Method for optimized rete pattern matching in pattern-directed, rule-based artificial intelligence production systems

Abstract: A demand-driven AI production system utilizing a RETE network for comparison matching in a condition/data match, rule-selection, and rule-firing execution cycle in which the RETE network is modified to maintain a list of instantiations satisfying the match conditions expressed in each node of the RETE network, passing of tokens to descendant nodes upon a comparison match, maintaining patterns to all ancestor nodes through which the tokens have passed, and traversing the patterns as a path for avoiding those RETE pattern matchings redundant between a previous match and a current match in progress.

Description: The invention relates to artificial intelligence (AI) production systems, and more particularly, to a method for optimizing the pattern-matching phase of a cyclic, rule-based, data-sensitive AI production system. It should be appreciated that the artificial intelligence branch of computer science has exhibited explosive growth in recent years. One facet of AI has been concerned with the modeling and use of inference systems. Such inference systems exploit the computer science formalism termed "rewrite" or "production" systems. At a minimum, a rewrite or production system includes an alphabet, an initial set of strings (axioms) defined over the alphabet, and a set of production or rewrite rules from which new strings (theorems) can be obtained. Peter Jackson, "Introduction to Expert Systems", Addison-Wesley Publishing Co., copyright 1986, pp. 29-51 and 126-141, points out that an AI production system comprises a rule set (sometimes called production memory), a rule interpreter that decides how and when to apply the rules, and a working memory that holds data, goals, and intermediate results. Brownston et al, "Programming Expert Systems in OPS5", Addison-Wesley Publishing Co., copyright 1985, pp. 4-31, contrast a rule-based production computational model with that of a procedural model. More particularly, Brownston describes a production system as an executable set of production rules in which each rule in such a system represents an ordered pair of a condition (state set) and action statements. ffThis leads to Brownston's characterization of a production system as an unordered finite sequence of data-sensitive production rules. Brownston's Comparison Production System Model of Computation Procedural Model of Computation Program Description: Program Description; A description of data expressed as objects, attributes, and values, and an unordered finite sequence of rules that can be referenced by the data. Each rule consists of a condition/pattern part and an action part. An ordered list of instructions written in a language with a well-defined syntax and semantics. The list has a specified beginning. The language includes a stop/halt instruction or punctuation whose meaning is to cease instruction excecution. Execution: Execution: Requires maintenance of a global data base containing the problem description and any modifications, additions, or deletions thereto and a recognize, resolve, act (RRA) or match, select, excecute cycle. The cycle: The instructions are directly executable. The first instruction in the list initiates execution. After this, execution proceeds in sequence punctuated by conditional branches until a stop or halt instruction is encountered. (a) identifies that subset of rules having a condition or pattern part matching the data, (b) selects at least one rule from the identified subset of rules according to an extrinsic protocol, and (c) executes (fires) the action prescribed by the action part of selected rule including modification to the data base. In addition to Jackson and Brownston, reference should also be made to: (1) Miranker, "TREAT: A Better Match Algorithm for AI Production Systems", Proceedings of the AAAI-87 Sixth National Conference on Artificial Intelligence, Vol. 1, July 13-17, 1987, pp. 42-47. (2) Miranker, Dept. of Computer Science, University of Texas at Austin, Report TR-87-03, January 1987. (3) Forgey, "OPS5 Users Manual", CMU-CS-81-135, copyright 1981. (4) Forgey, "Rete: A Fast Algorithm for the Many Pattern/Many Object Pattern Match Problem", Artificial Intelligence, Vol. 19, copyright 1982, pp. 17-37. (5) Schor et al, "Advances in Rete Pattern Matching", Proceedings of AAAI '86. (6) Chambers et al, "Distributed Computing", Academic Press, copyright 1984, pp. 10-19. (7) Aho et al, "Compilers: Principles, Techniques, and Tools", Addison-Wesley Publishing Co., copyright 1986, pp. 608-632. Brownston, Miranker, Forgey, and Schor describe pattern-driven, forward-chaining production systems using a matching-rule, select-rule, execute-rule cycle based on the OPS5 AI language. Furthermore, these references point out that the process of many data-object/many pattern matching is the most computationally intensive phase in the production system control cycle. The references teach several techniques for reducing the computational intensity of the pattern-matching phase. First, advantage can be taken of the temporal redundancy of the data objects resident in working memory. That is, the set of objects over which the pattern portions of the rules are compared can be limited to those objects in working memory which have been either created dr modified since the last cycle. Second, the many object/many pattern comparison can be systematized through use of sorting or dataflow-like processing networks. While there are other comparison algorithms, such as Miranker's TREAT, the best known comparison method having these attributes is the RETE algorithm ascribed to Forgey. Forgey and Jackson discuss the RETE pattern/object matching method used in the AI production system control cycle. This method includes: (a) compiling the condition elements of the pattern portion of a rule into an augmented data flowgraph (see Aho and Chambers) or a comparison sorting network (Forgey); (b) comparing each object with conditions of the pattern as expressed in the compiled network over a set of nodes (alpha-nodes) that each test one object at a time; (c) passing tokens indicative of a match from antecedent nodes to descendant nodes (beta-nodes) joined on a pattern-determined basis, comparing each token received at a descendant node, and passing tokens on to descendants in turn until the paths through the flowgraph are traversed; and (d) maintaining a list of instantiations satisfying the match conditions expressed at each node. In the remainder of this specification, the terms MAKE and CREATE, MODIFY and UPDATE, and REMOVE and DELETE will be used interchangeably. Also, the RECOGNIZE, RESOLVE, and ACT phases of the AI production system control cycle serve as synonyms for MATCH, SELECT, and EXECUTE. It is an object of this invention to devise a method for optimizing the pattern-matching phase of a cyclic, rule-based, data-sensitive production system. It is a related object to devise a method in which a path through a RETE network used during the comparison phase can be traced, thereby minimally invoking the comparison, token passing, and recording aspects of the RETE algorithm. It is yet another object to devise a method utilizing the path for deletion of an object, including removal of any counterpart instantiations recorded at the lists maintained by various nodes through which a token has passed. It is a further object to devise a method in which alteration of objects by a MODIFY command is accomplished as a function of the DELETE and MAKE commands and their associated processes. The aforementioned objects are satisfied by the method steps executed during the pattern-matching portion of the matching, selection, and execute cycle of an AI production system comprising: (a) compiling a RETE network of the condition elements of the pattern portion of the rule being matched, the join nodes of said network being grouped in a pattern-determined associative manner; and (b) applying those data objects created or modified in the immediately preceding cycle to said RETE network. The method includes the further steps at each node of: (b1) maintaining a list of instantiations satisfying the match conditions expressed at that node, (b2) passing tokens to descendant nodes upon an object/pattern comparison match, (b3) maintaining pointers to all ancestor nodes through which the token for each object passed, and (b4) traversing said pointers as a path for avoiding those RETE node pattern/object matchings redundant between a previously matched object and an object being processed. The invention, which is defined in the attached claims, is described in detail below with reference to the drawings, of which; Figure 1 depicts a directed graph RETE network produced during compilation of the pattern portion (left hand side) of the sample rule. Figure 2 shows a block diagram of the prior art logical machine as executed on any general purpose, stored program-controlled digital computer upon which the method of the invention may be practiced. In order to enhance appreciation for the method of this invention, a description of the generation, function and use of the RETE comparison algorithm and,supporting data structures according to the prior art will be set out. Following this is a discussion of the invention utilizing the data structures. Lastly, a pseudo-code implementation and description is presented. Classic RETE Processing Reference should be made to previously cited Forgey, Brownston, and Miranker publications for implementation and additional details. The example discussed is a scheduling application involving parts and machines. Suppose there is a company that manufactures parts, that the company has two machines A and B, that there are two types of parts (P and Q) manufactured by the company, and that to complete a part of type P, machine A and then machine B must be used; while to complete a part of type Q, machine B and then machine A must be used. Suppose there is interest in a computer program that does production scheduling (deciding which parts should be routed to which machines) as the work progresses. Machines will be represented by records matching the following data declaration: Declare machine, type, state. Here "type" and "state" are attributes or fields in the machine record. "Type" may have the value "A" or the value "B" - representing the type of machine, and "state" may have the value "AVAILABLE" or "BUSY" - representing the current availability of the machine to accept a part to begin processing. In this example, data-driven production system techniques as well as terminology common to such systems are used. Specifically, many records will be able to co-exist, each satisfying the declaration above. The collection of all records satisfying any one such declaration will be called a "class". Each individual record will be called a "class member". The overall collection of all class members from all classes will be called "working memory". Each part (partially or fully complete) will be represented in the system by class members (records) satisfying the following declaration: Declare part, type, state, next-machine-needed. Here "type" can have the values "P" or "(Q)", while "state" must be "in-process", "waiting", or "complete", representing that a part must at any instant be either in the process of being machined, or waiting for a machine, or through with all needed machining. Suppose also that occasionally the company gets express orders that must be expedited. Such orders are of very high priority, and although the machining of a nonexpress part will not be interrupted to make the machine available for an express part, the company's policy is that while there is an express part in the system that will need a machine in the future, that machine will not start any nonexpress work - thus increasing the likelihood that the machine will be available or near becoming available when it is needed,for an express part. The declaration for a class member that would represent an express order might look like the following: Declare express-order, part-type, state, array-of-all-machines-needed, index-of-next-machine-needed. Using data-driven, forward-chaining, condition-action rules (or productions) to encode the routing of parts to machines, one rule in the computer program might look like the following when paraphrased in English: Sample-rule: When there is a machine - which shall be called M such that M is available and there is a part - which shall be called O such that the part is waiting for its next machining step and the next machining step required for O must be done on the machine M and there are no express parts that satisfy both the part is not complete, i.e., it is waiting or in-process and the part will need machine M before it is complete Then take the following actions: change the state of M to BUSY change the state of O to IN-PROCESS route the part O to the machine M for processing. The list of conditions between the 'When' and the 'Then' in the rule is called the antecedent or left-hand side (LHS) of the rule. The actions to be taken when the LHS is satisfied by some list of class members is called the consequent or right-hand side (RHS). There would be many other rules in such a system, and there would be a method for notifying the computer program whenever a machine completed its work on a part. Referring now to Figure 1, there is shown a RETE network for the LHS of the Sample-Rule. All arcs are directed and are considered as pointing down in the above picture. RETE processing starts at the top nodes (class anchors) and flows downward through the network. The nodes marked MACHINE, PART, and EXPRESS-ORDER are, respectively, anchors for queues of all machine, part, and express-order class members that exist in the program at that point in time. Changes to machine, part, or express-order class members can be of several kinds. New members can be created, representing new objects that the system should consider in routing parts to machines. Class members can be destroyed, representing the deletion of some object from participation in the system. For example, when a part is shipped to a purchaser, it might be natural to delete the class member representing that part from the system, as it should no longer be considered in any decisions about scheduling. Changes can also be made to existing class members. Different values are assigned to the attributes of the class member. This is done to represent changes in state, etc. Each time a change is made to working memory, that change is passed through the RETE network. In rough terms, the central nodes of the network fragment in Figure 1 correspond to tests that must be performed to determine what lists of class members satisfy all the conditions in the LHS of the Sample-Rule above. Class members arranged in lists are tested at a node to determine which lists satisfy the corresponding conditions and, for each node, summary information is retained in a queue based at that node. Usually, the retained information indicates which lists of class members passed the test associated with the node. The nodes labelled AVAILABLE?, WAITING?, and NOT-COMPLETE? are called alpha-nodes. Alpha-nodes correspond to tests that only mention a single class member. These nodes have only one incoming arc in the RETE network. The nodes labelled PART-NEEDS-MACHINE? and MACHINE-RESERVED-FOR-EXPRESS? are called beta-nodes. In this RETE network, the beta-nodes all have two incoming arcs and correspond to tests that determine whether a list of several class members satisfies a condition. For example, the node labelled PART-NEEDS-MACHINE? corresponds to a test that determines whether a pair of class members, the first a part and the second a machine, is such that the next-machine-needed field on the part class member is identical to the type field of the machine class member. In other words, when given a part and a machine, the node determines whether the part needs to go to that machine next. If a pair (or, in general, a list of any length) of class members satisfies a test, that fact is recorded in a control block which shall be called a Satisfaction Block (SB). The SB is placed in a queue that is anchored at the RETE node and becomes a record of what lists of objects satisfied the conditions associated with that node. The declaration for a simple SB might look like the following: Declare satisfaction-block, next-SB-in-queue, list-of-class-members; where the specified list-of-class-members field indicates a list of class members which together satisfy the test associated with the node. Lists of objects that satisfy the conditions associated with a node are candidates that need to be considered for satisfaction of later conditions. To make all the needed tests, while restricting consideration to just those lists of class members that have satisfied all earlier tests, a computational strategy is adopted that pushes tokens through the RETE network (top to bottom when the network is drawn, as in Figure 1). A token represents a change to some SB and reflects processing that must be done at a successor node. Tokens might satisfy the following declaration where the phrases following the '/*' on any line are comments: The declaration of a token includes a direction field which can be set to have one of two values, LEFT or RIGHT. It shall be assumed that all the RETE networks under discussion satisfy the following principles: A node with only one incoming arc is always viewed as having its one ancestor as a left ancestor. Where a node has two ancestors (a beta-node) and each ancestor is an alpha-node or is an anchor to a class, then one ancestor can be arbitrarily picked as the left ancestor, while the other will be the right. Where a beta-node has one ancestor that is a beta-node, then that ancestor shall be called the left ancestor, and the other ancestor (which in the example and in most implementations must be an anchor or alpha-node) becomes the right ancestor. The use and flow of tokens are illustrated by examining the processing that would take place in the sample RETE network of Figure 1. Suppose the process is started with an empty working memory. The RETE network of Figure 1 does not contain any satisfaction blocks. Further, suppose that during initialization of the execution of the program containing the rule above, the first machine class member is created, representing machine A. Since there is no work yet for the machine to do, the state field on the class member would be set to AVAILABLE. For each successor in the RETE network of the machine anchor node, a token would be created that contains a list of one class member - the new machine class member - in its list-of-class-members field. In the current example, there is only one node that is a successor of the machine anchor node. Thus, only one token would be generated, and it would point to the new machine for its list-of-class-members field. It would point to the AVAILABLE? node for its node field, and it would specify that the token arrived at the AVAILABLE? node from a LEFT ancestor. The token would contain MAKE in its CHANGE-TYPE field, indicating that the new class member was just created. Typically, if there are many tokens generated at once, they are placed on a stack and processed one at a time. In this simple case, only one token has been generated, so the processing for that one token would immediately proceed. (Typically, the token would be pushed on the token stack but soon thereafter would be popped off.) This processing done at the node would include executing the test associated with the node for the machine (class member) in the token. In this case, the newly created machine would be tested to see whether its state field listed it as available - which it would be. Thus, the test associated with the node would be passed and several actions would be taken. First, a Satisfaction Block would be created to record that the list of class members in the token passed the test. In this case, the new SB would point to the one new machine class member. Second, new tokens with change-type of MAKE would be generated, one for each successor of the AVAILABLE? node. Again, in this simple case, there is only one successor to the AVAILABLE? node so a token is stacked that specifies (a) machine A, (b) the PART-NEEDS-MACHINE? node, and (c) LEFT. Again, since there is only one node on the stack, processing for that node immediately proceeds. However, a list of a machine and a part is required to satisfy the PART-NEEDS-MACHINE? node, and there are no parts in existence yet. Thus, there is no more processing that can be done at this time, so control is returned back to the application. Suppose that the next action taken by the application during this initialization phase is to create the second machine; namely, machine B. As before, a token would be generated to cause testing at the AVAILABLE? node to determine whether this machine is available - which it would be. An SB is created so that there are now two SBs in the queue of the AVAILABLE? node. Since nothing more can be done, control (as before) returns to the application. Suppose that the application begins normal operation, and scheduling for the first part is to start. The application makes a part class member to represent the existence of the first part, a type P part that is waiting to be machined. Since type P parts must first be machined by machine A, the part's NEEDS-MACHINE field is set to A. Again, a token with change-type of MAKE is generated, indicating this change. The token would point to the new part, specify the WAITING? node, and the left direction. Processing of the token at the WAITING? node would discover that the part was indeed waiting, so an SB would be created and enqueued off the node and a new token would be created (and pushed onto the stack), indicating that this new part needs to be tested at node PART-NEEDS-MACHINE? where the arrival is from the right. With only one token on the stack, it is popped off and processing for that token proceeds immediately. A machine and a part are now needed to perform the test associated with the condition at a beta-node (the PART-NEEDS-MACHINE? node). There are two machines, A and B, which have previously arrived in tokens at the PART-NEEDS-MACHINE? node. This fact is recorded by the existence of two SBs enqueued off the AVAILABLE? node, which is the left ancestor of the current node. Thus, a loop is executed that walks through all SB blocks off the AVAILABLE? node. For each such SB; the test is performed at the PART-NEEDS-MACHINE? node to determine if the new part (of type P) needs the machine in the SB for its next machining step. If the test (PART-NEEDS-MACHINE?) fails, then no additional action is taken. If the test passes, then a new SB is created and enqueued off the PART-NEEDS-MACHINE? node. In this case, the test of the type P part with machine B fails, and the test with machine A passes. Thus, an SB with a list of class members comprised of machine A and the new type P part is enqueued off the PART-NEEDS-MACHINE? node, and a token representing this change to PART-NEEDS-MACHINES?'s SB queue is generated for the one successor of this node. The change made to PART-NEEDS-MACHINE?'s SB queue is the creation of a new SB. Thus, the new token is a MAKE token, and its list of class members includes the A machine and the new P part. PART-NEEDS-MACHINE? is an example of a positive beta-node. When a MAKE token arrives at such a node from one direction, the list of class members in the token is successively augmented with the list of class members from each SB off the predecessor node in the opposite direction. For each such SB, the test for the current node is performed using the augmented class member list. A passed test causes more tokens to be generated - one for each successor node. These new tokens are MAKE tokens that represent new candidates at successor nodes. This token is popped off the stack, having arrived at the MACHINE-RESERVED-FOR-EXPRESS? node from the left. This node is a negative beta-node. Unlike positive beta-nodes, the output of a negative beta-node is not an augmented token. A token arriving at a negative beta-node from the left includes a list of class members. This same list is either passed intact to all successor nodes, or it is stopped altogether. The list is passed on (in tokens) if no test passes at this node involving this class member list augmented by any one of the lists in the right ancestor's SB queue. This list is stopped and not passed to any successor if some test passes at this node when the arriving list is augmented in turn by each of the lists from the right ancestor's SB queue. Negative beta-nodes are often discussed in the following terms. Class member lists that arrive from the right will stop class member lists arriving from the left from passing through. Thus, a negative beta-node is much like a gate, where the things arriving from the right (according to what tests pass and fail) determine whether things arriving from the left will pass through. It should be noted that the arrival from the right of a DELETE token (indicating the deletion of an SB from the right ancestor's queue) at a negative beta-node can cause creation of a new SB at the negative beta-node and the corresponding generation of MAKE tokens for all successors of the negative beta-node. This happens when the arriving token indicates deletion of the only SB in the right ancestor's queue that was stopping some SB in the left ancestor's queue. Likewise, a MAKE token arriving from the right can stop an SB that is in the negative betanode's SB queue, and the MAKE token can cause the removal of an SB and the generation of DELETE tokens for all successors. In this example, the right ancestor of the MACHINE-RESERVED-FOR-EXPRESS? node is the NOT-COMPLETE? node. There are no express orders, so there can be no express orders with the part not complete. Therefore, there is nothing in NOT-COMPLETE?'s SB queue, and there is nothing that will stop any list of class members (in a token) arriving from the left from passing on through. Thus, arrival of a MAKE token from the left causes a new MAKE token to be generated and passed to the production node. There is only one production node shown. However, in a real RETE network, there is one production node for each rule. Whenever a token arrives at a production node, the arrival indicates some change to the conflict set for that rule. A MAKE token arriving at a production node indicates that a new instantiation should be created. A DELETE token arriving indicates that an existing instantiation should be eliminated. In this example, the arrival of the MAKE token at the production node indicates that a new instantiation should be made. Indeed, the pair of class members consisting of machine A and the one part of type P satisfies the conditions of the Sample-Rule based on all the class members that exist at this time. Thus, this one instantiation would normally become a candidate for firing with all other instantiations of other rules. If the rule did fire, then the action part of the rule would change the status of both the P type part and of machine A. Those changes, when pushed through the RETE network, would invalidate this one instantiation to Sample-Rule. Next, the RETE processing is examined in more detail for this case. As expressed before, this example merely illustrates the prior art. Suppose that the single instantiation is selected and Sample-Rule does fire. The first action taken is to change the status of machine A to BUSY. This change in machine A's state field with the classic RETE algorithm would be treated as a deletion of the old machine class member, followed by the creation of a new machine class member that is identical to the old one except for the altered value in the state field - the new value being BUSY. If the machine A class member is deleted, then before the class member is destroyed, a DELETE token is created for the AVAILABLE? node with the token pointing to the machine A class member. As always, the token is stacked with any other tokens. When popped and processed for the AVAILABLE? node, the test associated with the AVAILABLE? node is repeated to determine whether the class member passed or failed the test when it was created. If the current test passes (or fails), then the original test must have likewise passed (or failed, respectively) when the MAKE token was processed. If the earlier test did fail, there was no additional RETE processing generated by the token. No SB block was made, and no new tokens were spawned. Thus, there is nothing more that needs to be done now, as there are no 'descendant' references in the RETE network that must be removed to reflect deletion of the class member. If the earlier test passed, then there must be a record of the fact in the SB queue for the node. This is found by searching the queue, i.e., walking through all the SB blocks in the queue until one is found with a class member list that is identical to the class member list for the current token. This SB block is then dequeued and destroyed. Also, the fact that the test passed indicates that additional tokens were made when the machine A class member was created, and those tokens must be sought out and destroyed since they mention (typically, they point to) a class member that is being destroyed. A new token (a DELETE token) is created for each successor to the current node, and these are all placed on the stack for later processing. In summary, processing for a DELETE token exactly follows in the footsteps of an immediately preceding MAKE token. All tests are repeated. When a test is passed, there is an additional expense of searching for the right SB to excise. When a DELETE token arrives at a positive beta-node from one direction, it must be paired with all class member lists from SBs enqueued off the opposite ancestor. For each test that passes, the SB in the current block must be found and excised, and new tokens must be sent to successors. Processing of a DELETE token at a negative beta-node likewise undoes the work of a MAKE token. The computational expense of processing DELETE tokens is greater than that for processing MAKE tokens. It is frequently necessary to do an additional search of the SB queue at a node to find and excise the appropriate SBs. RETE Processing as Modified According to the Invention It was unexpectedly observed that if additional information links were maintained in an SB with its successor SBs, then those SBs that mention the same class member list as in the original SB could immediately be deleted if the original SB were deleted. The process for creating new SBs is largely unchanged, other than the responsibility for saving the additional information. However, in the resulting structure, a class member is the root of a tree of all SBs (and instantiations) that mention the class member, the class member itself being treated like an SB enqueued off an anchor node. Thus, deletion of all SBs (and instantiations) that mention the class member is largely a matter of walking a tree and excising all SBs encountered. The algorithm can be implemented so that no test is ever repeated. This reduction in the total number of tests to execute RETE processing is especially important when tests are slow to execute; for example, when class members are stored on a slower storage medium, and accessing class members for testing is computationally expensive. Also, with the invention described herein, no searching of SB queues is required to find the SBs to be excised. This can markedly speed up execution of RETE processing, depending on the length of the SB queues. In this invention, an SB might have the following declaration: If node N is a successor of node M in a RETE network (i.e., there is an arc from M to N), and if X is an SB queued off M, and Y is an SB queued off N so that the class member list in X is an initial or terminal segment of the list in Y - so that the deletion of X would cause (using classic RETE processing techniques) tokens to be generated that would result in the deletion of Y, then say that Y is an immediate descendant or a successor of X, and say that X is an immediate ancestor or a predecessor of Y. If M is a left (or right) predecessor of N, then say that X is a left (or right, respectively) predecessor of Y, and Y is a left (or right, respectively) successor of X. If N is a beta-node, there may be many SBs enqueued off N that are all immediate descendants of the same SB enqueued off M. However, a given SB can have at most one left and at most one right immediate ancestor. Fields similar to those added to the SB declaration in order to arrive at the improved SB declaration (but excluding ancestor pointers) can be associated with each class member. Thus, in all particulars, a class member itself can be treated like an SB insofar as handling creations and deletions. Likewise, fields similar to those in an improved SB (excluding those that point to descendants) can be added to instantiations, as instantiations record what lists of class members pass through production nodes and they are similar to and treated like SBs. The sibling fields in an improved satisfaction block are used to form a doubly-linked (usually circular) list of all SBs that have a common immediate ancestor. The left and right descendant fields point respectively to any left/right descendant. The left and right ancestor fields are each either set to point to the appropriate ancestor SB if such exists, or are set to a null value if no such ancestor SB exists. When a MAKE token is being processed at either an alpha or a positive beta-node and a test is passed, all ancestor SBs are known and all the fields in an improved SB can be easily set to the correct values. When a delete of a class member is being processed, a depth-first walk of the descendant SB tree can be executed, and each SB can be deleted from the doubly-linked queue of sibling SBs. If that deletion exhausts the sibling queue, then the predecessor SB's descendant field can be set to null. If the predecessor SB's descendant field points to the deleted SB and the sibling queue is not depleted by the deletion, then the ancestor SB's descendant field can be set to point to another descendant SB. Once an SB is no longer referred to (pointed to) by any other SB, it can safely be destroyed. Care must be taken to eliminate all left and all right descendants of any SB that is going to be deleted. Processing at negative beta-nodes can be done in several ways. However, it is not possible to simply follow an immediate translation of the above-described techniques into the world of negative beta-nodes. The problem in using the classic approach to processing at negative beta-nodes is that a complete record is not maintained of the result of every test performed at a negative beta-node. With the processing for positive beta-nodes, as described above, every passing test is recorded in an SB. A failing test is recorded by the nonexistence of an SB. In contrast, the SB queue for a negative beta-node (with the classic algorithm as earlier described) is a subset of the SB queue for the left ancestor node. An SB is created and kept (off the negative beta-node) exactly in those cases where no SB in the right ancestor's queue passes the test when paired with the SB from the left ancestor. Thus, the results of all tests involving SBs that appear in the negative beta-node's SB queue are known; namely, all such tests failed. However, if an SB from a left ancestor does not reappear in the negative beta-node's SB queue, then there is no information retained about which SB or SBs from the right ancestor node stopped the left arriving SB by passing the test when the left and right SBs were coupled together. Since negative beta-nodes tend to be significantly less common than positive beta-nodes, it is possible to treat them using an approach similar to that in the classic RETE processing while still obtaining marked speed improvements for most applications. Using this approach, the elimination of an SB from a node that is a right ancestor of a negative beta-node would trigger the spawning of DELETE tokens that would be passed on to the negative beta-node and processed in the classical way. It is also possible to keep additional information at each negative beta-node about which SBs from the right stop which SBs arriving from the left. This can be done, for instance, by keeping a list (the standard SB queue for the negative beta-node) of all left arriving SBs that are not stopped, and also by keeping another list of all left arriving SBs that are stopped and keeping with each entry in that list another list of all the right arriving SBs that stop the associated left arriving SBs. Blocks in this new list might be called Stopping SB Blocks, or SSBBs. In this case, fields similar to those in the improved-satisfaction-blocks (outlined above) can be used to link SSBBs to their ancestor SBs. While the details of the algorithm are different for processing at positive and negative beta-nodes, the general approach is similar to that outlined above for the processing done at alpha and positive beta-nodes. Pseudo-code Implementation and Comment One use of the RETE algorithm and of the method of this invention is in the implementation of the pattern matching for data-driven production systems. The control cycle for such a system is shown with reference to Figure 2. In that environment, when a program is first initialized, the RETE network for the set of rules,in the application must be built. However, the invention relates to modifications to a RETE network once built, and the following discussion assumes that one has already been compiled. The fundamental loop or cycle of execution for a data-driven production system is the Match-Resolve-Act cycle. This is illustrated in Figure 2 and set out in the pseudo-code outline as follows: Some initial conflict set (set of all instantiations) is given; The heart of the RETE processing is thus in the routines MAKE, DELETE, and MODIFY. These routines are notified of changes to working memory, and they push those changes through the RETE network and thereby compute the corresponding changes to the conflict set. An outline of these routines follows. As derived from the illustrative example in the previous section, the data structures that correspond to the following declarations shall be used: The declaration of a token appears as follows: It should be noted that tokens will only be used for MAKE-type actions in the outlined implementation; thus, there is no change-type field in this declaration of a token. The declaration of a Satisfaction Block (SB) should be formatted as: The declaration of a Stopped SB Block (SSBB) consists of: MAKE is passed a pointer to a just created class member and an identifier of the class. It computes the corresponding changes to the conflict set. DELETE is passed a pointer to the class member that is to be deleted. DELETE computes the corresponding changes to the conflict set. Thereafter, it frees up the storage being used by the class member. MODIFY is passed a pointer to the class member that is to be changed. It is also passed a description of the field that is to change, and it is passed the new value that is to be assigned to the field. MODIFY will delete the old copy of the class member insofar as the RETE algorithm is concerned. Then it makes the indicated change to the class member, and does the RETE processing to reflect the creation of a new class member replacing the old one, but with the one field changed. Thus, it can be seen that the heart of the processing takes place in the two routines MAKE-PROCESS and DELETE-PROCESS. These routines are outlined in pseudo-code. First, consider the MAKE-PROCESS routine. As mentioned before, the input to MAKE-PROCESS is the token stack, and MAKE-PROCESS is driven by the token stack. When that stack is empty, then MAKE-PROCESS's work is complete and it returns to the caller. There are several references in the above to the utility routine SET-SB-AND-SEND-ON. This routine accepts a node, a list of pointers to class members, a pointer to a left ancestor (possibly null), and a pointer to a right ancestor (possibly null). It does two things. First, it creates an SB from the list of pointers to class members, and it enqueues that SB off the passed node. Second, it loops through each successor node in the RETE network of the passed node, and for each it creates and pushes a token. A pseudo-code description follows: It remains to outline the DELETE-PROCESS routine. This is called to delete all references in the RETE network to an SB, an SSBB, an instantiation, or a class member - which, for the purposes of the RETE algorithm, is a special kind of SB. DELETE-PROCESS is called and passed either a pointer to a class member or a pointer to an SB. If a class member is passed, then that class member is deleted from its class queue. If an SB is passed, then that SB is deleted from the SB queue in which it resided - while the integrity of all SB queues is maintained. Whether an SB or a class member is passed, DELETE-PROCESS proceeds to call itself recursively in order to eliminate all left and right descendant SBs. Thinking of all the descendants of an SB as forming a tree, the recursive invocation of DELETE-PROCESS actually walks that tree in a depth-first manner and just before leaving a node in that tree (which is an SB), that SB is excised from all queues in which it resides and the SB is destroyed (i.e., the block of storage is freed): It will be further understood by those skilled in this art that various changes in form and detail may be made therein without departing from the scope of the claims.

Claims: A method for optimizing a cyclic, rule-based, data object sensitive production system, said system including means for storing data objects and rules, and means cooperating with the storage means for executing a control cycle, each rule having pattern indication and action specifying parts thereof, comprising the cyclic steps of: (a) identifying an executable subset of rules by matching the pattern parts of the rules to those data objects in the storage means modified or created during a preceding cycle; (b) selecting a rule from the identified rules; and (c) executing the action prescribed by the selected rule; wherein identification step (a) further comprises: (a1) compiling a data flowgraph of the condition elements of the pattern portion of the rule being matched, said flowgraph being formed from entry nodes and join nodes, the entry nodes representing counterpart individual condition elements and join nodes representing compound condition elements arranged in a pattern-determined associative manner; (a2) applying those data objects created or modified in a preceding cycle to said flowgraph, and at each node (entry nodes and join nodes): maintaining a list of instantiations satisfying the match conditions expressed at that node, passing tokens to descendant nodes, and characterised by the steps of : maintaining pointers to all ancestor nodes through which the token for each object passed, and responsive to indication of object change, traversing said pointers as a path for avoiding those flowgraph node pattern/ object matchings redundant between a previously matched object and an object currently being processed. The method according to claim 1, wherein the data flowgraph is a RETE network and wherein the patterndirected associative manner for arranging the join nodes is left associative. The method according to claim 1, wherein the indication of object change includes creation, modification, or deletion of an object occasioned during the preceding cycle and applied to the flowgraph during the current cycle, and further wherein the deletion of an object includes deletion of instantiations maintained at the nodes visited by any traverse of the pointers.

Inventors: LOEB DAVID JEROME, MILLIKEN KEITH ROBERT, LOEB, DAVID JEROME, MILLIKEN, KEITH ROBERT
Applicants: IBM, INTERNATIONAL BUSINESS MACHINES CORPORATION
Application year: 1988
Main classification: G06F9
IPC codes: G06F17, G06F9, G06N5