Giorgini - CoopIS 2001 Implicit Culture for Multi-agent Interaction Support Paolo Giorgini...

Giorgini - CoopIS 2001

Implicit Culture for Multi-agent Interaction Support

Paolo Giorgini

Department of Mathematics University of Trento

[email protected]

Joint work with:

Enrico Blanzieri, Paolo Massa and Sabrina Recla

2Giorgini - CoopIS 2001

Outline

• Motivations

• Implicit Culture

• Systems for Implicit Culture Support (SICS)– A SICS for Multi-agent interaction support

• The eCulture Brokering System

• Conclusion and future work


Motivations

• Interaction among agents is crucial for the efficiency of MAS

– new agents enter into the system without the necessary knowledge and skills

– new agents are not able to learn from the others’ behavior

– it is not possible to define and represent a priori the relevant knowledge the agents need for the interaction


Motivations

• In order to improve its behavior, a new agent should act consistently with the knowledge and the behaviors (culture) of the other agents.

• We propose a way for supporting multi-agent interaction based on the idea of

Implicit Culture [Blanzieri, Giorgini and Giunchiglia: 2000]


Implicit Culture: basic definitions (1)

Let P be a set of agents, O a set of objects, A a set of actions. We define:

• environment PO• scene as the pair <B,A>, where B , and A A

• situation as <a,,t>, where aP and is a scene

• executed situated action as the action executed in given situation.

• F : deterministic function that describes the evolution of the environment.


Environment

a c

b

t”t

’t

F


Environment

a c

b

t+1 ”t+1

’t+1



• Random variable ha,t that describes the action that the

agent a executes at the time t • expected action as the expected value of ha,t , E(ha,t )

• situated expected action as the expected value of ha,t

given a situation <a,,t>; E(ha,t |<a,,t>)

• Cultural constraint theory for a group GP, as a theory on the situated expected actions of the agents of G

• Cultural action w.r.t. G, as an executed action that satisfies a cultural constraint theory for G



Implicit CultureRelation between G e G’ such that the expected situated

actions of G’ are cultural action for G

Implicit Culture phenomenonG and G’ are in implicit culture relation


… the idea

the agents of G’ perform actions that agents of G would perform in the same situations

a

t

b

’t c

”t

G’G


Systems for Implicit Culture Support (SICS)

Goal: establish an implicit culture phenomenon

– acquisition of cultural constraint theory for G– proposing to G’ scenes such that the expected

situated actions satisfy the cultural constraint theory for G.


a

t

b

’t c

”t

G’G

SICS: architecture

Observer DB

Observer stores in a data base the situated executed actions of the agents of G.

InductiveModule

Inductive Module that using the data of the DB and the a priori theory o, induces a cultural constraint theory

Composer

Composer proposes to a group G’ a set of scenes such that the expected situated actions satisfies


a

bc

G’G

SICS: architecture

Observer DB

Observer stores in a data base the situated execute actions of the agents of G.

InductiveModule

Inductive Module that using the data of the DB and the a priori theory o, it induces a cultural constraint theory

Composer

Composer proposes to a group G’ a set of scenes such that the expected situated actions satisfies

t+1

’t+1

”t+1


The eCulture Brokering System

• The system is the result of collaboration between University of Trento and ITC-irst.

• GoalGoal: Permit to a citizen to access, via web, to the information about cultural goods collected in the (Trentino) museums.

– The user demands the system information about cultural goods related to a particular epoch.

– The system queries the databases of the museums and answers.


Multi-agent Architecture

Directory Facilitator (DF) knows the agents of the system and their services

DF

Agent Resource Broker (ARB) gives information about the external available resources

ARB

Broker (Br) builds an “answer” withsome grade of specialization in an area.

BrkBr2Br1 … Wrapper (Wr) is the interface between the system and a database.

DB

DB

Wr1

Wr2

DBWrh

…

Personal Agent (PA) permits a user to access the system

PAn

PA1

…


User interface


Agents interaction

DF ARB

BrkBr2Br1 …

DB

DB

Wr1

Wr2

DBWrh

…PAn

PA1

…

1) a user, by the PA, requests information about a century; the PA asks the DF which Broker can satisfy the request.

3) the Broker asks the ARB which are the external resources that can be useful.

2) The PA accepts or refuse the proposed Broker; it sends to the accepted Broker the request of the user.

4) The Broker asks the DF which Wrappers are able to interface the resources.

5) The Broker queries the Wrappers and build the answer for the user.


Agents interaction

DF ARB

BrkBr2Br1 …

DB

DB

Wr1

Wr2

DBWrh

…PAn

PA1

…

1) a user, by the PA, requests information about a century; the PA asks the DF which Broker can satisfy the request.

3) the Broker asks the ARB which are the external resources that can be useful.

2) The PA accepts or refuse the proposed Broker; it sends to the accepted Broker the request of the user.

4) The Broker asks the DF which Wrappers are able to interface the resources.

5) The Broker queries the Wrappers and build the answer for the user.

6) The Broker send the answer to the PA that sends it to the user


DF and Implicit Culture

• The DF provides a “yellow pages” service• The Brokers are specialized in a different thematic areas

The SICS is used to support the activity of the DF with the goal of suggesting to each PA the most suitable Broker


DF and Implicit Culture

Agents observed: Personal Agents (G = G’)

Cultural Constraint Theory:

<request,x,s, time> <accepts,x, Broker,s>

Proposed Scenes: Brokers

Observed Actions: <requests,x,s,t> : The PA x request to the DF, at time t, a Broker for

getting information about the century s <accepts,x,y,s,t> : at time t, PA x accepts the Broker y, proposed by

the DF, about the century s <refuses,x,y,s,t> : at time t, PA x refuses the Broker y, proposed by

the DF, about the century s


Example

Accept(VI)Refuse(IV)

Br3Br2Br1Br0

PA0

Refuse(IV)PA1

Refuse(XVI)Accept(VI)Refuse(IV)

PA2

Refuse(IV)

PA3

Accept(XVII)

Accept(XI)

Accept(II)

Accept(XVII)

Refuse(XVII)

Refuse(XVII)

Observation stored by the SICS

Accept(XVII)Accept(XIII)

Accept(XI)

PA1 asks for a Broker for the VI century.

1) find the cultural actionsAccepts(VI)

2) find the scenes

3. Propose the scene with the maximum probability of facilitation

1. Find the predictive agents

2. Select the similar agents

PA0, PA2

PA1 is more similar to PA2

than to PA0

Br1


The eCulture Brokering System

• Developed using JACK Intelligent Agents, a commercial agent-oriented development environment built on top of and fully integrated with Java

• It follows FIPA (Foundation for Intelligent Physical

Agents) specifications for DF and ARB

• Databeses: Oracle and Microsoft Access


Conclusions• We have presented

– the idea of Implicit Culture and how to use it for supporting Multi-agent interaction

– eCulture Brokering System

• Implicit Culture Support allows us to improve the agents interaction without need to equip the agents with additional capabilities

• Future work:– Extend the use of SICS to other agents, in particular to the

ARB (Agent Resource Broker)– Implementing the inductive module for inducing cultural

constraint theories for different groups of agents


… more

• http://www.science.unitn.it/~pgiorgio/ic

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