Mediation in the Knowledgeable Community
Toyoaki Nishida, Hideaki Takeda, and Kenji Iino
Graduate School of Information Science
Nara Institute of Science and Technology (NAIST)
8916-5, Takayama, Ikoma, Nara 630-01, Japan

Abstract
The Knowledgeable Community is a framework ofknowledge sharing and reuse based on a multi-agent
architecture. In this paper, we describe the scopeand goal of the Knowledgeable Community project,present the organizational structure that facilitates
mediation between those agents requesting for a service and those providing the service, and illustrate atechnique of ontology-based mediation by example.

KBSinterface

network interface

1 Introduction

mediator
Social Amenities

Large scale knowledge base is indispensable to put
AI theories to work in the real world. Previously,
two rather separate approaches have been taken tothis end. One is to take a rather straightforward
approach to building a large scale knowledge basesystem, such as Cyc [6, 4]. The other end is to develop a framework of knowledge sharing and reuse by
developing common languages and ontologies amonginteracting [8, 11]. Obviously, these two approaches
are complementary to each other. It is bene cial tointegrate the two and take a step toward large scale
knowledge sharing.
The purpose of the Knowledgeable Community
project [9, 10] is to develop an arti cial communityof cooperating agents for large scale knowledge sharing. Currently, we are focusing on (a) developmentof an organizational structure that helps agents to interact easily, (b) design of knowledge media which ismeaningful both to humans and computer softwares.
In what follows, we will give an overview of theKnowledgeable Community project, describe the organizational structure of agents, and present a mechanism for mediating agents based on the ontologicalstructure embedded in the community of agents. We
will illustrate how the mechanism for ontology works
using an example.

common protocolcommon language
common ontology
common social convention

ARPA
KSE
Inisiative

Federation Architecture
facilitatorcomputer networks (LAN + Internet)

InternetFigure 1: The framework of the Knowledgeable Community
face with natural or/and arti cial problem solver.
Agents communicate with each other using common
protocol, language, ontology, and social convention.
Social amenities of various kinds help agents to cooperate and maintain coherence of the agent community.
Roughly, agents can be classi ed into three categories. The rst category involves those that provide
domain, problem solving, and task knowledge. The
second consists of facilities that play functions similar
to operating systems. The rest consists of mediatorsthat help agents to nd proper information servers.
We are building the Knowledgeable Community on
top of the federation architecture proposed by theARPA knowledge sharing e ort. We choose a distributed information system as a testbed. The system, called KC-Kansai, is designed to provide various information about the Kansai area1 in Japan.
Each agent provides information about institutions,transportations, accommodations, shops, restau-2 The Framework of the Knowledgeable Community

The Knowledgeable Community is an arti cial community of cooperating agents (Figure 1). Each agent
consists of a communication software and an inter-1

1

Roughly, it consists of Kyoto, Nara, Osaka, and Kobe.

client
requestconcept associator
activity spacefacilitatorbusinesspublishingacedemic
education

publish / subscribe

book store

meeting

researchinterest

mediatormediator

accomodationinterest related to request

interest

interest

mediator

mediator

publish / subscribeinterest

ASTEM
Kobe
Univ.
Kobe

publish / subscribe

Kyoto
Univ.

Kyoto

Nara park

Osaka
Univ.

Keihanna
Todaiji
Plaza
NAIST ATR Daibutsu

Osaka
Osaka
castleWakakusa
Mtn
Nara

Yamanobe
trail

OBP

facilitator

publish / subscribetransportation

geography space

mediator
facilitatoramusement
refrleshing appreciating
Japanese culture
lunch relaxing
shopping sightseeing
diningontology server

Asuka

PTRANSpublishpublishfacilitator
serverserverinstituions

serveruniversities

publishserverserverserver

request travel
itineraryshortagency
longreserach distance medium distanceinstitutes
distance

private universities

shuttleservice

public universities
NaraLocal

Figure 2: Organization of agents in the Knowledgeable Community

NAIST

rants, points of interest, and so on. One of themost typical tasks of KC-Kansai is travel assistancein which agents cooperatively help travellers gureout itinerary or execute the plan.

KyotoUniv.

transportation

railways

air planes
ANAJR

Kintetsu

The Univ.
TokyoFigure 3: Ontology servers and concept associatorsfor mediation
Major source of knowledge for mediation is ontology. Ontological information is provided by two
kinds of agents (Figure 3). Ontology servers provide
logical information about concepts based on de nitions and mutual relations of concepts represented in
description logic. In particular, as agents are associated with conceptual structures kept by ontology
servers, it is possible to access agents by following
conceptual relations. The other kind of agents arecalled concept associators, which correlate concepts
in terms of conceptual distance.2
In the rest of this section, we will illustrate how an
ontology server works. Suppose an agent sends out
an message asking for agents that provide information about going from Tokyo to NAIST. The message
will be sent to the physical-transfer agent which inturn will call for an ontology server to gure out what
Tokyo means and NAIST means. It will also ask formediators to propose a means of physical-transfer.
The mediator will make use of ontology server to determine the type of physical-transfer and return thenames of agents that provide information about primary means of transportation.
Now, let us illustrate a more sophisticated example. Consider Figure 4 which shows a part of theontology for KC-Kansai. Each box in solid line denotes a class concept and one in a grey line an agent
associated with a concept. Suppose the following request is given:

3 Mediation in the Knowledgeable Community

We would like to incorporate into the Knowledgeable
Community as much social amenities as possible, todecrease the overhead imposed at the development
and maintenance stages. Suppose one is to write anagent for travel arrangement. Probably, s/he mayrst try to gure out what information is neededto accomplish the task and what kind of assistanceother agents can provide. Once one has found a set ofservers, one may try to understand the speci cationof each server to incorporate o ered services. We
would like to organize agents in the Knowledgeable
Community so that one need not know which agent
provides what information service in what form.
Two kinds of agents are introduced for these purposes. Facilitators take care of low level communication issues (Figure 2). For instance, they monitor execution of information servicing agents andsolve low-level timing problems. If a message froma client does not specify the recipient, a facilitator
will forward it to mediators, which will suggest a setof possible recipients based on its knowledge about
agents. Although facilitators are public and do notuse heuristics to nd a potential recipients, mediators are private in the sense that they are based onrather subjective heuristics. Mediators are like ordinary agents except the di erence in the type ofinformation they provide.

2 As for more detailed description of concept associators,
the reader is referred to [10].

2Example

GEOGRAPHICAL-THING
PLACE
name GEOGRAPHICAL-NAME
display-point LOCATION-POINT
feature
AREA
border setof BOUNDARY-LINE
PREFECTURE

Associated Agent
LOCATION-POINT
x-location
y-location

BOUNDARY-LINEboundary-lines listof LOCATION-POINT
boundary-kindPOINTlocation LOCATION-POINTCITY

CLASS NAME
class-subclass relation
CLASS NAME
slot nameslot nameclass of slot value

COURSElines listof LOCATION-POINT
RIVER

LAKE

BUILDING
telephoneaddress

PARK

TRAFFIC-LINE
ROAD

Park-Agent

BUS-LINE
Traffic-AgentSTATION
traffic-facility

RAILROAD

RailroadAgent

NARAKOTU-BUS-LINE
KINTETSU-RAILROAD

BUS-STOP
RailroadAgent

JR-RAILROADRAILROAD-STATION

JR-RailroadAgent

Kintetsu-RailroadAgent

ACCOMMODATIONKINTETSU-STATION
JR-STATION

Kintetsu-RailroadAgent

JR-RailroadAgent
PLACE
Temple-AgentTEMPLE

SIGHT-SEEING-PLACE

SPOT
LAKE
PARK

TEMPLE
Hotel-Agentrequired-time SHORT-TIME-SPEC
fee
HOTEL
open-time
single-room-number
close-timetwin-room-number
Sightseeingsingle-room-fee
Agent
Temple-Agenttwin-room-fee
UNIVERSITY
check-in-time
check-out-timenearest-station
parking-limit
DORMITORY
morning-fee
UNIVERSITY-HALL
dinner-fee
Park-Agent

Figure 4: Part of ontology for travel plan
Give me the phone numbers for accommodation near Nara.
The request is represented in KQML and KIF, as
shown in Figure 5a. Given the message from a client,a facilitator will forward it to a mediator, which inturn will send a message to an ontology server, as
shown in Figure 5b, asking for a list of agents which
can answer the speci ed inquiry. The ontology server
analyzes the message it received. It rst looks at theconcept de nition of accommodation and its superand sub- classes, ending up with a message as shownin Figure 5c.

(a) Problem representation in KQML and KIF(broker-all :content(ask-one :content (and (accommodation ?x)(near ?x nara)(telephone ?x ?tel)):aspect ?tel :language KIF)
:reply-with q1)(b) Message from mediator to an ontology server(recommend-all :content(ask-one :content (and (accommodation ?x)(near ?x nara)(telephone ?x ?tel)):aspect ?tel :language KIF)
:reply-with m1)

4 Related Work
The Knowledgeable Community is being built on topof several technologies. Compared with network information resource utilization technology, such as information lens [7] and information resource nding[2, 1], the Knowledgeable Community is distinguishedfrom existing technologies by the degree information and knowledge sources are structured and organized. The current technology for network information resource management and utilization assumes
that humans play an essential role in interpreting andmanipulating information. Although this approach(c) response from the ontology server(reply :content ("temple" "hotel"):in-reply-to m1)

Figure 5: Messages used for mediation3

brings about direct implications to the end users, itdoes not decrease intellectual information processing loads. In contrast, the Knowledgeable Community
aims at knowledge media that are understandable
and manipulatable both by 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 tomake use of potential information. The approach inthis direction involves concepts such as softbots [3]and knowbots [5].
The agent-oriented technology is similar to theobject-oriented technology. However, there are two
di erences. First, agents are active in the sensethat they understand and react the environments autonomously, while objects are passive entities that
only respond incoming messages. Second, instanceobjects are almost empty in the object-oriented technology; they are just a set of individual values inobject-oriented systems, while instance agents are
crucial in the agent-oriented technology. In theagent-oriented technology, instance agents are notsimply a set of individual values but also individualentities that may persist and evolve over time.
We have chosen the platforms developed by theARPA knowledge sharing and reuse initiative [8, 11]
as a base. The net progress in the Knowledgeable
Community is the the use of knowledge media andpowerful mediation mechanism.
Problems related to mediating information producers and consumers are surveyed and discussed indetail in [12]. The method presented in this paper
is concerned with more conceptual issues and is intended to be a proposal from the AI side.

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5 Conclusion
In this paper, we have presented a framework of theKnowledgeable Community. In particular, we havefocused on ontology-oriented agents organization andillustrated how it helps mediation.
There are lots of interesting research issues left
for further work. A most promising direction may
be to incorporate ecological and biological mechanisms to allow Knowledgeable Community to evolve
autonomously.

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