Implicit Culture for Multi-agent Interaction Support

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Implicit Culture for Multi-agent Interaction
Support

• Paolo Giorgini
• Department of Mathematics
• University of Trento
• pgiorgini_at_science.unitn.it
• Joint work with
• Enrico Blanzieri, Paolo Massa and Sabrina Recla

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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

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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

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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

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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 eÍ P ÈO
• scene as the pair ltB,Agt, where B Í e, and A Í A
• situation as lta,s,tgt, where aÎ P and s is a scene
• executed situated action as the action executed
  in given situation.
• Fe deterministic function that describes the
  evolution of the environment.

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Environment
Fe
c
a
e
b
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Environment
c
a
st1
st1
e
b
st1
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Implicit Culture basic definitions (2)

• 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 lta,s,tgt E(ha,t lta,s,tgt)
• Cultural constraint theory for a group GÍ P, 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

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Implicit Culture basic definitions (3)

• Implicit Culture
• Relation between G e G such that the expected
  situated actions of G are cultural action for G
• Implicit Culture phenomenon
• G and G are in implicit culture relation

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the idea
G
G
st
a
a
st
g
st
b
c
b
e

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

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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.

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SICS architecture
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SICS architecture
G
G
a
st1
st1
c
b
st1
e
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The eCulture Brokering System

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

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Multi-agent Architecture
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User interface
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Agents interaction

Brk
Br2
Br1
DB
Wr1
PA1
DB
Wr2


PAn
DF
ARB
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Agents interaction

Brk
Br2
Br1
DB
Wr1
PA1
2) The PA accepts or refuse the proposed Broker
it sends to the accepted Broker the request of
the user.
DB
Wr2


PAn
DF
ARB
6) The Broker send the answer to the PA that
sends it to the user
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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
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DF and Implicit Culture
Agents observed Personal Agents (G G)

• Observed Actions
• ltrequests,x,s,tgt The PA x request to the DF, at
  time t, a Broker for
• getting information about the century s
• ltaccepts,x,y,s,tgt at time t, PA x accepts the
  Broker y, proposed by
• the DF, about the century s
• ltrefuses,x,y,s,tgt at time t, PA x refuses the
  Broker y, proposed by the DF, about the
  century s

Proposed Scenes Brokers
Cultural Constraint Theory ltrequest,x,s,? timegt
?ltaccepts,x,? Broker,sgt
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Example
PA1 asks for a Broker for the VI century.
Observation stored by the SICS
Br3
Br2
Br1
Br0
1) find the cultural actions
Accept(VI)
Refuse(IV)
PA0
Accept(XVII)
Accept(XIII)
2) find the scenes
Refuse(IV)
PA1
Refuse(XVII)
Accept(XI)
Accept(II)
1. Find the predictive agents
Accept(XVII)
Refuse(XVI)
Accept(VI)
Refuse(IV)
PA2
Refuse(XVII)
Accept(XVII)
2. Select the similar agents
Refuse(IV)
PA3
Accept(XI)
3. Propose the scene with the maximum
probability of facilitation
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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

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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

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more

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