The Exchange of Retrieval Knowledge about Services between Agents Mirjam Minor Mike Wernicke.

The Exchange of Retrieval Knowledge about Services

between Agents

Mirjam MinorMike Wernicke

Can computer systems make experiences

M. Minor, M. Wernicke German Workshop on Experience Management, April 2003

Outline

• Brief introduction of Kühnel’s assistant agents

• The role of TCBR to improve this agents

• Merging of Case Retrieval Nets

• Implementation issues

• Contribution to Experience Management


Kühnel’s assistant agents

• Personal assistant agents that distribute how-to knowledge: – executing services on demand of user

(service: utility program like print a file)– planning with sub-services if necessary– collaborating with other agents by

• remote execution

• exchange of services


A service description


User requests

• How to find appropriate services to a user request?– Navigate? - only for a small service library– String matching? - bad recall– Textual Case-Based Reasoning!

• The service descriptions are the cases.• The retrieval is performed by means of a

Case Retrieval Net (CRN).


Textual CBR

• Case: AV pairs + textual sections, mapped on sets of information entities by means of dictionaries, e.g. print;prints;printing

• Similarity function: computes the best matching cases of the case base concerning a query, uses local similarity values, e.g.

Query Case

ji

e esimCaseQuerySIM

i j

ee ).,(),(


Retrieval of Texts in a CRN

Opens Yahoo in an Internet Explorer

Starting Yahoo with Netscape

__YAHOO__

__OPEN__

__START__

__BROWSER__

__IE__ __NETSCAPE__

Case

Information Entities

Query


Central vs. personal CRNs

• Central approach in the manner of yellow pages:– Easier to manage, e.g. by a central administrator

– Rather a client-server than an autonomous agent model

– Inconsistencies and ambiguities due to different sub-domains

• Personal approach with initially delivered and individually extendable dictionaries:– Individual service libraries with individual CRNs

– Communication of services and parts of the own CRN


Merging Case Retrieval Nets

__IEa__

__IEi=j’__

__IEy__

__IEz__

__IEx__

C1 C2C1‘

Merged set of case nodes

Merged set of Information Entities



Types of possible ‘syntactic’ conflicts:– Non-identical duplicates of information

entities:the new IE takes all strings of both originals

– Duplicates of similarity relationships with different weights: choose the maximum degree

– Differently assigned strings, e.g.__print__; print; printer__printer__; printer; print device



__IEa__

__IEi=j’__

__IEy__

__IEz__

__IEx__

C1 C2C1‘

Case nodes

Information Entities

new similarity arcs?

new relevance

arcs



New relevance arcs: rebuild the CRN

Missing similarity links, e.g.:

Still an open research issue!

__ALTAVISTA____GOOGLE__

__SEARCH ENGINE__


Implementation

• Java package ‘TCBR’ that implements a case-based retrieval for texts with a Case Retrieval Net and dictionaries

• Java package ‘AgentTCBRShell’ to extend the original description of a service with IEs and local similarity relationships

• The prototype is implemented and runs as a Java application.


System Architecture

RMI

Searching in

Planning

ExecutingAgent A

personal service library

personal dictionaries

CRN Searching in

Planning

ExecutingAgent B

personal service library

personal dictionaries

CRN


Contribution to Experience Management

• Agents that exchange how-to knowledge in form of services

• Transfer of background knowledge

• Integration of received knowledge with the own knowledge repository

• Individual ‘treasury of experiences’ that develops during the agent’s work history

Agents are able to make experiences and to share them!

Questions?

Long-Lived CBR SystemsLifecycle models and their impact on CBR systems

regarding usage, structure, and maintenance

ICCBR-03 Workshop in Trondheim, NorwayOrganizers: M. Nick, M. MinorImportant Dates:

April 9, 2003 submission deadlineJune 24, 2003 workshop

http://www.iccbr.org/2003/index-ws2.html


Applications of TCBR

• ExperienceBook: www.informatik.hu-berlin.de/~cbr-ws/EXP_BOOK

• Support of system administrators and common computer users during daily work

• Cases: problem description with solution, e.g. how to print an ASCII file with UNIX

• Lesson learnt by long-term use: knowledge management to keep it up to date


The Knowledge Creation Spiral of Nonaka/Takeuchi:

Socialisation Externalisation

Internalisation Combination

implicit

explicit

explicitimplicit

from

to


Experience Management (EM)

• deals with the management of experiential knowledge within an organization

• main problems:– How to make experiential knowledge explicit?– How to find (retrieve) it when it is needed? – Support of EM processes by tools, – Integration with the usual business processes


State-of-the-art EM Systems

• CBR systems: experiential knowledge in cases, user-triggered retrieval processes

• Ontology-based systems: Ontology = hierarchy of concepts, user browses through domain ontology

• Communities of practise: regular meetings of experts, user participates or deals with documentations of the meetings


EM Systems in Long-Term Use

• EM systems learn:– New experiential knowledge which is stored for

the users– Internal knowledge to execute or improve the

systems’ core tasks

• During usage, EM systems collect an own treasury of experiences!


Knowledge Sorts of the Treasury

• Knowledge contents (in CBR: the cases)• Own immanent experiences of the system:

– Work history of knowledge pieces– Indexing and other background knowledge– Valuating knowledge– Social knowledge– Knowledge about users

The Exchange of Retrieval Knowledge about Services between Agents Mirjam Minor Mike Wernicke.

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