in:Proceedings International Conference on Building and Sharingof Very-Large Scale Knowledge Bases '93 (KBKS '93),
Tokyo, Japan, December 1{4, 1993, pp. 157{166

Towards the Knowledgeable Community
Toyoaki Nishida and Hideaki Takeda
Graduate School of Information Science
Nara Institute of Science and Technology (NAIST)
8916-5, Takayama, Ikoma, Nara 630-01, Japannishida@is.aist-nara.ac.jp and takeda@is.aist-nara.ac.jp
Abstract

The Knowledgeable Community is a framework of knowledge sharing and reuse basedon a multi-agent architecture. In this paper,
we describe the scope and goal of the Knowledgeable Community project and discuss how
sharing of knowledge is achieved in the Knowledgeable Community. In particular, we focus on the organizational structure that facilitates mediation between those agents requesting for a service and those providing
the service. We describe several techniquesof mediation. Preliminary implementation of
the Knowledgeable Community is in progress.

1 Introduction

Recently, importance of infrastructure has become oneof critical issues of AI. Previously, two rather separate
approaches have been taken to this end. One is to takea rather straightforward approach to building a large
scale knowledge base system. The Cyc project [?, ?]is the most prominent one in this direction. Emphasis
is placed on comprehensive collection of commonsense
knowledge in a formal knowledge representation language based on rst order logic. In contrast, the otherapproach is to develop a framework of knowledge sharing and reuse. Development of common languages andontologies among interacting agents are major issues in
the latter approach. The DARPA knowledge sharing
initiative [?, ?] is the most representative approach inthis direction.
These two approaches are considered to be addressing complementary aspects. It is worth seeking a wayof integrating the two.
One way of integration could be regarding a large
scale knowledge base as one of shared knowledge bases.
However, this is not a good way to go. First, this would
result in undesirable duplication of ontological de nitions. Second, it is hard to understand and maintaina single very complex knowledge base.

In this paper, we take an alternative approach andexplore a way of distributing large scale knowledge ina society of autonomous agents. The resulting framework, called the Knowledgeable Community, is characterized by ontology oriented agent organization andmulti-stage mediation mechanism.
Ontology oriented agent organization means thatagents are associated with ontologies, structural de nitions of concepts. A mediation mechanism is provided
for mediating agents looking for a service with those
providing the service by combining concept similarityand de nition. In order to derive even in the course ofdevelopment, we propose building up the Knowledgeable Community in a bottom up manner.
Currently, implementation of the rst version of theKnowledgeable Community is in progress using platforms developed by the DARPA knowledge sharing
and reuse initiative [?, ?].
The rest of this paper is organized as follows. First,
we discuss the importance of what we call informationcentered AI as opposed to processing-centered AI. Second, we introduce the framework of the Knowledgeable
Community, and feature design principles behind it.
Third, we describe technical issues related to ontologyoriented agent organization and multi-stage mediation
mechanism. Fourth, we discuss methodological issues
on developing the Knowledgeable Community. Finally,
we compare our approach with related work.

2 Information-centered AI and
Knowledge Sharing

Traditional textbooks characterize Arti cial Intelligence as a study of human intelligence and its implementation by arti cial information processors. Typicalapproaches of AI are to isolate basic mechanisms of human information processing and design artifacts thatcan mimic it. This approach can be called processingcentered AI, as the primary focus is to understand anddesign a mechanism of information processing. Researchers tend to overly constrain themselves to issuesof medium diculty which are hard enough to con-

Interaction
with Human

Creation ofInformation
Creative
Understanding
Thinking
Intention
Cooperative Work Analogy

Presentation

Machine Learning
Cognitive Science

Pattern
Recognition
Monitoring

Discovery
Human Interface

DialogMulti-Media

Knowledge Sharing and Reuse
representation

Systems Theory

Interaction with
the Real World

Systematization

Induction

Information
Signal Analysis

Model

PredicateCalculus

Information
and Control
Planning

Information
TheorySemantics
Situation

Knowledge
Engineering

Linguistics

Knowledge
Acquisition

SearchConstraint
Complexity Programming
TheoryKnowledge based
Systems

Case-based Reasoning
Qualitative Reasoning

Commonsense Reasoning
Information Model
Analysis andModeling ofInformation

Nonmonotonic Reasoning

Truth Maintenance

Problem Solving

Figure 1: Information-centered AI; the central issue is information; six dimensions are de ned to explore information; core issues are placed near the center of the picture.
vince people of importance of a new theory but which
are not too hard to tackle.
Unfortunately, this approach runs into trouble.
First, the end users cannot expect an endorsement thatcon rms achievement of their goal. This is because
AI researchers hardly make serious e orts on developing useful systems and instead they prefer hacking
sophisticated information processing mechanisms even
if they are not useful. Thus, valuable information may
be pushed out of systems until it is made computerunderstandable. Second and more importantly, the exploitation of domain knowledge is not expected eventhough it is of primary importance to domain experts,
for comprehensive analysis and development of knowledge base are regarded as a secondary issue and does
not attract much attention. As a result, conventional
AI systems often fail to keep end users whose evaluation and critical comments are invaluable to obtaininsights of the problem analysis and improvement.
An alternative view, which may be called
information-centered AI, is to think of AI as a discipline of modeling, understanding engineering, and creating the contents and structure of information, rather

than information processing. Figure ?? shows researchareas of information-centered AI. Analysis and modeling of information are regarded as a foundation of
information-centered AI. Problem solving is concernedwith theoretical issues of making use of informationand deriving new information. Knowledge engineering
focuses on engineering information, involving such issues as sharing and reuse of information, knowledge
acquisition, organization of knowledge. Creation of
information explores issues such as creative thinking
or discovery. Interaction with humans handles relationship between information space and humans. Interaction with the physical world involves modeling,planning, monitoring, and understanding the physicalworld.
There are several advantages of informationcentered AI. Unlike processing-centered AI, unstructured information only understandable to humans isregarded equally valuable and is subject to intensive
study even if it is not automatically processed. The
primary goal of this approach is not to fully automate the process, but to solve problems regardless ofthe means; it does not matter whether problems aresolved automatically. Attention is focused on identifying critical problems and seeking the best way of solving them, either by full or partial computational support. Second, this view would give a better characterization of part of conventional and future AI research
whose essence has been to understand the nature andstructure of knowledge and information. Indeed, recent progress of research on large scale accumulation ofknowledge and knowledge sharing can be captured in
the context of information-centered AI, for the emphasis is on the content of knowledge rather than problem
solving methods based on knowledge.
Pragmatically, even information-centered AI should
accompany implementation; AI is an empirical science and AI theories have to be implemented for empirical evaluation. The way information-centered AI
systems are implemented may be quite di erent fromconventional AI systems. The purpose of implementing conventional AI systems has been mainly to test
the idea. Certain aspect of intelligence is identi ed,
isolated, and implemented as a rather independent
system. In contrast, the purpose of implementing
information-centered AI systems is to explore the information space. Full automatic problem solving is notalways necessary. Rather, man-machine interactive
problem solving is sometimes valuable either for responding immediate requirements or for gathering insights into human information processing for automation in future.
Towards this end, the following characteristics are
considered to be critical to information-centered AI
systems:
1. allowing heterogeneous approaches
2. being useful, even in the course of development
3. allowing many people to participate in development.
Information-centered AI can be regarded as a successor of existing technologies, in particular information ltering such information lens [?], and information resource nding [?] such as World-Wide Web [?].
Information-centered AI aims at establishing exploiting more structured and organized information spacethat allows intelligent information processing using AI
technologies. Recent work in large scale knowledge
bases [?, ?], knowledge sharing [?], and computer-aided
thinking [?] are good examples in this direction.

3 The Framework of the Knowledgeable
Community

The Knowledgeable Community is a framework ofknowledge sharing and reuse based on a multi-agent
architecture. Agents in the Knowledgeable Community are units of knowledge or ability of reasoning
and problem-solving. Agents are spatially distributedand communicate with each other by exchanging messages. The multi-agent architecture re ects the current
and future technologies of interconnected computers
through high speed telecommunication networks at the
hardware level and object-oriented concurrent softwarearchitecture at the software level, enabling large-scale
integration of computer softwares implemented using
varieties of technologies.
The Knowledgeable Community is characterized by
the following features:
1. the Knowledgeable Community facilitates both information and knowledge sharing;
2. organizational structure of agents is given in which
two special types of agents, facilitators and mediators, help to nd agents which can provide desired
service;
3. the Knowledgeable Community handles knowledge
media [?] that are meaningful both to humans
and to computers;
4. a testbed is given to demonstrate and evaluate theKnowledgeable Community.
3.1 Design Policies of the Knowledgeable
Community
As participants of the Knowledgeable Community,
agents are expected to cooperate with each other tosolve given problems. We pose requirements on agentsand their communication in order to realize such cooperation. General requirements are as follows:(1) Ontological relations among agents In the
Knowledgeable Community, we assume ontological relations among agents to enable sharing of knowledge,
reasoning, and problem-solving. For example, domain
knowledge agents, which manage knowledge in certain
domains, are often structured hierarchically in accordance with hierarchical structure of domains.
(2) Publication of agents' abilities Cooperation
can be achieved only if participants know each others'
abilities. We request each agent to provide descriptionof its ability. This description is used in two ways in
the Knowledgeable Community. One is for agents topublish their descriptions of abilities, and the other isto reply queries about their abilities.
Furthermore, agents' abilities can be understood indirectly by ontological relations among agents.
(3) Autonomy of agents' organization Since
there is no central organization in the Knowledgeable
Community, each agent should have an ability to takecare of its own actions including starting and ending.
If there are ontological relations among agents, someagents instead can have abilities to take care of behaviors of related agents, such as starting, ending or evencreation of agents.
(4) Public communication languages and ontologies In the Knowledgeable Community, various

languages are used for communication, ranging frommachine understandable data to natural languages,
and various contents are told, varying from numericalinformation in engineering to literary sentences.
Communication in the Knowledgeable Community
should be understandable not only by concerned agents
but also by other agents to some extent so that the facilitators,

clientrequestfacilitator
publish / subscribe

mediator
interest related to request

interest

interest

mediator

mediator
publish / subscribe

facilitator
publishpublish / subscribe

facilitator
publishfacilitator
serverserverserverpublishserverserverserverFigure 2: Organization of agents in the Knowledgeable
Community
message may be interpreted by other agents and forwarded to an adequate agent whose existence has notbeen known to the sender.
Local languages and ontologies are allowed but
should be reported to some agents in order to makemessages meaningful to other agents.
(5) Communication with unspeci ed addressee

The purpose of communication among agents is notcommunication itself, but achievement of problem
solving through communication. Since agents in the
Knowledgeable Community are merely sources of knowledge or reasoning, the central concern is what canbe done about a problem rather than which agent
can deal with it. Thus, the Knowledgeable Community
should allow not only point-to-point (agent-to-agent)communiciation but communication with unspeci ed
addressee which can reduce a priori knowledge aboutother agents.
(6) Allowing various communication modes In
the Knowledgeable Community, communication varies
from simple communications like question-answer pairs
to long dialogues. In particular, concentrated communication may be needed to achieve cooperate work
among agents. The Knowledgeable Community should
provide not only sparse communication but also tightly
coupled communication among agents.
3.2

Organization of Agents

We base the organizational structure of the Knowledgeable Community on the federation architecture [?, ?].
Figure ?? shows how agents in the Knowledgeable
Community are organized.
We have classi ed agents into several types according to their roles in the agent organization (see Table ??). Roughly, the following agent types are distinguished:

like those introduced in [?, ?],which manage low level communication processing among agents.
 mediators which bridge between those agents requesting for a service and those providing the service.
 ordinary agents which provide facts about theworld or derive new information by various kindsof computation or problem solving method (Table ??). Agents may be further subcategorized
into instance agents and class agents depending
on how they are associated with ontology.
All message transmission is made through facilitators.
When the addressee is speci ed, the facilitator will
deliver the message to the agent speci ed as the addressee. Otherwise, the facilitator will forward themessage to mediators, which will propose the recipient or its candidates based on knowledge about otheragents and ontologies.
Mediators play functions similar to facilitators in thesense that they help agents interoperate. The di erence is that mediators are more like ordinary agents
except for the contents of information they provide;
ordinary agents provide information about the world,
while mediators know what other agents do. Facilitators are more like an operating system in computer
systems. They are public and handling low level communication services, and do not use heuristic knowledge. In contrast, mediators provide private information using heuristics of various kinds.
Mediators express their interests by subscribing tofacilitators. On the other hand, agents publish theirability to facilitators when registered to the Knowledgeable Community. Facilitators keep and occasionally match the two kinds of publicized information.
Each ordinary agent acts either as a client or aserver depending on the status of information processing. When a task is given, one of ordinary agents willserve as a client and others will be potential servers. As
the process of information processing proceeds, tasks
may be decomposed into subtasks and di erent agents
may in turn serve as a client.
3.3

Knowledge Media as Common
Knowledge Representation in the

Knowledgeable Community
The Knowledgeable Community incorporates knowledge
media which are meaningful both to humans and tocomputers. Some information that agents produce or
receive is formally described so that computer programs can handle it, while other information only
makes sense to humans who are able to interpret it.
The former is inevitable for enjoying the bene t of
information processing capabilities of computer software. Agent communication language, knowledge interchange format, and ontology should be shared sothat computer software may interoperate. We employ
KQML [?], KIF [?], and Ontolingua [?]. On the other
hand, incorporating the latter type of information into
systems pragmatically makes sense, for it is immedi-Table 1: Types of agents

Facilitators(message handling agents)

Mediators(agent for messages with unspeci ed addressee)(message processing agents)

Ordinary agents(agents for messages with speci c addressee)

ately useful to end users and is valuable as sample data
for formalization.
Essential part of an agent may be either a human or
a computer software. In the former case, a human interface is provided to translate computer-oriented formal message into an informal representation so thatend users can read it. In the latter, an application
program interface is provided to interface application
software with network of agents.

Table 2: Varieties of functions of ordinary agents
1. Information service; the following are typical functions displayed as question-answer (Q-A) or requestanswer (R-A) pairs:(a) (Providing facts)
Q: What is the coecient of linear expansion

of copper at 20 C?
A: 16:5

2

10

06

.

(b) (Providing cases)
R: Show me typical examples ofip-

op cir-cuits.
A: (typicalip-

ops are shown)(c) (Providing generic facts)
Q: How much heat energy is radiated from object surfaces?
A: E = kT

4, where E : energy radiated, k: con-stant, T : the absolute temperature of the surface.
(d) (Providing ontological information)
Q: What are the subclasses of animals?
A: birds, mammals, . . . .
2. Inference engine services such as:
databasemanagement

languageprocessors,

systems,

belieftheoremprovers,

management

systems,expert system shells, learning algorithms, and so on.
3. Computation and problem solving service such as:
numerical and symbolic computation, data analysis, diagnosis, scheduling, design, and so on; theoretically, this can be regarded as a product of an
information source and an inference engine.

Ontology servers
Concept associators
Information servers
Inference severs
Computation servers
Problem solvers...

KC-Kansai as a testbed of the Knowledgeable
Community
Use of good testbed is expected to highly speed up thedevelopment process. Currently, we are using travel arrangement problems as a testbed and are implementing
KC-Kansai that is a multi-agent system for arranging
travel in the Kansai area in Japan.
Figure ?? shows the conceptual framework of KCKansai. Spatially distributed agents communicate
with each other through various communication media ranging from low-speed serial communication line
to high-speed communication channel such as B-ISDN.
Some agents provide information about a local community such as local events, local institutes, transportation, restaurants, accommodation, amusement, and soon. Others provide information processing service suchas arranging meeting, travel, and so on.
This domain is interesting as a testbed, for:1. a rich inventory of information source is involved
and no single existing information source covers
the information range;
2. each information source is managed individuallyand provides rather private information;
3. quality and characteristics of information generated by each information source are rather incoherent;
4. information sources are dynamically changing.
Several interesting applications of KC-Kansai areforeseen including travel arrangement, active yellow
page, and virtual tour. Figure ?? illustrates how travel
arrangement is made in KC-Kansai. In this context,
KC-Kansai will help client agents in two ways. First,
it will gure out a travel plan from a given speci cation. Second, it will monitor the execution process ofa plan and deal with replanning if necessary. Personalcommunication media will be used for this purpose.
Active yellow page helps to nd one or more service
to achieve a given goal. A key question here is designing an organization of server agents so that a given
set of requirements of clients can be achieved under agiven set of constraints.
Virtual tour provides end users with interactive
movie of local area (Figure ??). It will be made available by incorporating multi-media technique.

3.4

Kyotoother local areasin the world

Tokyo
Osaka
Kobe

Nara

: client: serverFigure 3: KC-Kansai as a testbedt

Kyoto
amusements
restaurantnight view

research
centeruniversity

Kobe conference hall

templesouvenirmuseum university
ryokanuniversity

Osaka
department
hospitaltransportation
store
research centertransportation
university
park
hotel
restaurant
historicalspot: client

museumNara: server: image server

Figure 4: KC-Kansai for travel arrangement
The Knowledgeable Community will be developed
through multiple phases. At early stages of development, agents in the Knowledgeable Community will bemostly hand-coded.
At later stages, agents will be able to learn fromothers or reason about the organizational structure.
Self-organization techniques might be applied to allow
the organization of agents to evolve autonomously.
Currently, the development of the rst stage withtravel arrangement being the subject domain is inprogress, which is a major focus of the rest of the paper.

Figure 5: KC-Kansai for virtual tour

4 The Organization of Agents forMediation
The more agents are in the Knowledgeable Community,
the harder to nd one or more agent that satis es agiven goal. An adequate means should be provided forfacilitating mediation between those agents requesting
for a service and those providing the service. Towards
this end, we propose ontology oriented agent organization and multi-stage mediation mechanism.

facilitator

forwarding the message

facilitator

forwarding the message

mediator

mediatorbroadcasting

message withunspecified addressee

reply

Agent Amessage withunspecified addressee

forwarding

Agent A

Agent 1

reply
reply

choose apropriate
sub-class agents

Agent 2class hierarchy

Agent 1

forwarding

Agent 1
Agent 2Agent 3
forwarding

Figure 6: Mediation by broadcasting
4.1

Agent 4

Ontology-Oriented Agent Organization

Figure 7: Mediation by class hierarchy

Frequently, a system of concept de nitions is called an
ontology1 [?]. An ontology plays an essential role inrepresenting the world. We assume that agents areorganized along with an ontology.
For example, the railway agent represents a concept
which is a specialization of the concept that the tracagent represent. We call the former a sub-class agent
and the latter a class agent. Class agents know theirsub-class agents, and sub-class agents know their classagents. There are also instance agents which represent
individual facts in the real world. For example, therailway agent has some instance agents, the railwaycompany-A agent for instance.
Agents may be created not only statically but also
dynamically. For example, the travel-planner agent
can have more than one instance agent temporarily
to process multiple requests.
Furthermore, there are special agents called ontology servers which maintain all relations in an ontology.
Not all concepts in an ontology are associated with anagent. An ontology server provides information about
concepts and relations.

choose appropriate sub-class agents and forward themessage to them (see Figure ??). If there are morethan one layer in the concept hierarchy, the message
can be passed through multiple class agents.

4.2

Mediation by ontology

Multi-stage Mediation

The Knowledgeable Community allows agents to send amessage without specifying the addressee. Such messages are sent to special agents called mediators, which
decide which agent is appropriate to receive them andforward the messages to it.
Mediation by broadcasting

The simplest mediation is to ask all or some agents
whether they can respond the message. A mediatorcollects replies and decides appropriate agents to send
the message (see Figure ??).
Mediation by class hierarchy

If there is a class hierarchy of agents, mediators forward the message to some class agent. Since a classagent knows abilities of its sub-class agents, it can
1

There may be more than one ontology.

tologies represent diDierent on-erent characterizations of the world.

facilitator

forwarding the message

consulting
mediator

Ontology A.

chooseappropriate agentsmessage withunspecified addressee

Agent A
forwarding
the message

Agent 3Agent 2Agent 1Figure 8: Mediation with ontology server

The role of a mediator is to nd appropriate agents
related to the given message, by asking relations
among agents and those among concepts to an ontology server (see Figure ??). If there exists an agent
associated with the concept in the message, an ontology server returns the name of the agent. Otherwise, itwill seek agents which may be related to the concepts
in the given message.
Mediation by heuristics

A rigorous class hierarchy is not powerful enoughto cope with vaguely speci ed requests. Such requests
will be handled by mediation by heuristics that calls
for such agents as concept associators or case-based mediation. Concept associators use concept spaces whichgive distances between concepts based on various criterion, allowing to retrieve concepts relevant to a given
set of concepts. Case-based mediation is a mechanismof reusing previous experiences of mediation in whichfacilitator

facilitator

forwarding the message

Concept
associatorconsulting

Ordinary agents

ontology
agent
mediator

mediator
message withunspecified addressee

Case-based
reasonerchooseappropriate agentsconcept
associator

Domain knowledge agents

Agent A

class hierarchy
traffic Agent

forwarding
the message

Agent 3Agent 2
hotel Agentbus Agent
railway Agent

Agent 1
Hotel-A Agent

Figure 9: Mediation by heuristics

Hotel-B Agent

local buscompany A.

local railway
company A.

regional railway
company A.

Figure 11: Examples of KC-Kansai agentsfacilitator

forwarding the message

choose an appropriate agent
mediator

advertising agents’ abilities

message withunspecified addressee

Agent A

forwarding
the message

Agent 1

Agent 2Agent 1Figure 10: Mediation by descriptions of agents' abilities
nontrivial reasoning is performed, either automaticallyor manually, either to nd the target. (Figure ??).
Mediation by descriptions of agents' abilities

A mediator interprets descriptions of agents' abilities to decide which agents are appropriate to the message (see Figure ??). This type of mediation can be
combined with mediation by heuristics to process collected descriptions of agents' abilities.
4.3

Examples of communication andmediation

Consider a system of agents shown in KC-Kansai ??.
We show how varieties of messages can be handled below. Although there may be more than one facilitator
or mediator, we simply describe as if there there were
only one facilitator and mediator, respectively.
(1) \Tell me your phone number" ) the Hotel-A
agent
This is almost the simplest case, for the addressee is
explicitly given and the facilitator will deliver the message directly to the Hotel-A agent, an agent associatedwith Hotel-A.
(2) \Tell me the phone number of Hotel-A" ) ?
To handle this message, the mediator asks the ontology server for how features of Hotle-A can be accessed.
In this case, since Hotel-A is realized as the Hotel-A

agent, the ontology server replies with the name of theagent, allowing the mediator to deliver the message tothe Hotel-A agent.
(3) \Tell me hotels where room price is within 10,000
Yen" ) ?
The mediator forwards this message to the hotel
agent, a class agent associated with the class hotel.
Then, the hotel agent broadcasts the message to its
instance agents each of which represents an individual
hotel, and collects the responses that meet the given
constraint.
(4) \Tell me how long it will take from Station-A to
Station-B" ) ?
We have assumed that Station-A and Station-B are
sub-classes of class station, but neither of the three
is associated with an instance agent. In this case, theontology server will seek indirect ways to obtain the information. For example, the ontology agent may knowthat information about stations is accessible from therailway agent. Then, the mediator will create a newmessage, send it to the railway agent, and transformthe response so that it may be adequate as a responseto the original message.
(5) \Propose a one-day trip which involves discussing
about research and education, appreciating Japaneseculture, and some refreshing time" ) ?
Since this is a vaguely speci ed request as far as visiting locations are concerned, mediation by heuristics
will be needed by the travel planner agent to gure out
a set of locations to visit. In the context of KC-Kansai,
relevant concept spaces are such as those given in Figure ??. A concept associator may be invoked to nda minimal cluster of concepts that may maximally ta given set of key words: \discussing about researchand education", \appreciating Japanese culture", and\refreshing". In this case, the concept associator may
nd that pairs of visiting locations, NAIST and Nara

activity spacebusinesspublishingacedemic
educationbook store

meetinglunch relaxing

research

shopping

diningaccommodation

amusement

appreciating
Japanese culture

refreshingsightseeing

transportation

geographic space
Kyoto
Univ.

ASTEM
Kobe
Univ.
Kobe

Kyoto

Nara park
Keihanna
Todaiji
Plaza
NAIST ATR Daibutsu

Osaka
Univ.
Osaka
Osaka
castleWakakusa
Mtn
Nara

Yamanobe
trail

OBP

Asuka

Figure 12: Concept spaces relevant to KC-Kansaipark, best cover a given request, as follows:
discussing
aboutvisiting

research andversityuni-

education

appreciating
Japanese

NAISTappreciating
Japanese

cultureNara park

culturerelaxing
refreshingand returns them as a response. Now the travel planner
agent comes to have a rather concrete plan as far as
the visiting locations are concerned and will elaborate
the plan using less heuristic mediation methods.

5 Building up the Knowledgeable
Community

Development of the Knowledgeable Community is along-term goal and is expected to take a long period.
In order to keep the Knowledgeable Community usefuleven at intermediate stages, we develop agents in the
Knowledgeable Community in a bottom-up, inductive
manner. It is our belief that building up the concept structure is a long-term trial-and-error process
based on rich data, rather than top-down, deductive
process [?].
Agents at the rst stage only provide unstructured
information such as natural language texts, voices, orimages which may only make sense to humans. These
kinds of information is easy to obtain and useful tohumans, even though agents cannot understand thecontents.
As the stage of development proceeds, the more sophisticated techniques will be used to improve agentsso that they can respond with more structured information, allowing new agents (or agencies [?]) to be

implemented which may derive new information fromstructured information.
Currently, the building up process is manually performed. We could use induction algorithms to induce
generic rules from data. This is still semi-manual, forevaluation and incorporation of hypotheses are left forhumans. In future, this process might be further exploited by introducing some kind of self-organization
facilities.

6 Related Work

The Knowledgeable Community can be regarded as a
successor of several technologies.
Compared with multi-media technology, the Knowledgeable Community is distinguished from existing
technologies by the degree information and knowledge
sources are structured and organized. The current
multi-media technology assumes that humans play anessential role in interpreting and manipulating information. Although this approach brings about direct
implications to the end users, it does not decrease intellectual information processing loads. In contrast,
the Knowledgeable Community aims at knowledge media that are understandable and manipulatable bothby humans and computers. AI techniques will be used
to structuring and organizing information so that computer software may handle it, decreasing the various
load required to make use of potential information.
The agent-oriented technology is similar to theobject-oriented technology. However, there are two differences. First, agents are active in the sense that they
understand and react the environments autonomously,
while objects are passive entities that only respond incoming messages. Second, instance objects are almost
empty, just a set of individual values in object-oriented
systems, while instance objects are crucial in the agentoriented technology. In the agent-oriented technology,
instance agents are not simply a set of individual values but also individual entities that may persist andevolve over time.
We have chosen the platforms developed by the
DARPA knowledge sharing and reuse initiative as abase. The net progress in the Knowledgeable Community is the the use of knowledge media and powerful
mediation mechanism.

7 Conclusion

In this paper, we have proposed information-centered
AI as opposed to processing-oriented AI, and presented
a framework of the Knowledgeable Community. We
have focused on the organization of agents for mediation and described two methods: ontology-oriented
agent organization and powerful mediation mechanism.

References[1] Tim Berners-Lee, Robert Cailliau, Jean-Francois
Gro , and Bernd Pollermann. World-wide web:The information universe. Electronic Networking:Research, Applications and Policy, 1(2), 1992.

[2] Mark R. Cutkosky, Robert S. Engelmore,
Richard E. Fikes, Michael R. Genesereth,Thoas R. Gruber, William S. Mark, Jay M.
Tenenbaum, and Jay C. Weber. PACT: An experiment in integrating concurrent engineeringsystems. IEEE Computer, January 1993:28{38,1993.
[3] Peter B. Danzig, Katia Obraczka, and Shih-Hao
Li. Internet resource discovery services. IEEE
Computer, September 1993:8{22, 1993.
[4] T. Finin, J. Weber, G. Wiederhold, M. Genesereth, R. Fritzson, D. McKay, J. McGuire,
P. Pelavin, S. Shapiro, and C. Beck. Speci cation of the KQML agent-communication language. Technical Report EIT TR 92-04, Enterprise Integration Technologies, 1992. (Updated
July 1993).
[5] Michael R. Genesereth and Richard Fikes. Knowledge Interchange Format version 3.0 reference
manual. Technical Report Logic-90-4, Computer
Science Department, Stanford University, 1990.
[6] T. R. Gruber. Ontolingua: A mechanism to support portable ontologies. Technical Report KSL
91-66, Stanford University, Knowledge Systems
Laboratory, 1992.
[7] Thomas R. Gruber. Ontolingua: A mechanism tosupport portable ontologies Version 3.0. Technical report, Knowledge Systems Laboratory, Stanford University, 1992.
[8] R. V. Guha and D. B. Lenat. Cyc: A midterm
report. AI magazine, pages 32{59, Fall 1990.
[9] Koichi Hori. A system for aiding creative concept
formation. IEEE Transactions on Systems, Man,
and Cybernetics, 24(5), 1994. (to appear).
[10] Douglas B. Lenat and R. V. Guha. Building Large
Knowledge-based Systems. Addison-Wesley, 1989.
[11] Thomas
W.
Malone, Kenneth R. Grant, Franklyn A. Turbak,
Stephen A. Brost, and Michael D. Cohen. Intelligent information-sharing systems. Communications of ACM, 30(5):390{402, 1987.
[12] Marvin Minsky. Society of Minds. Simon &Schuster, Inc., 1985.
[13] Robert Neches, Richard Fikes, Tim Finin,
Thomas Gruber, Ramesh Patil, Ted Senator, andWilliam R. Swartout. Enabling technology forknowledge sharing. AI Magazine, 12(3):36{56,1991.
[14] R. S. Patil, R. E. Fikes, P. F. Patel-Schneider,D. McKay, T. Finin, T. R. Gruber, and R. Neches.
The DARPA knowledge sharing e ort: Progress
report. In Charles Rich, Bernhard Nebel, andWilliam Swartout, editors, Principles of Knowledge Representation and Reasoning: Proceedingsof the Third International Conference. Morgan

Kaufmann, 1992.

[15] Mark Ste k. The next knowledge medium. AI
Magazine, 7(1):34{46, 1986.