Implicit Culture for Multi-agent
Interaction Support
Paolo Giorgini
Department of Mathematics
University of Trento
pgiorgini@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
eP O
e
• scene as the pair <B,A>, where B  , and A A
• situation as <a,s,t>, where a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
a
g
st
s”t
c
a
b
s’t
e
b
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Environment
c
a
s”t+1
st+1
b
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s’t+1
e
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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 <a,s,t>; E(ha,t |<a,s,t>)
• Cultural constraint theory for a group G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’
a
a
st
g
b s’t
s”t
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
Observer stores in a data
Inductive
Module
S
S0
base the situated executed
actions of the agents of G.
Inductive Module that
using the data of the DB and
the a priori theory So, induces
a cultural constraint theory S.
G
G’
a
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a
st
s”t
g
Composer proposes to a
group G’ a set of scenes such
that the expected situated
actions satisfies S.
DB
Observer
Composer
b s’t
c
b
e
12
SICS: architecture
Observer stores in a data
S0
Inductive
Module
S
base the situated execute
actions of the agents of G.
Inductive Module that
using the data of the DB and
the a priori theory So, it
induces a cultural constraint
theory S.
G
G’
a
st+1
s”t+1
Composer proposes to a
group G’ a set of scenes such
that the expected situated
actions satisfies S.
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DB
Observer
Composer
b
e
c
s’t+1
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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
Personal Agent (PA) permits a
user to access the system
Br1
Br2 … Brk
Wrapper (Wr) is the interface
Wr1
PA1
…
Wr2
DB
Wrh
DF
ARB
Broker (Br) builds an “answer” with
some grade of specialization in an area.
Agent Resource Broker (ARB)
…
PAn
DB
between the system and a database.
DB
gives information about the external
available resources
Directory Facilitator (DF) knows
the agents of the system and their
services
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User interface
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Agents interaction
Wr1
DB
Wr2
DB
PAn
…
Br2 … Brk
…
Br1
1) a user, by the PA, requests information
PA1
Wrh
DF
ARB
DB
about a century; the PA asks the DF which
Broker can satisfy the request.
2) The PA accepts or refuse the proposed
Broker; it sends to the accepted Broker
the request of the user.
3) the Broker asks the ARB which are the
external resources that can be useful.
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.
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Agents interaction
Wr1
DB
Wr2
DB
PAn
…
Br2
…
Br1
1) a user, by the PA, requests information
… Brk
PA1
Wrh
DF
ARB
DB
about a century; the PA asks the DF which
Broker can satisfy the request.
2) The PA accepts or refuse the proposed
Broker; it sends to the accepted Broker
the request of the user.
3) the Broker asks the ARB which are the
external resources that can be useful.
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
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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:
 <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
Proposed Scenes: Brokers
Cultural Constraint Theory:
<request,x,s,
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time
> <accepts,x,
Broker
,s>
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Example
PA1 asks for a Broker for the VI century.
Observation stored by the SICS
Br0
Br1
Br2
Br3
1) find the cultural actions
Refuse(IV)
Accept(XIII)
Accept(XVII)
Accept(VI)
Accepts(VI)
PA1
Refuse(IV)
Accept(II)
Accept(XVII)
Accept(XI)
Refuse(XVII)
PA2
Refuse(IV)
Accept(XVII)
Accept(VI)
Refuse(XVII)
Refuse(IV)
Accept(XI)
PA0
PA3
2) find the scenes
1. Find the predictive agents
Refuse(XVI)
PA0, PA2
2. Select the similar agents
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PA1 is more similar to PA2 than
to PA0
3. Propose the scene with the
maximum probability of facilitation
Br1
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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
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