priority score are assigned.)
4.3 Ambiguity
A claim with more ambiguity wins. This criterion is checked only if the current claim can be merged with the claim it's compared to. (See Claim Manipulation in Appendix 3 for further information.) If two claims have equivalent ambiguity, the ambiguity due only to the current claim is compared. If this differs, the claim with the smaller local ambiguity will win (regardless of whether the two claims can be merged).
4.4 Connection Weight
A claim made by more grammatical operators and exactly matched operands wins (that is with smaller connection weight). If the connection weights of two claims are equal then the claim with fewer loose connections wins.
4.5 Adjacent
A claim with smaller adjacency score wins.
4.6 Focus size
A claim with a larger focus wins. For example, a claim made on ‘Sell Book and Buy Toy’ with focus [0 . . . 3] [5 . . . 8], will win over a claim with focus [9 . . . 11] [13 . . . 15]).
4.7 Match Restrictiveness
A claim using a more restrictive token matcher wins. The most restrictive token matcher is the exact token matcher, which has a restrictiveness of 100. The substring token matcher has a restrictiveness of 60.
4.8 Variable
When comparing two claims the claims with no variables is better than a claim with variables. If both claims have variables then the one with more variables but smaller ones is preferred to the one with less but wider variables (see variable condition).
4.9 Unknown (Deprecated)
A claim with unknown element(s) will win over a claim with no unknown elements. If both claims have unknown elements the one with less unknown elements wins.
4.10 Default Criterion:
A claim, which its focus is closer to the beginning of the input wins.
5 Policy Components
5.1 Name
Used to refer to the policy in the OPAL language. A policy name is a series of capital letters (‘A’ to ‘Z’), digits (‘0’ to ‘9’) or under score (‘_’) that begins with a capital letter. Policies names should be unique (in the scope of the agent they are declared in).
5.2 Title
Used to refer to the policy in interactions and menus. Spaces/symbols can be used in the title and it does not need to be in capital letters. The title is a literal and is automatically enclosed in quotes.
5.3 XML Tags (Not Available in Backward Compatibility Mode)
The interpretation network will support the generation of output that contains XML tags. This section will discuss the types of tags that will be used and how they are generated. It will use the AV Network as an example. See FIG. 3.
An XML tag is made up of three components
1. name: The name of the tag
2. parameter: The name of a tag parameter. Each tag may have 0 or more parameters.
3. value: The value of a tag parameter. Each parameter must have exactly one value.
A basic XML tag has the form:
<<name>[<parameter>=“<value>”]></<name>>
For example, a tag having two parameters would have the form:
<name parameter1=“value1” parameter2=“value2”></name>
The interpretation network supports two parameters, type and ID. These are discussed in more detail later. For now, just note that the general form of an XML tag generated by the network is:
<name type=“value1” ID=“value2” > </name>
The XML tags that are supported by the interpretation network can be divided into four groups (two of which consist of a single tag):
1. The interpretation tag
2. The ambiguity tag
3. The logical tags
4. Domain specific tags
The first three groups are common to all applications. The domain specific tags vary from application to application.
The tags are generated in two ways. The interpretation tag and the ambiguity tag are generated automatically by the interpretation network. The logical tags and domain specific tags are generated by setting the xml-tag and xml-type fields in the policy of a network agent.
As mentioned above, the system supports two parameters: type and ID. The type parameter is optional. For logical and domain specific tags, it can be specified by setting the xml-type field of a policy. The ID parameter is generated automatically by the system. Its value is equal to the ID of the claim that is being actuated by the policy.












XML Tags Generated Automatically by the System











Tag Name
Type (optional)
Data Values







interpretation
none
none



xor
relation
none











XML Logical Tags Common to All Applications











Tag Name
Type (optional)
Data Values







and
relation
none



or
relation
none



lessThan
relation
none



greaterThan
relation
none



ifThen
relation
none











AV Network Application Specific XML Tags











Tag Name
Type (optional)
Values







TV
function
none



VCR
function
none



Power
parameter
none



Channel
parameter
none



Volume
parameter
none



On
attribute
none



Off
attribute
none



Up
attribute
none



Down
attribute
none



Station
attribute
PBS, CBS, NBC, etc.










EXAMPLE 3
Simple Command















Input: ‘Turn TV on’.



XML: <interpretation>









<TV type=“function” ID=“1_”>









<power type=“parameter” ID=“2_”>









<on type=“attribute” ID=“3_”>



</on>









</power>









</TV>









</interpretation>










EXAMPLE 4
Simple Command: Attribute with Values















Input: ‘Turn TV to PBS’.



XML: <interpretation>









<TV type=“function” ID=“1_”>









<channel type=“parameter” ID=“2_”>









<station type=“attribute” ID=“3_”>









PBS









</station>









</channel>









</TV>









</interpretation>










EXAMPLE 5
Compound Attributes















Input: ‘Turn on TV to PBS’



XML: <interpretation>









<TV type=“function” ID=“1_”>









<and type=“relation” ID=“2_”>









<power type=“parameter” ID=“3_”>









<on type=“attribute” ID=“4_”>



</on>









</power>



<channel type=“parameter” ID=“5_”>









<station type=“attribute” ID=“6_”>









PBS









</station>









</channel>









</and>









</TV>









</interpretation>










EXAMPLE 6
Compound Commands















Input: ‘Turn VCR off and TV volume down.’



XML: <interpretation>









<and type=“relation” ID=“1_”>









<VCR type=“function” ID=“2_”>









<power type=“parameter” ID=“3_”>









<off type=“attribute” ID=“4_”>



</off>









</power>









</VCR>



<TV type=“function” ID=“5_”>









<volume type=“parameter” ID=“6_”>









<down type=“attribute” ID=“7_”>



</down>









</volume>









</TV>









</and>









</interpretation>










EXAMPLE 7
Ambiguity















Input: ‘Turn off.’



XML: <interpretation>









<xor type=“relation”>









<TV type=“function” ID=“1_”>









<power type=“parameter” ID=“2_”>









<on type=“attribute” ID=“3_”>



</on>









</power>









</TV>



<VCR type=“function” ID=“4_”>









<power type=“parameter” ID=“5_”>









<on type=“attribute” ID=“6_”>



</on>









</power>









</VCR>









</xor>









</interpretation>










5.3.1 Generating XML tags

Syntax: {XML tag: tag-name}
XML tag goes after the policy condition.















Policy::



(THEATERNAME:



  “



  ‘AMC’|‘century21



    {xml-tag: ‘theaterName’}



    {action:



      {execute:*}}



  ”) ,



Input: ‘amc’



Output: <theaterName ID=“1_”> amc </theaterName>










5.3.2 Generating XML type tags

Syntax: {XML tag: tag-name}
XML type tag goes after the XML tag. XML type is valid only if XML tag is provided for the policy.
EXAMPLE 9
Generating XML Type Tags












Policy::


(THEATERNAME:


  “


  ‘AMC’|′century21


    {xml-tag: ‘theaterName’}


    {xml-type: ‘attrib’}


    {action:


    {execute:* }}


  ”) ,


Input: ‘amc’


Output: <theaterName type=“attrib” ID=“1_”> amc </theaterName>









5.3.3 Ambiguity tags

Ambiguity tags are generated automatically.
EXAMPLE 10
Generating XML Type Tags















Policy::



(MOVIENAME:



  “



  ‘john malkovich’



  {xml-tag: ‘movieName’}



  {xml-type: ‘attrib’}



    {action:



      {execute:*}}



  ”)



(MOVIESTAR:



  “



  ‘being john mlkaovich’



    {xml-tag: ‘movieStar’}



    {xml-type: ‘attrib’}



    {action:



      {execute:*}}



  ”)



(MOVIE:



  “



  MOVIENAME,MOVIESTAR



    {xml-tag: ‘movie’}



      {action:



      {execute:*}}



  ”),



Input: ‘john malkovich’



Output:



<movie ID=“1_”><ambiguity>



<movieName ID=“2_”>john malkovich</movieName>



<movieStar ID=“3_”>john malkovich</movieStar>



</ambiguity></movie>










5.3.4 Backward compatibility

    
    
        Using the backward compatibility mode will stop the automatic generation of the ambiguity XML tags and would prevent the XML tag and XML type from showing in the tree mode of policy editor.

5.4 Priority

    
    


The priority assigned to a policy. A policy's priority is assigned to all claims made by that policy.
Of the criteria used to order the claim list, priority is the only one that can be set explicitly in the policy. Priority is the second criterion used when comparing two claims; coverage is compared first. If two claims are equal in terms of coverage, then the one with a higher priority will win. Therefore, priority provides the policy writer with a good amount of control over which policy should produce the winning claims.
The default priority is 0. Priority can explicitly be set to any integer between −1000 and +1000, inclusive. A positive integer will give the claims made by the policy higher priority; a negative integer will give them lower priority.
A list is kept of all different priorities that were associated to sub-claims building up a claim. The focus of the sub-claims with the same priority will be added up and would be kept in the list.
EXAMPLE 11
The Priority List of Claims
P1: ‘aaa’ p=1
P2: ‘bb’ p=2
P3: ‘cccc’ p=2
P4: (AB) & C
User-Input: “cccc aaa bb”.















Priority
2
1









Focus size
5 . . . 7
[0 . . 3][9 . . . 12]










The priority criterion of two claims will be compared in the following manner:

    
    
        1. The claim with highest priority will be chosen as the better claim.
        2. If both claims have the same highest priority then the coverage percentage of that priority is compared. The coverage percentage is the focus size of that priority divided by the total focus size of that claim. The claim that has the higher coverage percentage will be chosen as the better claim
        3. If both claims have the same coverage on their highest priority then that vector will be compared again from step 1 disregarding the highest priority.
    
    


Priority is used only for ordering the claim list of the agent containing the policy. When an agent passes a claim up chain, the priority of the claim is removed.
5.5 Continuation (Not Available in Backward Compatibility Mode)
The continuation field provides support for recognizing continuation in the interpretation network.
For example consider the following dialog:
01 User “Movies stang Tom Hanks”

    
    
        System: Lists all movies starring Tom Hanks
    
    


02 User: “And Meg Ryan”

    
    
        System Lists all movies starring Tom Hanks and Meg Ryan

The interpretation network should recognize that the input 02 depends on the input 01.

The “continuation field” is used to indicate if the interpretation of an input depends on the interpretation made on the previous input The continuation field has the following syntax (which goes right after the policy condition):

{continuation: join}

    
    


EXAMPLE 12
Current Input is a Continuation
P1:
/BOI ‘and’
/*
/BOI is a terminal condition that claims the beginning of the input

    
    
        {continuation: join}
        {action: {execute: ‘Recognized dependency’ }}
    
    


Whenever a policy is a join continuation then any claims made by that policy will be tagged as continued. Lets say the claim made by a policy with a {continuation: join} field would be part of the winning claim. If this is true then the input is going to be reinterpreted based on the claims made on the previous input. So in the above example the “And Meg Ryan” input will be claimed with dependency to the previous input (“Movies starring Tom Hanks”) and therefore “And Meg Ryan” will be reinterpreted again but this time the claims made on the previous input (“Movies starring Tom Hanks”) would be used.
5.6 Condition
Used to make claims from the input. Each condition will return a claim list of all claims it produces. There are four types of conditions: terminal, reference, unary and binary.
5.6.1 Terminal Condition
There are four types of terminal conditions:
Token Terminal: Predefined patterns retrieved from a string, a file or a database.
Number Termimal: Refers to claims made by other policies within the same agent
Symbol Terminal: Refers to claims made by other agents.
Type Terminal: Refers to a part of the input not claimed.
5.6.1.1 Token
Tokens are identified based on the following parameters:
1. Alphabet: Defines letters as the normal token type. Example: Characters ‘a’ to ‘z’ define the English alphabet
2. Number: Defines digits as the numeric token type.
3. Symbol: Defines special characters (i.e. not alpha or number) as the token type.
4. Delimiters: Defines what may separate tokens (e.g. white space, tabs, new line . . . ).
5. Tokenizer class: The class used to identify tokens based on the above parameters.
The above parameters are defined in properties/ParserParameters.xml. Whatever is not included as an alphabet, numeral or symbol is considered a delimiter by the tokenizer.
EXAMPLE 13
Defining Alphabet in ParserParameters.xml
<Policy:Alphabet>
<Policy:CharacterRange lowerLimit=“a” upperLimit=“z” />
<Policy:CharacterRange lowerLimit=“A” upperLimit=“z” />
<Policy:CharacterRange lowerLimit=“　” upperLimit=“￮”/>
</Policy:Alphabet>
EXAMPLE 14
Defining Numerals in ParserParameters.xml
<Policy:Numeric>
<Policy:CharacterRange lowerLimit=“0” upperLimit=“9”/>
</Policy:Numeric>
EXAMPLE 15
Defining Symbols in ParserParameters.xml
<Policy: Symbols>
<Policy:CharacterRange lowerLimit=“!” upperLimit=“/”/>
<Policy:CharacterRange lowerLimit=“:” upperLimit=“@”/>
<Policy:CharacterRange lowerLimit=“[” upperLimit=“{grave over ( )}”/>
<Policy:CharacterRange lowerLimit=“{” upperLimit=“{tilde over ( )}”/>
</Policy:Symbols>
Note that based on example 11, the symbol $ will be recognized as a symbol because it is in the range between “!” and “/”. But the symbol £ will not be recognized because it is not in any of the defined ranges. For it to be recognized as a symbol a new range would have to be added to the symbol definition (“£” is the Unicode for £).
EXAMPLE 16
Recognizing the £ Sign
<Policy:Symbols>
. . .
<Policy:CharacterRange lowerLimit=“£” upperLimit=“£ ”/>
</Policy:Symbols>
Other than the above-mentioned parameters, customized tokenizer classes may introduce application or language dependent types. The custom tokenizer class should replace USTextTokenizer in ParserParameters.xml.
A token is any stream of characters of the same type (e.g. alphabet, number, symbol) separated by delimiters or other tokens. Symbols are exception to this rule because symbols are broken up character by character.














Input
Tokens









‘John Smith’
‘john’




‘smith’



‘mp3’
‘mp’ (alphabet),




‘3’ (number)



‘we are: “world's greatest” company’
‘we’ (alphabet),




‘are’ (alphabet),




‘:’ (symbol),




‘′′’ (symbol),




‘world’ (alphabet),




‘′’ (symbol),




‘s’ (alphabet),




greatest (alphabet),




‘′′’ (symbol),




‘company’ (alphabet)










To see details on number tokens, see the section on number terminals.
5.6.1.2 Token Terminals
There are three types of token terminals:
Files (/F)
Databases(/DB)
5.6.1.2.1 Strings
The condition applies if part of the input matches the string. The string is broken into tokens. The tokens are implicitly joined using the adjacent operator. Each token is matched using the default <exact, substring>match. See Section 5.3.3 on how each token is matched using <exact>and <substring>; see Section 5.4.4 on how the ADJACENT operator works.
EXAMPLE 18
String Condition


    
    
        Condition: ‘John malkovich’
        User-Input: ‘a john malkovich movie’
        The condition will apply and will mark ‘John malkovich’ as claimed
        User-Input:‘a john who is a malkovich movie’
        The condition will not apply because ‘John’ and ‘malkovich’ are not adjacent in the input.
        User-Input: ‘a john malk movie’
        The condition will not apply because ‘malk’ does not match ‘malkovich’.
        User-Input: ‘a john malkovic movie’
        The condition will not apply because “malkovic” is a substring match of ‘malkovich’.
    
    


The ‘, “, and/are special characters that should be referred to using % SINGLE_QUOTE %, % DOUBLE_QUOTE % and % BACK_SLASH % sequences.
EXAMPLE 19
Using Special Characters in String Condition
Condition: ‘% DOUBLE_QUOTE%malkovich%DOUBLE_QUOTE%
User-Input: ‘a “malkovich” movie’
The condition will apply.
5.6.1.2.2 Files(/F)
The condition applies if part of input matches one of the lines in a text file. By default, each entry in the text file can make a claim even if all its tokens are not found in the input.
EXAMPLE 20
The IF Condition
MN.txt:
sixth sense
being john malkovich
the wall ---------------------------------------------------
Condition: /F ‘MN.txt’
User-Input: malkovich and sixth sense
The condition will apply and two claims will be made.
The claims (in order) would mark the following:
‘malkovich’
‘sixth sense’
A claim will be made on only those input tokens that are in order.
EXAMPLE 21
The /F Condition
MN.txt:
being john malkovich ---------------------------------------------------
Condition: /F ‘MN.txt’
User-Input: john malkovich
The condition will apply and will make the claim: ‘john malkovich’
EXAMPLE 22
The /F condition
MN.txt:

    
    
        being john malkovich
        ---------------------------------------------------
        Condition: /F ‘MN.txt’
        User-Input: malkovich john
        The condition will apply and will make the claim: ‘malkovich’ (The claim-on ‘john’ lost to the claim on ‘malkovich’).
    
    


The default behavior will change if token matchers (see Section 5.3.3, e.g. <exact>, <substring> . . . ) are applied to the /F condition. If token matchers are used then all tokens in the file entry must appear in the input adjacent and in order.
EXAMPLE 23
The /F condition
MN.txt:
sixth sense
---------------------------------------------------
Condition: <exact>/F ‘MN.txt’
User-Input: sense
The condition will not apply as ‘sixth’ does not appear in the input.
EXAMPLE 24
The /F condition
MN.txt:
sixth sense ---------------------------------------------------
Condition: <exact>/F ‘MN.txt’
User-Input: sense sixth
The condition will not apply as ‘sixth sense’ does not appear in the input in the same order as the file.
EXAMPLE 25
The /F condition
MN.txt:
sixth sense
---------------------------------------------------
Condition: <exact>‘MN.txt’
User-Input: sixth a sense
The condition will not apply as ‘sixth sense’ is not adjacent in the input.
5.6.1.2.3 Common Files
A common file may be provided for an agent network to modify the behavior of file conditions (/F). To prevent unwanted claims made by file conditions create a common file. The common file should contain a list of common words (e.g. the, as, is).
EXAMPLE 26
The Problem
Why we Need Common File


    
    
        Common file not provided:
        MN.txt:
        sixth sense
        being john malkovich
        the wall
        ---------------------------------------------------
        Condition: /F ‘MN.txt’
        User-Input: the movies in sunnyvale
        The condition will apply and one claim will be made on token ‘the’.
    
    


EXAMPLE 27
How Common File Will Help
Common file provided:
common.txt:
the
being
---------------------------------------------------
Condition: /F ‘MN.txt’
User-Input: the movies in sunnyvale
The condition will not apply.
The file condition will ignore the common words only if they are not adjacent to a non-common word.
EXAMPLE 28
Common Files
Common file provided:
Condition: /F ‘MN.txt’
User-Input: the wall
The condition will apply.
5.6.1.2.4 Databases (/DB)
The condition applies if part of input matches one of the columns in a database. A database spec file (in text format) should be provided for the /DB condition. This spec file should contain the following information:
1. JDBC driver
2. JDBC server location
3. Database name (or ODBC data source name)
4. Table name
5. Column name
6. Login
7. Password
EXAMPLE 29
Defining spec file for JDBC connection to Pointbase using /DB
MS.spec:
com.pointbase.jdbc.jdbcUniversalDriver
jdbc:pointbase://192.168.0.26:1000/
movies
star
star_name
public
public
---------------------------------------------------
Condition: /DB ‘MS.spec’
EXAMPLE 30
Defining Spec File for JDBC Connection to Microsoft Access using /DB
MS.spec:
sun.jdbc.odbc.JdbcodbcDriver
jdbc:odbc:
movies
star
star_name
---------------------------------------------------
Condition: /DB ‘MS.spec’
Known Issue:
The token matchers (e.g. <exact<, <substring>, . . . ) is not applicable to the /DB.
5.6.1.3 Number terminal (/NUMBER)
The condition applies if a number token is found in the input (numbers are identified by the tokenizer).
You may also define a range for the /NUMBER condition.
Numbers are tokenized using the following rule:
<DIGIT>+[(COMMA|PERIOD)<DIGIT>+]+
EXAMPLE 31
Valid Numbers
34.5
3,456
3,456.56
Known Issues:
The following are also considered to be valid numbers:
3.4.5
23,3434,43
3.4,5

    
    
        There is no way to explicitly declare the format of a number (i.e. integer, positive integer, negative integer, real, scientific notation . . . ).
        Ranges acceptable in /Number is limited to long integer values (±232).
        + and −signs are not claimed by operator /NUMBER

5.6.1.4 Symbol Terminal (/SYMBOL)

    
    


The condition claims a single symbol in the input (numbers are identified by the tokenizer, refer to Section 5.4.1.1).
EXAMPLE 32
Symbol Terminal
Condition: /SYMBOL
User-Input:‘@’
The condition will apply and the symbol @ will be claimed.
EXAMPLE 33
Symbol Terminal
Condition: /SYMBOL
User-Input:‘#@%’
The condition will apply and the symbol # will be claimed.
5.6.1.5 Delimiter Terminal (/DELIMITER)
(Not Available in Backward Compatibility Mode)
The condition claims a delimiter in the input (delimiters are identified by the tokenizer, refer to Section 5.4.1.1).
EXAMPLE 34
Delimiter Terminal
Condition: ‘blue’/DELIMITER ‘book’
User-Input: ‘blue book’
The condition will apply.
Claim: [‘blue’ ][‘ ’ ][‘book’ ].
Delimiters are most useful when used in conjunction with variables.
Note that Blanks are added to the beginning and the end of all text inputs automatically and therefore the beginning and the end of input is always a delimiter.
EXAMPLE 35
Delimiter Terminal at the Beginning of the Input
Condition: /DELIMITER ‘book’
User-Input: ‘book’
The condition will apply.
Claim: [‘ ’ ][‘book’ ].
5.6.1.6 Beginning of Input Terminal (/BOI)
(Not Available in Backward Compatibility Mode)
The condition claims the first character of the input that is always a blank space (blanks are added to the beginning and the end of all text inputs automatically).
EXAMPLE 36
BOI Terminal
Condition: /BOI ‘a’
User-Input: ‘a’
The condition will apply.
Claim: [‘ ’ ][‘a’ ].
User-Input: ‘b a’
The condition will not apply.
User-Input: ‘a b’;
The condition will apply.
Claim: [‘ ’ ][‘a’ ].
Important Notes:


    
    
        The purpose of using /BOI should be to detect continuation (normally if the input depends on the previous input). Policies that detect continuation should not be referred in the policies that make relation between other policies.
        It is recommended to always use <exists>operator along with the /BOI operators as normally the beginning delimiters themselves are not important
        This condition should only appear at the beginning of the policy condition (may follow the exists> condition)

5.6.1.7 End of Input Terminal (/EOI)

    
    


(Not Available in Backward Compatibility Mode)
The condition claims the last character of the input that is always a blank space (blanks are added to the beginning and the end of all text inputs automatically).
EXAMPLE 37
EOI Terminal
Condition: /EOI ‘a’
User-Input: ‘a’
The condition will apply.
Claim: [‘a’] [‘ ’].
User-Input: ‘a b’
The condition will not apply.
User-Input: ‘b a’
The condition will apply.
Claim: [‘a’] [‘ ’].
Important Notes:


    
    
        The purpose of using /EOI should be to detect continuation (normally if the input depends on the next input). Policies that detect continuation should not be referred in the policies that make relation between other policies.
        It is recommended to always use <exists> operator along with the /EOI operators as normally the ending delimiters themselves are not important
        This condition should only appear as the last operator in policy condition. 5.6.1.8 Token Type Terminal (/TOKEN)
        The condition applies if a user defined token type is found in the input. New token types could be defined in the tokenizer.

5.6.1.9 Check condition (<check (agent, property, key; value)>):

    
    


Makes a claim only if an agent's property is equal to a specific value. This condition is especially useful for the AAOSA agents to know about the state of the application or device. For example the TV agent in a TV/AV interface could use the <check> condition to know if the TV is on. If the TV is on and the user says ‘power on’ then the TV agent could generate the proper response. There are two types of properties that may be accessed through messaging:
a. Data property (basic objects)
b. Data store property (collection objects: hash tables, vectors, . . . )
Each agent must allow access to its data property by declaring it as a valid data property. The methods that should be sub-classed for this purpose are summarized in the following table:















Access permission
methods
Set message



















Data
public Boolean
public void
<check (agent, property; value)>


Property
IsValidDataProperty (
setData (



String propertyName)
String property Name,




Object value)




Object




getData (




String propertyName)


Data
public Boolean
public void
<check (agent, property, key;


Store
IsValidDataStoreProperty (
setDataStoreElement (
value)>


Property
String propertyName)
String propertyName,




String propertyKey,




Object




propertyValue)




public Object




getDataStoreElement (




String propertyName,




String propertyKey)









All AAOSA agents have a STATE property that could be checked (STATE is a basic property).
The parameters of a check message, <check (agent, property, key; value)>, are:
Agent: The agent address that its property is to be checked. This parameter is optional. If omitted then an agent will “check” its own property.
Property: The name of the property to be checked
Key: The key to the data store properly (as a string). This parameter is optional. If omitted then an agent will “check” a basic property.
Value: The value that the property will be compared to (as a string).
EXAMPLE 38
Check Condition


    
    
        P1:
        Condition: ‘open’ {action: {execute: ‘door
        openned’,set(DOOR,‘STATE’,‘ ’,‘OPEN’)}}),
        P2:
        Condition: <check(DOOR, ‘STATEI,’; ‘OPEN’)>‘open’ {action: {execute: ‘door is already open!’}}
        User-Input: ‘open’
        If the state of the door is open then policy P2 will apply and when actuated it would reply with:‘door is already open!’
    
    


See section 6.2.6 and 6.2.7 for more information on set and get.
5.6.2 Reference Conditions
5.6.2.1 Policy Reference
The condition applies if another policy has claims. A policy name is a series of capital letters (‘A’ to ‘Z’), digits (‘0’ to ‘9’) or under score (‘_’) that begins with a capital letter. A policy can only refer to policies that are declared before it.
5.6.2.2 Agent Reference
The condition applies if the referenced agent has claims. The referenced agent must be a listener to the agent that owns the policy.
An agent is referred to using its address. An agent address has the following format: agent-instance-name . agent-class @ domain
An agent could be referred to only if it's agent-instant-name would be a series of capital letters (‘A’ to ‘Z’), digits (‘0’ to ‘9’) or under score (‘_’) that begins with a capital letter.
EXAMPLE 39
Agent Reference


    
    
        DOWN.com.MySubClassedAAOSA@x
        All agents in the same domain may be referred to using only the agent-instance-name. By default all agents are made in domain x.

5.6.2.3 Variables (?)

    
    


Variables behave and generate claims according to the following rules:

    
    
        If the variable condition is bound by tokens on both sides of the condition, then all the tokens found between the boundary tokens are considered to be part of the variable and are added to the policy's claim. This type of variable condition is also known as an un-adjustable variable condition.
    
    


EXAMPLE 40
Variable Condition Bound from Both Sides


    
    
        P1: ‘in’ ? ‘folder’
        Input: in my java folder
        The variable condition in the claims made by P1 is bound from left by the ‘in’ token and from right by the ‘folder’ token. Therefore, “my java” is identified as the variable part of the input and is added to the claim's focus.
        If the varable condition is bound only on either the left or right side of the condition, then the leftmost/rightmost token of the variable part of the input is added to the policy's claim. The variable part of the input is also referred as a left/right “adjustable” variable
    
    


EXAMPLE 41
Variable Condition Bound from One Side


    
    
        P1: ‘in’ ?
        Input: in Java folder
        
            In this case the variable is bound on the left by the ‘in’ token and the variable part is identified as “right adjustable”. Per our rule, the “java folder” string is marked as the variable part of the input and “java” is added to the claim focus.
        
        
        If a claim with aright (or left) adjustable variable is merged with another claim that does not have a variable part then the variable part of the claim is adjusted by excluding all the tokens that are found from the beginning of the next claim focus.
    
    


EXAMPLE 42
Merging Variable Claims with Claims with No Variable Part


    
    
        P1: ‘a’ ?
        P2: ‘b’
        P3: P1<P2
        Input: a x y z b r
        In this case, P1's subclaim variable part consists of the string “x y z” and its focus consists of “a” and “x” (second rule applies). When merged, P3's focus consists of ‘a’, ‘x’ and ‘b’.
        Claim list processing is performed as described in the policy reference. The variable length will be checked only after the appropriate checks for coverage, priority, ambiguity, connection weight, adjacency, focus size and restrictiveness are done.
        If the variable condition does not mark any of the input no claims are made.
    
    


EXAMPLE 43
No claims are Made When the Input Contains No Variable Parts


    
    
        P1: ‘in’ ? ‘folder’
        Input: in folder
        Or
        P1: ‘in’ ?
        Input: folders that are in
        In both of these examples the variable condition does not mark any of the input and no claims are generated.
        When comparing two claims, any claims with no variables are selected over claims with variables
    
    


EXAMPLE 44
Claims with No Variables Win Over Claims with Variables


    
    
        P1: ‘a’ ?
        P2: ‘a’ ‘b’
        Input: a b
        In this case, P2 will win over P1.
        If two claims contain variables, then the claim with the greater number of variables wins.
    
    


EXAMPLE 45
Claims with Greater Number of Variables Win


    
    
        P1: ‘a’ ? ‘b’ ? ‘c’
        P2: ‘a’ ? ‘c’
        Input: a x by c
        In this example, P1 will be selected over P2.
        If two claims have the same number of variables, then the claim with the smaller variable length wins
    
    


EXAMPLE 46
Claims with Smaller Variable Length Wins


    
    
        P1: ‘a’ ? ‘b’
        P2: ‘c’ ? ‘d’
        Input: a x b c j k d
        In this example, P1 is selected as it has the smallest variable length.
        Variable conditions may have a name assigned to them. A named variable condition consists of the variable operator followed by a semicolon followed by the variable name. For example, [?:First] is a valid variable name. References to variable names are only valid within the actions of the policy where the variables are declared.
    
    


EXAMPLE 47
Named Variables


    
    
        P1: ‘a’ ?:A
        
            {action: {execute: ?:A}}
        
        
        P2: P1 ‘b’ ?:A
        
            {action: {execute: ?:A}}
        
        
        Input: a x b y
        In this example, P2 will be selected as the best claim and the result of the actuation consists of ‘y’
        The use of cascading variables is illegal and its behavior is undefined and not supported
    
    


EXAMPLE 48
Cascading Variables


    
    
        P1: ‘in’ ???
        The use of unbound variable conditions is illegal and its behavior is undefined and not supported
    
    


EXAMPLE 49
Unbound variables


    
    
        P1: ?

5.6.3 Unary conditions:

    
    


Unary condition is an operation on one operand. There are six basic unary conditions:
Optional
Exists
Token matching
Unknown
Recursive
Tag
5.6.3.1 Optional condition ([operand]):
This condition will always make a claim. The claim made depends on the operand:
If the operand has a claim, then the optional condition will make the same claims as the operand.
If the operand has no claims the optional condition will claim null, an empty claim with a focus of zero.
EXAMPLE 50
Optional Condition
Condition: [‘actor’ ] ‘John malkovich’
User-Input: actor john malkovich
The condition will apply and will make the following claims:
‘actor john malkovich’
‘john malkovich’
User-Input: john malkovich
The condition will apply and will make one claim:
‘john malkovich’
Policies should not be written in a way that they could make a null claim. For example, the following policies are not supported (although the SDK would not prevent them from being written):
EXAMPLE 51
Policy Conditions that Should be Avoided
P1: [‘a’ ]
P2: [‘a’ ] | [‘b’]
P3: [‘a’ & ‘b’ ]
Each of the above policies makes a null claim regardless of the input. An example of the bizarre behavior that would result is:
EXAMPLE 52
Avoiding Null Claims


    
    
        P1: [‘a’ ]
        P2: P1 & ‘b’
        User-Input: b
        P2 will make a claim on ‘b’ with no reference to the null claim made by P1 and with connection weight 0 (the & operator will be ignored when calculating the connection weight).

5.6.3.2 Exists Condition (<exists> operand):

    
    


Checks to see if the operand has claims. If this is true, then the <exists>condition will make a null claim (an empty claim). If this is false, then the <exists>condition will not make a claim.
EXAMPLE 53
Exists Condition


    
    
        P1:
        Condition: ‘starring’<exists>‘siamak’
        User-Input: starring siamak
        The condition will apply, and will claim ‘starring’.
        P1 condition will make the claim only if the word starring, appears in the input adjacent to claim(s) made by the token terminal ‘siamak’.
        User-Input: starring
        The condition will not apply.
    
    


If binary conditions such as adjacent, and (&), ordered (<) and combo (,) appear in the same policy as the <exists> condition, then they behave as if <exists> condition is making claims on the input. For example:
EXAMPLE 54
Exists Condition


    
    
        P1:
        Condition: ‘aaa’ <exists> ‘aaa’
        User-Input: aaa aaa
        The condition will apply and marks the first ‘aaa’ ([0 . . . 2]) as its claim.
        User-Input: aaa
        The condition will not apply, as there is no ‘aaa’ appears in the input before another ‘aaa’.
    
    


Note that the scope of <exists>condition is only the policy in which it appears.
EXAMPLE 55
Exists Condition


    
    
        P1:
        Condition: ‘aaa’<exists>‘aaa’
        P2:
        Condition: P1 ‘aaa’
        User-Input: aaa aaa
        The P2 condition will apply and will mark the first and second ‘aaa’ ([0 . . . 2] [4 . . . 6]) as its claim.
    
    


Policies should not be written in a way that they could claim null. That is the following policies are not supported (although the SDK would not prevent them from being written):
EXAMPLE 56
Policies that Should be Avoided
P1: <exists>‘a’
P2: [‘a’ ] & <exists>‘b’
P3: <exists>(‘a’ & ‘b’)
Each of the above policies makes a null claim regardless of the input. An example of the bizarre behavior that would result is:
EXAMPLE 57
Policy Conditions that should be Avoided


    
    
        P1: <exists>‘a’
        P2: P1 & ‘b’
        User-Input: b
        P2 will make a claim on ‘b’ with no reference to the null claim made by P1 and with connection weight 0 (the & operator will be ignored when calculating the connection weight).
    
    


When using <exists>, an agent is checking against a part of the input that it is not going to claim. Therefore the agent must be aware of parts of the input that are possibly another agent's responsibility. This is against the agent oriented programming style where an agent should only care about its own domain and should not worry about other domains. Therefore <exists>should always be avoided if possible. There is usually a better way to write the policies or configure the agent network to avoid the usage of <exists>.
5.6.3.3 Token Matching Condition
This unary operator defines the type of token matching applied to token terminals and file terminals. There are 6 types of token matching:
<exact>
<accent>
accent-substring>
<accent-edit-difference>
For details on using these condition with combination to file terminal (/F) please refer to the section on file terminals.
5.6.3.3.1 <exact>
An input token matches a terminal token only if they are the same length and all their characters are exactly the same.
EXAMPLE 58
<exact>condition
P1:
Condition: <exact> ‘malkovich’
User-Input: ‘malkovic’
Claim: malkovic
The condition will not apply.
5.6.3.3.2 substring>
Two tokens match only if there is an exact match or:

    
    
        a) Both tokens are larger than 3 charas (this number can be changed in ParserParameters.xml).
        b) The beginning characters must have an 80% match, starting with the first characters. This percentage can be changed in ParserParameters.xml
    
    


If the size of input token getting matched is less than the size of pattern, the input token will be claimed otherwise only the matched part of the input token will get claimed.
EXAMPLE 59
Substring Matching


    
    
        P1:
        Condition: <substring> ‘malkovich’
        User-Input: ‘malkovic’
        The condition will apply (note that the length of user input is less than the pattern's)
        Claim: malkovic
        P1:
        Condition: <substring> ‘malkovich’
        User-Input: ‘malkovichab’
        The condition will apply (note that the length of user input is greater than the pattern's).
        Claim: malkovich
        P1:
        Condition: <substring> ‘malkovich’
        User-Input: ‘malkovi’
        The condition will not apply because it does not satisfy the 80% match.
        Claim: N/A
        P1:
        Condition: <substring> ‘malkovich’
        User-Input: ‘alkovich’
        The condition will not apply because the starting characters do not match.
        Claim: N/A

5.6.3.3.3 <accent>

    
    


Tokens are matched using a rule-based collator.
5.6.3.3.4 <accent-substring>
Similar to a substring matcher but <accent-substring>matches the characters using a collator.
5.6.3.3.5 <accent-edit-difference>
Matches two tokens using a rule based collator. The match allows missing or extra characters (less than a certain threshold currently defined in ParserParameters.xml). Tokens are matched only if they are larger than a certain length (currently defined in ParserParameter.xml).
5.6.3.3.6 Using Multiple Matchers
More than one token matching might be used to match terminal tokens. The matching will then be applied to the tokens one by one. If the first matching does not make a claim, the next matching in the list will be used.
EXAMPLE 60
Using Multiple Matches


    
    
        P1
        Condition: <exact , substring> ‘malkovich’
        User-Input: ‘being john malkovic’
        The condition will apply, and will mark the following:
        ‘malkovic’ (substring match)
    
    


The default matching used are: <exact, substring>.
EXAMPLE 61
Default Matches (<exact, substring>)


    
    
        P1
        Condition: ‘malkovich’
        User-Input: ‘john malkovic’
        The condition will apply, and will mark the following:
        ‘malkovic’ (substring match)
    
    


Token matching types could be also applied to file terminals (changing their default matchings)
EXAMPLE 62
Matching Types Applied to File Terminals


    
    
        MN.txt:
        sixth sense
        being john malkovich
        the wall
        ---------------------------------------------------
        Condition: <exact, substring> /F ‘MN.txt’
        User-Input: malkovich and sixth sense
        The condition will apply and only one claim will be made:
        ‘sixth sense’
    
    


Each token matching type has a value, which shows how restrictive it is. The restrictive-ness values is a constant, predefined heuristically:
Exact (100)
Substring (60)
Accent (50)
Accent-Substring (85)
Accent-Edit-Difference (50)
5.6.3.3.7 Fast Matchers (<exact>, <substring>)
The matchers are categorized into fast and flexible matchers. The fast matchers are: <exact> and <substring>.
5.6.3.3.8 Flexible Matchers(<exact>, <substring>,accent>, <accent-substring>, <accent-edit-difference>)
The flexible matchers include all fast matchers and the <accent>, <accent-substring>and <accent-edit-difference> matchers. These matchers are not included into the development platform by default unless the parserParameter.xml would be changed to include them by replacing the following line:
<Policy:TokenMatchers value=“fast”>
With:
<Policy:TokenMatchers value=“flexible”>
Note: It is not recommended to use the flexible matchers as they have a very poor performance.
5.6.3.4 Unknown Condition (<unknown if missing>operand)—deprecated:
Checks if the operand has claims. If this is true it will make the claim(s). If this is false, it will change the state of the claim to unknown (i.e. the operand is unknown). If a claim with unknown is actuated the system can prompt the user for clarification. How this prompting works and the attributes of the policies involved are discussed in section 4 (Actions).
EXAMPLE 63
Unknown Condition


    
    
        P1:
        Condition: ‘starring’ <unknown if missing> MOVIESTAR
        User-Input: starring siamak
        The condition will apply. P1 will claim ‘starring’, and change the state of the claim to unknown.
    
    


EXAMPLE 64
Unknown Condition


    
    
        P1:
        Condition: ‘starring’ <unknown if missing> MOVIESTAR
        P2:
        Condition: ‘starring’ <unknown if missing> <exists>MOVIESTAR
        User-Input: starring bruce willis
        P1 and P2 will both apply without changing the state of the claim.
        P1 will claim the whole input but P2 will only claim ‘starring’.
    
    


The <unknown if missing>operator is deprecated and it is recommended to use other approaches for dialoging (refer to section 6 for more information).
5.6.3.5 Recursive condition (operand+):
The recursive condition makes new claims by merging subsets of the claims made by the operand. Specifically, each claim made by the recursive condition is the result of merging claims that form a maximal disjoint subset of the set of claims made by the operand. Two claims are said to be disjoint if their focuses do not overlap (that is, the intersection of their focuses is null). A set of claims is disjoint if every claim in the set is disjoint to every other claim in the set. A set is maximal if it is not a subset of any other set.
EXAMPLE 65
+ condition


    
    
        MN.txt:
        sixth sense
        being john malkovich
        ---------------------------------------------------
        Policy without recursive condition:
        Condition: (/F ‘MN.txt’)
        User-Input: ‘being john malkovich and ‘sixth sense’
        The condition will apply and will make two claims.
        The claims (in order) will mark the following:
        
            1. ‘being john malkovich’
            2. ‘sixth sense’
        
        
        Policy with recursive condition:
        P1:
        Condition: (/F ‘MN.txt’)+
        User-Input: ‘being john malkovich’, and ‘sixth sense’
        The operand makes the two claims above. The disjoint subsets of the operand claims are {1} (the set containing claim 1), {2} (the set containing claim 2), and {1, 2} (the set containing claims 1 and 2). The sets {1} and {2} are not maximal because they are subsets of {1, 2}. Therefore, the recursive condition will make a claim by merging claims 1 and 2. The claim will mark the following:
        
            1. ‘being john malkovich’, ‘sixth sense’
        
        
    
    


EXAMPLE 66
+ condition


    
    
        MN.txt:
        fifth sense
        sixth sense
        being john malkovich
        ---------------------------------------------------
        P1: (/F ‘MN.txt’)+
        User-Input =‘being john malkovich and sixth sense’
        The condition will apply and will make two claims. The claims (in order) will mark the following:
        
            1. ‘being john malkovich’, ‘sixth sense’
            2. ‘being john malkovich’, ‘sense’
        
        
        Note that a claim is not made by combining the operands's claims on ‘sixth sense’ and ‘sense’ because the two claims are not disjoint. Another way to look at this is that merging these two claims would make an ambiguity. The recursive condition is not allowed to make new claims that have ambiguities.
    
    


A policy condition should not reference a recursive condition.
EXAMPLE 67
Policies Containing Recursive Conditions Should not in General be referenced by other policies
The Recursive Condition does not Generate all Possible Inner-Products


    
    
        AB: ‘a b’
        C: ‘c’
        CD:‘c d’
        P1: (AB | C)+
        RECURSIVE: (P1 |CD)+
        
            0 2 4 6
        
        
        input: a b c d
        claims made by P1 recursive operand:
        1. AB:ab [0.2]
        2. C: c [4]
        3. CD: c d [4.6]]
        maximal disjoint subsets for AB and C:
        {1,2}
        claims made by P1:
        1. (AB, C): a b c [0.2.4]—best claim made by P1 Note that P1 does not propagage claims made by AB and C individually because the claims are each individually subsets of the maximal disjoint subset (1,2).
        claims made by RECURSIVE:
        1. (AB, C): a b c [0.2.4]
        2. CD: c d [2.4]—best claim made by RECURSIVE
    
    


EXAMPLE 68
An Example of Referencing Recursive Conditions in a Policy that Might go Unnoticed


    
    
        P1: ‘a’ ‘b’+
        Here the recursive condition is actually being referenced by the adjacent condition.
    
    


Be careful of the common mistake illustrated in the following example, in which A agent is downchain of Top agent:
EXAMPLE 69
Common Mistake in + Operand Usage


    
    
        A agent's policies:
        P1: ‘a’
        TOP agent's policies:
        P1: A+
        User-input =‘a a a’
        The condition in TOP agent will apply and will only claim the first ‘a’ in the input (default criterion)
    
    


It might be expected that the TOP agent in the above example should claim the whole input ([0 . . . 0 ] [2 . . . 2] [4 . . . 4]). The reason it does not is that Agent A passes only its best claim up to the TOP agent. In other words, the recursive condition (like all other conditions) does not affect how a down chain agent makes claims on the input. If multiple claims on character ‘a’ are required, then Agent A should have a recursive condition of its own:
EXAMPLE 70
Correct usage of + Operands


    
    
        A agent's policies:
        P1: ‘a’
        P2: P1+
        TOP agent's policies:
        P1: A+
        User-input ‘a a a’
        The condition in TOP agent will apply and will claim all ‘a’s in the input.
    
    


In the latter example it is not necessary to have the recursive condition in the TOP agent. However, a good practice is to use a “catch all” policy, which applies the recursive condition to all of an agent's policies and down chain agents.
When two claims are merged using the recursive condition (and similarly the combo condition) then the resulting claim will have a loose connection. Claims with loose connections will fail to merge to other claims using the grammatical operators (such as &,<and adjacent).
EXAMPLE 71
Incorrect Usage of + Operands
P1: ‘a’+
P2: P1 & ‘c’
Input: a a c
P2 will not apply.
Performance Note: The performance of the recursive condition depends on the input and the claims made by the operand. The recursive condition has a built in time-out to limit the amount of time that will be spent in the condition. This time-out is currently set to twenty seconds. When the maximum allowed time has elapsed, the condition returns the set of claims generated up to that point. Each claim will indicate that it was made by a timed-out condition. This set will not necessarily include the best possible result. The algorithm starts by making claims that involve the best operand claim. The assumption is that these will most likely create the best overall claims.
EXAMPLE 72
Timing Out in Recursive Condition


    
    
        P1: ‘a’
        P2: P1+
        User-input =‘a a a . . . a’
        The recursive condition can handle about 180 ‘a’s in 20 seconds. If the number of ‘a’s goes above this, the condition will time out and return the results generated so far. The message ‘had enough in recursive condition’ will be logged in the err.txt file, and the resulting claim will be tagged as timed out.
    
    


Recommended usage of the recursive operator is only in the catch policy (described later in the reference).
5.6.3.6 Repeat Condition (A)
The repeat condition is used to repeat the application of the AND (&), ORDERED (<) or adjacent operators. The following table illustrates the behavior of each repeat operator:














Repeat operator
What it means









A&{circumflex over ( )}B
A&B&B&B&B . . .



A<{circumflex over ( )}B
A<B<B<B<B<B . . .



A{circumflex over ( )}B
A B B B B B . . .










EXAMPLE 73
^ Operand
P1: ‘book’ | ‘toy’ | ‘shirt’
P2: P1 ‘and’ P1
User-Input: ‘book and toy and shirt’
Condition P2 will apply and will make two claims:
1. ‘book and toy’
2. ‘toy and shirt’.
Now consider replacing P2's condition:
P2: P1 ^ (‘and’P1)
Condition P2 will apply and will make three claims:
1. ‘book and toy and shirt’
2. ‘book and toy’
3. ‘toy and shirt’
EXAMPLE 74
&^ Operand
P1:‘a’ &^ ‘b’
User-Input:‘b b a b b x b’
Condition P1 will apply and will make the claim ‘b b a b b b’.
EXAMPLE 75
<^Operand
P1: ‘a’ <^‘b’
User-Input: ‘b b a b b x b’
Condition P1 will apply and will make the claim ‘a b b b’.
EXAMPLE 76
^ Operand
P1: ‘a’ ^‘b’
User-Input: ‘b b a b b x b’
Condition P1 will apply and will make the claim ‘a b b’.
Note that repeat condition cannot be applied to OR and COMBO conditions conceptually.
5.6.4 Tag Condition (<tag: >operand):
Tags claim(s) made by an operand. The tags may be accessed later by the check tag condition. Tag is used to classify claims. Classifying claims allows a policy to know if the claim belongs to it or not.
EXAMPLE 77
Tag Condition
MN.txt:
being john malkovich
---------------------------------------------------
P1:
Condition: <tag: ‘movie-name’ >(/F ‘MN.txt’)+
User-Input: ‘john malkovich’
The condition will apply making one claim.
The claims (in order) would mark the following:
‘being john malkovich’ (tag=‘movie-name’)
Using tags and check tags means that an agent has to be aware of policies of another agent (which could be a policy of an agent which is not an immediate down chain). This is against the agent oriented programming style where an agent should not care about the policies of another agent and should not be aware of any agent other than its immediate down chains. Therefore tags and check tags should be avoided if possible. There is usually a better way to write the policies or configure the agent network to avoid the usage of tags.
5.6.4.1 Check Tag Condition (<check tag: >operand):
Filters out all claims made by an operand that does not have a specific tag.
EXAMPLE 78
Check Tag Condition
MN.txt:
being john malkovich
MS.txt:
john malkovich
MOVIE agent policies:
P1:
Condition: <tag: ‘movie-name’ > (/F ‘MN.txt’)+
P2:
Condition: <tag: ‘movie-star’ > (/F ‘MS.txt’)+
CINEMA agent policies:
P3:
Condition: <check tag: ‘movie-star’ >MOVIE
P4:
Condition: <check tag: “movie-name”>MOVIE
User-Input: ‘john malkovich’
The condition will apply, and CINEMA will make two claims.
The claims would mark the following:
‘john malkovich’ belongs to P4
‘John malkovich’ belongs to P3
Now that the claim is tagged the CINEMA agent knows which policy in MOVIE agent made the claims.
Using tags and check tags means that an agent has to be aware of policies of another agent (which could be a policy of an agent which is not an immediate down chain). This is against the agent oriented programming style where an agent should not care about the policies of another agent and should not be aware of any agent other than its immediate down chains. Therefore tags and check tags should be avoided if possible. There is usually a better way to write the policies or configure the agent network to avoid the usage of tags.
5.6.5 Binary conditions
Binary condition merges claims made by two operands at the left and the right of a binary operator.
5.6.5.1 OR Condition (operand1 operand2):
Will apply only if at least one of the left or right operands has claims.
EXAMPLE 79
| condition


    
    
        P1:
        Condition: ‘Willis’ | ‘malkovich’
        User-Input: ‘willis’
        The condition will apply, and will claim willis as the input.
        User-Input: ‘willis or malkovich’
        The condition will apply, and will make two claims.
        The claims (in order) would mark the following:
        ‘malkovich’
        ‘Willis’

5.6.5.2 AND Condition (operand1 & operand2):

Will apply only if both left and right have claims.

    
    


EXAMPLE 80
AND condition


    
    
        P1:
        Condition: ‘john’ & ‘malkovich’
        User-Input: ‘malkovich whose first name is john’
        The condition will apply, and will mark the following: ‘malkovich’ ‘john’.
        User-Input: ‘john’
        The condition will not apply.
    
    


The AND condition will apply only to those claims that have no loose connections.
EXAMPLE 81
AND Condition on claims with Loose Connections


    
    
        P1:
        Condition: ‘send’, ‘receive’
        P2:
        Condition: P1 & ‘email’
        User-Input: send and receive email’
        Condition P2 will apply, and will claim the following:
        ‘receive email’
        ‘send email’.

5.6.5.3 ORDERED Condition (operand1 <operand2):

    
    


Will apply only if both left and right operands have claims and the part of the input claimed by the left operand should come before the part of input claimed by right operand.
EXAMPLE 82
ORDERED Condition


    
    
        P1:
        Condition: ‘john’ < ‘malkovich’
        User-Input: ‘john whose family name malkovich’
        The condition will apply, and will claim ‘john’ and ‘malkovich’.
        User-Input: ‘malkovich whose first name is john’
        The condition will not apply.
    
    


The ORDERED condition will apply only to those claims that have no loose connections.
5.6.5.4 ADJACENT condition (operand1 operand2):
Will apply only if both left and right operands have claims, and the left operand claim appears just before (adjacent to) the right operands claim.
The claims made by left and right operands are merged, the resulting claim will be accepted only if the adjacency score of the merged claim is 0.
EXAMPLE 83
ADJACENT Condition


    
    
        P1:
        Condition: ‘john’ ‘malkovich’
        User-Input: ‘John malkovich’
        The condition will apply, and will claim the whole input.
        User-Input: ‘john smith malkovich’
        The condition will not apply. As the adjacency score of the merged claim is 1.
    
    


The ADJACENT condition will apply only to those claims that have no loose connections.
5.6.5.5 COMBO condition (operand1, operand2):
Will try to join the left and right operands to make all combinations of ambiguous or non-ambiguous claims.
EXAMPLE 84
COMBO Condition


    
    
        P1:
        Condition: <exact> ‘johhn malkovich’ | <exact> ‘bruce willis’
        P2:
        Condition: <exact> ‘being johhn malkovich’ | <exact> ‘sixth sense’
        P3:
        Condition: P1, P2
        User-Input: ‘bruce willis and john malkovich’
        P3 will make three claims.
        The claims (in order) are:
        ‘john malkovich’ (ambiguous, belongs to P1 or P2), bruce willis' (belongs to P1)
        ‘john malkovich’ (non-ambiguous, belongs to P1), ‘bruce willis’ (belongs to P1)
        ‘john malkovich’ (non-ambiguous, belongs to P2), ‘bruce willis’ (belongs to P1)
        ‘John malkovich’ (ambiguous, belongs to P1 or P2)
        ‘john malkovich’ (non-ambiguous, belongs to P1)
        ‘John malkovich’ (non-ambiguous, belongs to P2)
        ‘bruce willis’ (belongs to P1)

Note: This is the only condition can make ambiguous claims.

    
    


Claims may not be merged together using a combo condition if the merge would cause ambiguity and one claim has a higher priority or a better connection.
EXAMPLE 85
When Priority Prevents Ambiguity


    
    
        P1: ‘a’
        P2: ‘a’ {priority: 1}
        P3: P1,P2
        Input: a
        The result will not be ambiguous as the priority of claim made by policy P2 is higher thab the priority of policy P1.
    
    


A better connection is defined to be based on the weight of the worst operator or operand used in a claim. The operators in order of weight are: adjacent, <,&. All operands in a claim have the same weight as the adjacent operator except for operands coming from the inexact matches of a database agent or a variable condition which have a weight worse than & and better than the combo (that weight is shown in the claim view as an INEXACT or {tilde over ( )}.
EXAMPLE 86
When Connection Prevents Ambiguity


    
    
        P1: ‘a’ ‘b’ ‘c’
        P2: ‘a’ ‘b’ & ‘c’
        P3: P1,P2
        Input: a b c
        P3 will not make an ambiguous claim as P1's claim has a better connection (adjacent vs. &)
    
    


Note that the number of operators will not make any difference here:
EXAMPLE 87
When Connection does not Prevent Ambiguity


    
    
        P1: ‘a’ & ‘b ’& ‘c’
        P2: ‘a’ & ‘b’ ‘c’
        P3: P1,P2
        Input: a b c
        P3 will be ambiguous as P1's claim has the same worst operator as the P2's claim (both are &)
    
    


When two claims are merged using the combo condition (and similarly the recursive, +, condition) then the resulting claim will have a loose connection. Claims with loose connections will fail to merg to other claims using the grammatical operators (such as &, <and adjacent).
EXAMPLE 88
Incorrect Usage of Combo Condition
P1: ‘a’, ‘b’
P2: P1 & ‘c’
Input: a b c
P2 will not apply.
The adjacency score of a claim made by a combo condition is not calculated directly from the focus of the claim (see adjacency score in Claim section). Instead it is calculated to be the sum of the adjacency scores of the sub-claims. For example, if P1 claims ‘cat’ and ‘mouse’ on the input ‘cat dog mouse’ and P2 claims ‘dog’, the adjacency score of (P1, P2) will be 1, whereas the adjacency score of (P1 & P2) will be 0. If P1 claims ‘cat’ on the input ‘cat and dog’ and P2 claims ‘dog’, the adjacency score of (P1,P2) will be 0, whereas the adjacency score of (P1 & P2) will be 1.
Recommended usage of combo operator is only in the catch policy (described later in the reference).
6 Actions
Actions transfers a claim to a standard format (e.g. an object containing XML)
6.1 Action Types:
6.1.1 Action:
The action that can be carried out unambiguously. The fields are: Delegate To; Execute; Explain; and Ask

    
    
        Delegate to: This field is used to delegate the actuation of a claim to other agents or policies responsible for making the claim. If you use this field the other fields would be ignored.
        Execute: The part that is ready to be executed by the application. This would be in a language of the application you're building the interface for (e.g XML).
        Explain: Human readable explanation of what was interpreted by the system (deprecated).
        Ask: A question to be asked from the user to narrow down the result. The agent that asks the question gets priority (focus). The agent asking the question should be the same agent that could claim the answer (refer to section 6 for more information).
    
    


EXAMPLE 89
Ations
TV: [‘TV’] ‘on’
{title: ‘TV’ }
{action: {execute:‘TV ON!’} {explain: ‘You asked to turn the TV on.’}
VCR: [‘VCR’] ‘on’
{title: ‘VCR’ }
{action: {execute: ‘VCR ON!’} {explain: ‘You asked to turn the VCR on.’}
HOME_ENTERTAINMENT: TV,VCR
{title: ‘HOME_ENTERTAINMENT’}
{action: delegate to TV, VCR}
{ambiguity action: delegate to TV, VCR}
User Input: TV on and VCR off
Actuation:
You asked to turn the TV on. You asked to turn the VCR on.
TV ON! VCR ON!
6.1.2 Ambiguity:
The action that will be carried out when there is an ambiguity in the claim. This action item is only used to delegate actuation to the overlapping sub claims that have caused the ambiguity. The sub claims, which caused the ambiguity, would be actuated normally and their actuations would be put together to make the ambiguity choices.

    
    
        Delegate to: This field is used to delegate the actuation of a claim to other agents or policies responsible for making the claim. If you use this field the other fields would be ignored.
        Execute: The part that is ready to be executed by the application. This would be in a language of the application you're building the interface for (e.g. XML).
        Explain: Human readable explanation of what was interpreted by the system (deprecated).
        Choice: A question to be asked from the user to resolve the ambiguity. The choices should refer to the agents or policies that caused the ambiguity.
    
    


For example lets say the TV and VCR policies would have an overlapping claim that has been put together by HOME_ENTERTAINMENT policy:
EXAMPLE 90
Ambiguity


    
    
        TV: [‘TV’] ‘on’
        {title: ‘TV’}
        {action: {execute: ‘<TV value=on />’ } {explain: ‘You asked to turn the TV on.’}
        VCR: [‘VCR’] ‘on’
        {title: ‘VCR’ }
        {action: {execute: <VCR value=on />} {explain:‘You asked to turn the VCR on.’}
        HOME_ENTERTAINMENT: TV,VCR
        {title: ‘HOME_ENTERTAINMENT’}
        {action: delegate to TV, VCR}
        {ambiguity action: delegate to TV, VCR}
        Which one do you mean?
        You asked to turn the TV on. You asked to turn the VCR on.
        
            1. TV
            2. VCR
        
        
    
    


When the system receives the input: ‘on’. The HOME_ENTERTAINMENT claim will then be ambiguous (as the claims made by TV and VCR would overlap). So the actuation made by the HOME_ENTERTAINMENT would be based on actuations made by TV and VCR policies. The actuation would be a multi choice menu. The menu prompt would be the made by putting together the explain actions of the TV and VCR actuations, and the menu items would be the titles of TV and VCR policies.
The explain field is now being deprecated. Instead the execute field will replace the explain field (when it is not provided):
EXAMPLE 91
Ambiguity Using Execute Field
TV: [‘TV’] ‘on’
{title: ‘TV’ }
{action: {execute: ‘TV ON!’ }
VCR: [‘VCR’ ] ‘on’
{title: ‘VCR’}
{action: {execute: ‘VCR ON!’ }
HOME_ENTERTAINMENT: TV,VCR
{title: ‘HOME_ENTERTAINMENT’ }
{action: delegate to TV, VCR}
{ambiguity action: delegate to TV, VCR}
User-Input: On
System:
Which one do you mean?
<TV value = on /><VCR value = on />
1. TV
2. VCR
The idea is that a scripting language would then take the interaction and make the proper prompt.
There are situations where there could be more than one questions asked for an ambiguity. For example the input bass in the network of FIG. 4 could generate ambiguity between FISH and DRUM or INSTRUMENTS and HOBBIES. The input ‘bass’ causes ambiguity in the HOBBIES agent (as bass could be a fish or a drum) and in the top agent (as bass can be a hobby or an instrument). Currently the system will find the source of the ambiguity and will only generate a question asking about that (in this case the source of ambiguity is if bass is a fish or a drum).
EXAMPLE 92
More than One Ambiguity
(TOP:

    
    
        “
    
    


(HOBBIES, INSTRUMENTS)+

    
    
        ”)
    
    


(HOBBIES:

    
    
        “
    
    


(FISH, DRUM)+

    
    
        ”)
    
    


(INSTRUMENTS:

    
    
        “
    
    


(DRUM)+

    
    
        ”)
    
    


(DRUM:

    
    
        “
        (‘bass’)+
        ”)
    
    


(FISH:

    
    
        “
        (‘bass’)+
        ”)
    
    


User: bass
System:
Which one do you mean?
1: FISH
2: DRUM
3: Ignore input
Selection (choose one):
Known issue:
There are network patterns that might lead to a too ambiguous situation. The following example demonstrates this situation. (See FIG. 5).
EXAMPLE 93
Too Ambiguous Error
(HOBBIES:

    
    
        “
        (INSTRUMENTS, DRUM)+
        ”)
    
    


(INSTRUMENTS:

    
    
        “
        (DRUM)+
        ”)
    
    


(DRUM:

    
    
        “
        (‘bass’)+
        ”)
    
    


User: bass
System:
Input too ambiguous. Please Rephrase!
6.1.3 Unknown action (deprecated):
The action that will be carnied out if the claim has unknown (there are missing information in the input). The same four fields are available.

    
    
        Delegate to: This field is used to delegate the actuation of a claim to other agents or policies responsible for making the claim. If you use this field the other fields would be ignored.
        Execute: The part that is ready to be executed by the application. This would be in a language of the application you're building the interfere for (e.g. XML).
        Explain: Human readable explanation of what was interpreted by the system (deprecated).
        Ask: A question to be asked from the user to narrow down the result. The agent that asks the question gets priority (focus). The agent asking the question should be the same agent that could claim the answer.

6.2 Action Building Blocks

    
    


Each field in the action forms (e.g. execute, explain, ask and choice) is created using variety of building blocks. When actuated each building block generates an output. These outputs would be put together to make the final action. Building blocks could be:
6.2.1 Strings
Outputs any information entered in quotes.
EXAMPLE 94
String Action Block












P1:


 ‘bruce willis’


    {title: ‘movie star’}


    {action:


      {explain: ‘You asked for a movie star’} }


 User-Input: bruce willis


P1 will make a claim. If the claim would be delegated to policy P1 then


the action would be: ‘You asked for a movie star’.









6.2.2 Star(*)

Outputs which part of the input was claimed by a policy.
EXAMPLE 95
Star Action Block












P1:


 ‘bruce willis’


 {title: ‘movie star’}


   {action:


     (execute: *}


User-Input: bruce willis


P1 will make a claim. If the claim would be delegated to policy P1 then


the action would be: ‘bruce willis’.









6.2.3 Variable (?)

Shows which part of the input was marked as a variable in a claim. The variable action corresponds to the variable condition in the policy with the same name.
EXAMPLE 96
Variable Action Block















P1:



    ‘starring’ ?:MOVIESTAR /DELIMITER



      {title: ‘movie star’}



      {action: delegate to P1}



      {action:



        execute: ‘who is the movie star ’,



   ?:MOVIESTAR ,‘ you are looking for?’} }










    
    
        User-Input: starring siamak
        P1 will make a claim. If the claim would be delegated to policy P1 then the action would be:
        Who is the movie star siamak you are looking for?
    
    


It is recommended to always use names with variables. However this is how multiple unnamed variables are handled:
EXAMPLE 97
Not Recommended Usage












P1:









‘send email from’ ? ‘to’ ?









{action: delegate to P1}



{action:



{execute: ? } }







User-Input: send email from Siamak to Kristi


P1 will make a claim. If the claim would be delegated to policy P1 then


the action would be: Siamak Kristi









Note that variable trims the leading and trailing delimiters, but other delimiters are considered to be part of the variable. Multiple variables are trimmed after they are combined.
EXAMPLE 98
Variable Trimmings












P1:









‘being’ ? ‘.’









{action: delegate to P1}



{action:



{execute: ‘var:’,?,‘.’ } }







User-Input: being john malkovich.


P1 will make a claim. If the claim would be delegated to policy P1 then


the action would be: var:john malkovich.









6.2.4 Policy Reference

Used as a reference to an action part of another policy.
EXAMPLE 99
Policy Reference Action Block















P1:



 ‘bruce willis’



   {title: ‘movie star’}



   {action:



     {execute: ‘<movie-star=’ , * , ′</>}



P2:



 ‘starring’ P1



   {title: ‘movie name’}



   {action:



     {execute: P1} }










    
    
        User-Input: starring bruce willis
        P1 will make a claim. If the claim would be delegated to policy P2 then the action would be delegated to P1 (the final result would be <movie-star=bruce willis</>)

6.2.5 Agent Reference

    
    


Used when the action part of another agent should be used to build this element.
EXAMPLE 100
Agent Reference Action Block












(In VOLUME agent)


P1:


DOWN









{title: ‘movie name’}



{action:









{execute: ‘<volume=’,DOWN,‘/>’} }







(In DOWN agent, downchain of the Volume agent)


P1:


‘down’









{title: ‘movie name’}



{action:









{execute: ‘down’} }







User-Input: down


Policy P1 in VOLUME agent will make a claim. If the claim would be


delegated to this policy then the action would be delegated to DOWN


agent (the final result would be <volume=down</>)









6.2.6 Set Message (set (agent, property, key; value))

The condition, set (agent, property, key; value), will cause the agent to sends a message to set a property in another agent. There are two types of properties that may be accessed through messaging:
c. Data property (basic objects)
d. Data store property (collection objects: hash tables, vectors, . . . )
Each agent must allow access to its data property by declaring it as a valid data property. The methods that should be sub-classed for this purpose are summarized in the following table:















Access permission
Set method
Set message



















Data
public Boolean
public void
<set (agent, property; value)>


property
IsValidDataProperty(
setData (



String propertyName,
String propertyName,




Object value,




ChainIdentifier




chainID)


Data
public Boolean
public void
<set (agent, property, key; value)>


Store
IsValidDataStoreproperty(
setDataStoreElement (


Property
String propertyName,
StringpropertyName,




String propertyKey,




Object propertyValue,




ChainIdentifier




chainID)









All AAOSA agents have a STATE property that could be set (STATE is a basic property).
The parameters of a set message, set (agent, property, key, value), are:
Agent: The agent address that its property is to be set. This parameter is optional. If omitted then an agent will “get” its own property.
Property: The name of the property to be set
Key: The key to the data store property (as a string). This parameter is optional. If omitted then an agent will “get” a basic property.
Value: The value that the property should be set to (as a string).
EXAMPLE 101
Set Action Block
action: set (CLOCK, ‘TIME’; ‘12:30’)
6.2.7 Get Message (<get (agent, property, key)>)
Sends a get message to an agent, and shows the value replied by that agent. There are two types of properties that may be accessed through messaging:
e. Data property (basic objects)
f Data store property (collection objects: hash tables, vectors, . . . )
Each agent must allow access to its data property by declaring it as a valid data property. The methods that should be sub-classed for this purpose are summarized in the following table:















Access permission
Set method
Set message



















Data
public Boolean
public Object
<get (agent, property)>


Property
IsValidDataProperty (
getData (



String propertyName)
String propertyName,




ChainIdentifier




chainID)


Data
public Boolean
public Object
<get (agent, property, key)>


Store
IsValidDataStoreProperty
getDataStoreElement (


Property
(String propertyName)
String propertyName,




String propertyKey,




ChainIdentifier




chainID)









All AAOSA agents have a STATE property (STATE is a basic property).
The parameters of a get message, set (agent, property, key, value), are:
Agent: The agent address that it's property is to be set. This parameter is optional. If omitted then an agent will “get” its own property.
Property: The name of the property to be set
Key: The key to the data store property (as a string). This parameter is optional. If omitted then an agent will “get” a basic property.
EXAMPLE 102
Get Action Block
action: get(CLOCK, ‘TIME’)
6.3 Default Delegation
Whenever the action part of a policy is left empty a default delegation would be used. By default actions are delegated to policies responsible for making the sub claims. That is the policy:
POLICY2: AGENT1,AGENT2, POLICY1
Will be interpreted as:
POLICY2: AGENT1,AGENT2, POLICY1

    
    
        {action: delegate to AGENT1, AGENT2, POLICY1}
        {ambiguity action: delegate to AGENT1, AGENT2, POLICY1}
        {unknown action: delegate to AGENT1, AGENT2, POLICY1}
    
    


The default delegation could be overridden by filling the action part. For example the following policy:
POLICY2: AGENT1,AGENT2, POLICY1

    
    
        {action: {execute: *} }

Will be interpreted as:

    
    


POLICY2: AGENT1,AGENT2, POLICY1

    
    
        {action: {execute: *} }
        {ambiguity action: delegate to AGENT1, AGENT2, POLICY1}
        {unknown action: delegate to AGENT1, AGENT2, POLICY1}

6.4 Actuation

    
    


Each policy can make an actuation by applying the action part to the claim it made. Actuation may be built by putting together other actuations (just like claims). Each actuation will have four arms: execute, explain, ask, and choice. Each arm will contain a list of strings or pointers to other actuations.
There is a difference when an actuation is converted to a string if the building blocks of the actuation are listed in one field or different fields. When building blocks are listed together there would be no space between each block that is converted to a string.
EXAMPLE 103
Actuation Example












P1:









‘aaa bbb ccc’



{action:









execute: ‘aaa’ , ‘bbb’ , ‘ccc’}







User-Input: aaa bbb ccc


P1 will make a claim. If the claim would be delegated to policy P1 then


the action would be: aaabbbccc.









When building blocks are listed in different fields there would be a space inserted between each block that is converted to a string.
EXAMPLE 104
Actuation












P1:









‘aaa bbb ccc’



{action:









{execute: ‘aaa’ , ‘bbb’ , ‘ccc’}







User-Input: aaa bbb ccc


P1 will make a claim. If the claim would be delegated to policy P1 then


the action would be: aaa bbb ccc.









Implementation note: The space is actually the default join style. The idea is to have different join styles that could be set in the policy. For example AND joins, OR joins, XOR joins, . . .

7 Putting it All Together

The agents are normally composed of 3 types of policies:
7.1 Tokens:
The condition of these policies has no reference to any other agent. The policies are made using binary and unary operators on token conditions, file terminals or database terminals.
7.2 Grammars
The condition of these policies makes grammatical relationship between the policies in this agent and its down chain agents. These grammatical relationships are made by using &, |, <, optional and adjacent operators. All that can be part of one command should be combined using the &, <or adjacent operators.
7.3 Catch Policy
The catch policy of an agent is used to make the claims that no policies were written to deal with. That includes different combination of commands and ambiguities that may occur. The catchall policy includes a combo of references to all other policies and down chain agents of that agent followed by a + operator. A | (or) condition should be used instead of a combo between any two policies or down chain agents which should never make ambiguities. For example, assume agents A, B and C are all downchain of Top agent.
TOP agent policies:
P1: . . .
P2: . . .
. . .
CATCH_ALL: (P1, P2, . . . , A, B, C)+
The following is an example of how a user interface could be built for a store where books and toy in different colors could be bought.















(TOKENS:



  “



  (‘red’|‘blue’|‘green’)



    {title: ‘A color’}



    {action:



      {execute: ‘<color=’,*,‘/>’}}



  ”),



(RELATION:



  “



  TOKENS   (‘and’ TOKENS)



    {title: ‘Colors’}



  ”),



(CATCH:



  “



  (TOKENS, RELATION) +



    {title: ‘Color’



  ”)










Step 3. Creat the TOY agent the same as the BOOK agent and join BOOK and TOY agents as down chain agents of the STORE agent (FIG. 8).

(CATCH:

“
(.BOOK, TOY)+
”)
8 Best Practices Guidelines for Agent Network Design
1) Always include comments in your policies briefly describing why they are there and what they do. In general an observer should be able to understand the logic of why you have coded your agent the way you have. If it is too complex to understand, it is very likely that it needs some cleaning up.
2) If there are more than 6 policies in an agent rethink using a single agent for all these policies. It may also be that the agent has too big a fan-out.
3) Avoid too many agents reporting to a single agent (larger than 6 fan-out). Add agents to group down-chains into sub networks. Make sure the groupings correlate with the semantics of the application domain. Just as in menu structures, a big fan out is untidy and in our case they can also result in inefficient processing of policies that include operators like A or
Categorization is possible and quite natural. After all, this is how our minds work to enhance our memory. It is much harder to memorize 16 down chains for one agent than it is 4, each of which lead to another.
We should use a domain expert in this case to identify the categorization.
The merits of limiting the fan out can be summarized as:

    
    
        Efficiency (memory and speed),
        Better reusability,
        Better Interactions (where top agent knows input belongs to it but doesn't know which down-chain to delegate it to).
    
    


As for the fan out, if there are many agents dowchain to one, it is very likely that there does exist a categorization for these concepts in the domain expert's lingo that we are missing and we should probably cover. If this is the case, then recategorizing and decreasing the fan out based on the domain classificafions and the resulting modularity will make those categories more readily reusable, and the interactions with the application more manageable for the end users. It will also make the policies in the top agent more readable and tidy. This added modularity may also help the efficiency of the application where operators such as “&” or other firm operators are used between all down-chains. This is because it reduces the number of claims the up-chain agent to the top node will be receiving when a policy containing operators such as the “&” of the down chains fires.
4) Avoid creating agents that do not represent the semantics of the application.
5) Start each agents list of policies with policies that pick the tokens associated with this agent. Then follow with policies that describe the relationships. At the end, include catch-all policies.
TOKENS: The condition of these policies has no reference to any other agent. The policies are made using binary and unary operators on token conditions, file terminals or database terminals.
RELATIONS: The condition of these policies describe the relationships between the policies in this agent and its down chain agents. These grammatical relationships are made by using operators such as &, |, <, optional and adjacent. All that can be part of one command should be combined using these operators.
CATCHALLS: The catchall policy of an agent is used to make the claims that no policies were written to deal with. That includes different combination of commands and ambiguities that may occur. The catchall policy includes a combo of references to all other policies and down chain agents of that agent followed by a + operator. A | (or) condition should be used instead of a combo between any two policies or down chain agents which should never make ambiguities.
6) Avoid using catch-alls extensively and try to cover them in Relations policies. For example try using the repeat operator (^) more often.
Try not to repeat all of the contents of another policy in the following policies. It is inefficient and untidy:
Bad:


    
    
        TOKENS: ‘web’ | ‘wap’
        RELATIONS: (‘web’ ‘wap’) & INDEX

Good:

        TOKENS: ‘web’ ‘wap’
        RELATIONS: TOKENS & INDEX
    
    


8) Any policy on its own may make a claim. Do not have policies that may claim lone words that are not relevant to the agent:
DUMMY_TOKEN: ‘the’
In this case an agent, THE, should be created which claims ‘the’ but is never delegated to or referred to without constraining conditions around it.
9) Avoid using tags. Using “tags” and “check tags” mean that an agent has to be aware of policies of another agent (which could be a policy of an agent that is not an immediate down chain). This is against the agent oriented design principle where an agent should not care about the policies of another agent and should not be aware of any agent other than its immediate down chains. Therefore “tags” and “check tags” should be avoided if possible. There is usually a better way to write the policies or configure the agent network to avoid the usage of tags.
10) Avoid using <exists>
RELATIONS: <exits>‘token’
When using <exists>, an agent is checking against a part of the input that it is not going to claim. Therefore the agent must be aware of parts of the input that are possibly another agent's responsibility. This is against the agent oriented design principle where an agent should only care about its own domain and not worry about other domains. Therefore <exists> should always be avoided if possible. There is usually a better way to write the policies or configure the agent network to avoid the usage of <exists>.
There are cases where using <exists> is appropriate as in the example below:
In this example, we have a top level FIND agent. Below FIND there is a FILETYPE agent and a FOLDER agent. FILETYPE has down-chain agents that recognize specifications for different kinds of files; FOLDER uses down-chain agents to identify specs for starting directories.
Now imagine a request like “java in web”. Both “java” and “web” are valid file types; both are valid directory names. What we wanted to do in FILETYPE is say that if we see a file spec followed by the word ‘in’, to see that as a stronger claim than one without it. Similarly, we want FOLDER to use ‘in’ to indicate that what follows may be a directory spec.
Clearly, we can't have both agents claiming ‘in’. But ‘in’ is important for both. So we use <exists> to give the policy with ‘in’ a higher priority.
Avoid policies that may make a null claim:
TOKENS: [‘a’ ]
NULLTOKENS: [‘a’] | [‘b’]
INTERESTING_TOKENS: [‘a’] | ‘b’]
NOTOKENS: <exists> ‘a’
WOWTOKENS: [‘a’] & <exists> ‘b’
OWTOKENS: <exists> (‘a’ & ‘b’)
9 Precedence Table
The OPAL language guarantees that the operands of operators appear to be evaluated in a specific evaluation order, namely, from left to right.












Precedence
Operator type
Operator







0
Terminals and
‘sring'



references
/NUMBER,




/SYMBOL,




/TYPE,




/F,




/DB,




/IDB,




<check (. . .)>,




AGENT reference,




POLICY reference,




?,




. . .


1
Brackets
( operand )




[ operand ]




operand+


2
Unary
<exists> operand




<check . . .>




<unknown . . .>operand


3
Binary adjacent
operand1 operand2




operand1 {circumflex over ( )} operand2


4
Binary ordered
operand1 < operand2




opernad1 <{circumflex over ( )} operand2


5
Binary and
operand1 & operand2




opernad1 & {circumflex over ( )} operand2


6
Binary or
operand1 | operand2


7
Binary combo
operand1 , operand2


8
Unary tag
<tag:. . .> operand




<check tag:. . .> operand





Notes: It is generally a good idea to use parentheses liberally in expressions involving mixed operators to avoid operator precedence problems.






10 Learning

A followup answer will be learned only if:
There would be a variable terminal (?) in the condition of the policy.
The agent asking the question would make a claim on the followup.
11 Claim Manipulation
The recursive operator (+) and most binary operators (adjacent, &, <, + and combo) make their claims by merging other claims.
Merging claims:
Two claims C1 and C2 will merge if and only if one of the following conditions would hold:


    1. Cfocus1∪Cfocus2=φ (if C1's focus does not intersect with C2's focus)
    2. Cloose-connecton-count1=0 and
    
        Cloose-connection-count=0 and
        Cfocus1∉Cfocus2 and
        Cfocus2∉Cfocus1 and
        Cpriority1=Cpriority2 and
        (Cconnection1=Cconnection2 or Cambiguity1>0 or Cambiguity>0)
    
    
    3. Conditions 1 and 2 hold for merging all claims that have been connected loosely to build C1 and all subclaims that have been connected loosely to build C2.

Ambiguous claims:

A claim C made by merging C1 and C2 is ambiguous if their focus' overlap: Cfocus1∪Cfocus2≠φ

12 Policies in BNF Notation

12.1 BNF Notation



The following is the BNF notation used to describe the Policy's syntax:
::= Is interpreted as ‘consists of’ or ‘is defined as’.
( )+ Parenthesis followed by a plus sign indicate that the sequence in the parenthesis may be repeated more than one time.
( )* Parenthesis followed by a plus sign indicate that the sequence in the parenthesis may be repeated zero or more times.
{tilde over ( )} ( ) Parenthesis preceeded by a not sign indicate that all sequences except those in the parenthesis are legal.
[ ] Square brackets state that the sequence in the brackets is optional.
< > Names embedded in angle brackets are syntactic elements such as <integers>, <names>etc. All syntactic names will be defined i.e. they will appear on the left side of the ‘::=’ symbol in a grammar rule.
“ ” Characters embedded in double quotes are keywords or characters.
- A dash between two characters defines a range of characters. For example “0”
- “3” is “0”| “1” |“2” |“3”.
| A vertical bar is read ‘or’ and is used to separate alternatives.
Whenever a BNF notation is used as a keyword in the grammar it is preceded by an escape character: ‘\’.
12.2 Policy's BNF Notation
Here is a BNF notation of the policy's grammar.












policy ::=


 label:


 “\”′′


  <condition>


  [“{“ “title:” <title> ”}”]


  [“{“ “xml tag:” <string literal> ”}”]


  [“{“ “xml type:” <string literal> ”}”]


  [“{“ “priority:” <integer> ”}”]


  [“{“ “continuation:” (“join”|“replace”)  ”}”]


  [“{“ “action:” <action>}]


  [“{“ “ambiguity action:” <action> ”}”]


  [“{“ “unknown action:” <action> ”}”]


 “\”′′


<label> ::= [“A” - “Z”|“_”] ([“A” - “Z” | “0” - “9” | “_” | “-”])*


<title> ::= <string_literal>


<integer> :: = [+|−] (“0” - “9”)+


<condition> ::= “(“ <condition> ”)”


<condition> ::= <ADJACENT_condition>


<condition> ::= <ORDERED_condition>


<condition> ::= <AND_condition>


<condition> ::= <OR_condition>


<condition> ::= <COMBO_condition>


<condition> ::= <OPTIONAL_condition>


<condition> ::= <TERMINAL_condition>


<condition> ::= <EXISTS>


<condition> ::= <TAG_condition>


<condition> ::= <CHECK_TAG condition>


<condition> ::= <CHECK_condition>


<OPTIONAL_condition> ::= “[” <condition> ”]”


<UNKNOWN_condition> ::= “<unknown if missing>” <condition>


<ADJACENT_condition> ::= <condition> <condition> |


          <condition> “{circumflex over ( )}” <condition>


<ORDERED_condition> ::= <condition> “<” <condition> |


          <condition> “{circumflex over ( )}” <condition>


<AND_condition> ::= <condition> “&” <condition> |


          <condition> “&{circumflex over ( )}” <condition>


<OR_condition> ::= <condition> “|” <condition>


<COMBO_condition> ::= <condition> “,” <condition>


<TERMINAL_condition> ::= <terminal>


<EXISTS_condition> ::= “<” “exists” “>” <condition>


<TAG_condition> ::=


 “<” “tag” “:” <string literal> “>” <condition>


<CHECK_TAG_condition> ::=


 “<” “check” “tag” “:” <string_literal> “>” <condition>


<CHECK_condition> ::=


 “<” “check” “(” <agent_address> “,” <property_name> “,”


 <property_key> “;” <property_value> “)” “>” <condition>


<terminal> ::= <token_provider>


<terminal> ::= <number>


<terminal> ::= <symbol>


<terminal> ::= <agent_name>


<terminal> ::= <policy_name>


<terminal> ::= <variable>


<token_provider> ::=


 [“[“ <matcher-list> ”]”] “′” <string_literal> “′”


<token_provider> ::=


 [“[“ <matcher-list> ”]”] /F “′” <string_literal> “′”


<token_provider> ::=


 /DB “′” <string_literal> “′”


<matcher-list> ::=


 “<” (<matcher> | (<matcher> “,” <matcher-list>)) “>”


<matcher> ::=


 “exact” | “substring” |


 “partial” | “accent” |“accent-edit-difference”


<number> ::= “/NUMBER” [ “(“ <integer> “,” <integer> ”)” ]


<symbol> ::= “/SYMBOL”


<agent_name> ::=


 [“A” - “Z”|“_”] ([“A” - “Z” | “0” - “9” | “_” | “-”])*


<policy_name> ::= (“/P” ([“0” - “9”])+)


<variable> ::= “?”


<string_literal> ::=


 “′” (~([“\”“,”\\“,”\n“,”\r“, ”′“])  |


 <special_characters> ) * “′”


<special_characters> ::=


 %BACK_SLASH% | %SINGLE_QUOTE% |


 %DOUBLE_QUOTE% | %NEW_LINE% 


<action> ::=


 [“{“ “execute:” <action_element_list> ”}”]*


 [“{“ “ask:” <action_element_list> ”}”]*


 [“{“ “choice:” <action_element_list> ”}”]*


 [“{“ “explain:” <action_element_list> ”}”]*


<action_element_list> ::=


 <variable_action_element> [“,” <action_element_list>]


<action_element_list> ::=


 <constant_action_element> [“,” <action_element_list>]


<variable_action_element> ::= “*”


<variable_action_element> ::= ?”


<variable_action_element> ::= <agent_name>


<variable_action_element> ::= <policy_name>


<variable_action_element> ::=


 “set” “(“ [<agent_address> “,”] <property_name> “,”


 <property_key> “;” <property_value> ”)”


<variable_action_element> ::=


 “get” “(“ [<agent_address>“, ”] <property_name> “,”


 <property_key> ”)”


<constant_action_element> ::= <string_literal>


<property_name> ::= <string_literal>


<property_key> ::= <string_literal>


<property_value> ::= <string_literal>


<expression> ::= <string_literal>


<agent_address> ::=


 <agent_name> [“.” <agent_class> “@” <agent_domain> ]


<agent_class> ::= <string_literal>


<agent_domain> ::= <string_literal>

## Claims

1. A user interaction method, comprising the steps of:
instantiating an actuation module having a back-end interface arranged to communicate with a first back-end application; and
instantiating a natural language interpreter having an input for receiving token sequences generated from user input, and an output for outputting interpretation result commands in an application-independent form toward said actuation module in response to said token sequences;
said actuation module receiving a first one of said interpretation result commands, translating said first interpretation result command into a set of at least one application-specific application command for said first back-end application, and forwarding said at least one application command toward said first back-end application, and
said actuation module receiving a command result from said first back-end application, translating said command result into a common interaction format, and forwarding said command result toward said user.

2. A method according to claim 1, further comprising the steps of:
said natural language interpreter issuing a clarification request in response to one of said token sequences and forwarding said clarification request toward said user.

3. A method according to claim 1, wherein said first back-end application supports at least one member of the group consisting of email management and contacts management.

4. A method according to claim 1, further comprising the steps of:
in conjunction with said actuation module translating said first interpretation result command into a set of at least one application-specific application command for said first back-end application, said actuation module developing a set of at least one application-specific undo command for said first back-end application, the set of at least one application-specific undo command being effective to undo the set of at least one application-specific application command; and
in response to user input after the step of said actuation module forwarding said command result toward said user, said actuation module forwarding said at least one application-specific undo command toward said first back-end application.

5. A user interaction method, comprising the steps of:
instantiating an actuation module having a back-end interface arranged to communicate with a first back-end application; and
instantiating a natural language interpreter having an input for receiving token sequences generated from user input, and an output for outputting interpretation result commands in an application-independent form toward said actuation module in response to said token sequences;
said actuation module receiving a first one of said interpretation result commands, translating said first interpretation result command into a set of at least one application-specific application command for said first back-end application, and forwarding said at least one application command toward said first back-end application, and
said natural language interpreter issuing a clarification request in response to one of said token sequences and forwarding said clarification request toward said user.

6. A user interaction method, comprising the steps of:
instantiating an actuation module having a first back-end interface arranged to communicate with a first back-end application and a second back-end interface arranged to communicate with a second back-end application; and
instantiating a natural language interpreter having an input for receiving token sequences generated from user input, and an output for outputting interpretation result commands in an application-independent form toward said actuation module in response to said token sequences;
said actuation module receiving a first one of said interpretation result commands, translating said first interpretation result command into a set of at least one application-specific application command for said first back-end application, and forwarding said at least one application command toward said first back-end application,
and said actuation module receiving a second one of said interpretation result commands, translating said second interpretation result command into a set of at least one second application command for said second back-end application, and forwarding said at least one second application command toward said second back-end application.

7. A method according to claim 6, further comprising the steps of:
said actuation module receiving a second command result from said second back-end application, translating said second command result into said common interaction format, and forwarding said second command result toward said user.

8. A user interaction method, comprising the steps of:
instantiating an actuation module having a back-end interface arranged to communicate with a first back-end application; and
instantiating a natural language interpreter having an input for receiving token sequences generated from user input, and an output for outputting interpretation result commands in an application-independent form toward said actuation module in response to said token sequences,
said actuation module receiving a first one of said interpretation result commands, translating said first interpretation result command into a set of at least one application-specific application command for said first back-end application, and forwarding said at least one application command toward said first back-end application,
wherein said natural language interpreter comprises:
a plurality of agents arranged in a network, each of said agents having at least one interpretation policy having a policy condition and a policy action associated with the condition, each of said interpretation policies making a claim on at least part of a token sequence provided to the agent if the condition of the interpretation policy is satisfied, and executing the associated policy action in response to determination that the claim is at least part of a winning claim.

9. A method according to claim 8, wherein a particular one of said agents has a particular interpretation policy having a policy condition that refers to a downchain one of said agents, any claim made by said particular policy being dependent upon any claims made by said downchain agent.

10. A method according to claim 8, wherein a particular one of said agents has a particular interpretation policy having a particular policy condition that refers to a downchain one of said agents, said particular interpretation policy further having a particular policy action associated with said particular policy condition,
wherein said particular policy action includes at least partial delegation to said downchain agent.

11. A user interaction method, comprising the steps of:
instantiating an actuation module having a back-end interface arranged to communicate with a first back-end application;
instantiating a natural language interpreter having an input for receiving token sequences generated from user input, and an output for outputting interpretation result commands toward said actuation module in response to said token sequences;
said natural language interpreter outputting a first interpretation result command toward said first back-end application in response to a first one of said token sequences;
said first back-end application providing a first response to said first interpretation result command in an application-specific form; and
said actuation module translating said first response into an application-independent form, and communicating said first response toward said user.

12. A method according to claim 11, further comprising the step of instantiating an I/O subsystem,
further comprising the step of said I/O subsystem generating said first token sequence in response to first user input,
and wherein said step of said actuation module communicating said first response toward said user comprises the steps of:
said actuation module communicating said first response to said I/O subsystem, bypassing said natural language interpreter; and
said I/O subsystem forwarding said response toward said user.

13. A method according to claim 11, further comprising the steps of:
said natural language interpreter outputting a second interpretation result command toward a second back-end application in response to a second one of said token sequences;
said second back-end application providing a second response to said second interpretation result command in an application-specific form; and
said actuation module translating said second response into said application-independent form, and communicating said second response toward said user.

14. A method according to claim 11, for use with a plurality of back-end applications including said first application, wherein said step of said natural language interpreter outputting a first interpretation result command toward said back-end application comprises the steps of:
said natural language interpreter outputting said first interpretation result command to said actuation module;
said actuation module developing a plurality of application commands in response to said first interpretation result command; and
said actuation module communicating each of said application commands to a corresponding destination one of said plurality of back-end applications, said destination applications including at least said first back-end application and a second back-end application,
said second back-end application providing a second response to said first interpretation result command in an application-specific form; and
said actuation module combining said second response with said first response for communicating toward said user in said application-independent form.

15. A method according to claim 14, wherein said step of said actuation module communicating each of said application commands to a corresponding destination one of said plurality of back-end applications comprises the step of said actuation module communicating each of said application commands to the corresponding destination one of said plurality of back-end applications in a form specific to the destination application.

16. A method according to claim 11, wherein said natural language interpreter comprises a distributed parser of natural language input.

17. A method according to claim 11, wherein said natural language interpreter comprises an agent network.

18. A method according to claim 11, wherein said natural language interpreter comprises:
a plurality of agents arranged in a network, each of said agents having at least one interpretation policy having a policy condition and a policy action associated with the condition, each of said interpretation policies making a claim on at least part of a token sequence provided to the agent if the condition of the interpretation policy is satisfied, and executing the associated policy action in response to determination that the claim is at least part of a winning claim.

19. A method according to claim 18, wherein a particular one of said agents has a particular interpretation policy having a policy condition that refers to a downchain one of said agents, any claim made by said particular policy being dependent upon any claims made by said downchain agent.

20. A method according to claim 18, wherein a particular one of said agents has a particular interpretation policy having a particular policy condition that refers to a downchain one of said agents, said particular interpretation policy further having a particular policy action associated with said particular policy condition,
wherein said particular policy action includes at least partial delegation to said downchain agent.

21. A method according to claim 11, wherein said agent network prepares said commands in dependence upon satisfaction, in said token sequences, of one or more policy conditions which make reference to one or more keywords in said predetermined set of keywords.

22. User interactive apparatus comprising:
a natural language interpreter having an input for receiving token sequences from a user interaction subsystem and an output for outputting interpretation result commands in response to said token sequences; and
an actuation subsystem in communication with first and second back-end applications, said actuation subsystem communicating first and second application commands to respectively said first and second back-end applications, both in response to a single one of said interpretation result commands.

23. Apparatus according to claim 22, wherein said natural language interpreter comprises a distributed parser of natural language input.

24. Apparatus according to claim 22, wherein said natural language interpreter comprises an agent network.

25. Apparatus according to claim 22, wherein said natural language interpreter comprises:
a plurality of agents arranged in a network, each of said agents having at least one interpretation policy having a policy condition and a policy action associated with the condition, each of said interpretation policies making a claim on at least part of a token sequence provided to the agent if the condition of the interpretation policy is satisfied, and executing the associated policy action in response to determination that the claim is at least part of a winning claim.

26. Apparatus according to claim 25, wherein a particular one of said agents has a particular interpretation policy having a policy condition that refers to a downchain one of said agents, any claim made by said particular policy being dependent upon any claims made by said downchain agent.

27. Apparatus according to claim 25, wherein a particular one of said agents has a particular interpretation policy having a particular policy condition that refers to a downchain one of said agents, said particular interpretation policy further having a particular policy action associated with said particular policy condition,
wherein said particular policy action includes at least partial delegation to said downchain agent.

28. Apparatus according to claim 22, wherein said agent network prepares said interpretation result commands in dependence upon satisfaction, in said token sequences, of one or more policy conditions which make reference to one or more keywords in said predetermined set of keywords.

29. Apparatus according to claim 22, wherein said actuation subsystem further communicates command results from each of said first and second back-end applications to said user interaction subsystem.

30. Apparatus according to claim 29, wherein said actuation subsystem converts said command results to a common interaction format before communicating them to said user interaction subsystem.

31. Apparatus according to claim 30, wherein said natural language interpreter further has an output for providing clarification requests toward said user.

32. Apparatus according to claim 31, wherein said natural language interpreter communicates said clarification requests toward said user via said user interaction subsystem in said common interaction format.

33. Apparatus according to claim 22, wherein said first back-end application supports at least one member of the group consisting of email management and contacts management.

34. User interactive apparatus comprising:
a natural language interpreter having an input for receiving token sequences from a user interaction subsystem and an output for outputting interpretation result commands in response to said token sequences; and
an actuation subsystem in communication with first and second back-end applications said actuation subsystem communicating first and second application commands to respectively said first and second back-end applications in response to said interpretation result commands,
wherein said actuation subsystem communicates said first application command to said first back-end application in response to a first one of said interpretation result commands and communicates said second application command to said second back-end application in response to a second one of said interpretation result commands.

35. User interactive apparatus comprising:
a natural language interpreter having an input for receiving user input from a user and an output for outputting interpretation result commands toward first and second back-end applications in response to said user input; and
an actuation subsystem having interfaces to each of said first and second back-end applications, said actuation subsystem being arranged to receive command results from both said first and second applications, and to convert said command results to a common interaction format, and to communicate them in said common interaction format toward said user.

36. Apparatus according to claim 35, wherein said actuation subsystem communicates said command results in said common interaction format toward said user via a path that bypasses said natural language interpreter.

37. Apparatus according to claim 35, wherein said natural language interpreter comprises a distributed parser of natural language input.

38. Apparatus according to claim 35, wherein said natural language interpreter comprises an agent network.

39. A user interaction method, for use with a first back-end application, comprising the steps of:
developing an interpretation result command in an application-independent form in response to token sequences generated from user input;
developing a set of at least one application-specific undo command for said first back-end application in dependence upon said application-independent interpretation result command, the set of at least one application-specific undo command being effective to undo the interpretation result command; and
in response to subsequent user input, forwarding said at least one application-specific undo command toward said first back-end application.

40. A method according to claim 39, further comprising the steps of:
translating said application-independent interpretation result command into a set of at least one application-specific application command for said first back-end application; and forwarding said at least one application command toward said first back-end application.