Intelligent Agents for norm-regulated MAS Alberto Sardinha Ricardo Gralhoz José Viterbo Karin...

Intelligent Agents for norm-regulated MAS

Alberto Sardinha

Ricardo Gralhoz

José Viterbo

Karin Breitman

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Outline

• Introduction to Intelligent Agents – Alberto Sardinha

• Ontologies – Alberto Sardinha

• Conflict Resolution – Alberto Sardinha

• ‘Intelligent’ norm violation – Ricardo Gralhoz

• Policy-Based Context-Aware Applications for Mobile Computing – José Viterbo

Introduction to Intelligent Agents

Alberto Sardinha

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Intelligent Agents

• Why should I use Intelligent Agents?

– To solve complex problems

– Dynamic and Nondeterministic environments

– Learn automatically from experience

– Open Systems

SARDINHA, J.A.R.P. A Method and a Framework for Building Intelligent Agents. ScD Thesis, Dep. de Informática, PUC-Rio, 2005.

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Intelligent Agents Architectures

RUSSELL, S.; NORVIG, P.. Artificial Intelligence. Prentice Hall, ISBN 0-13-103805-2, 1995.

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WOOLDRIDGE, M.. Intelligent Agents. G. Weiss (ed.), MULTIAGENT SYSTEMS: A MODERN APPROACH TO DISTRIBUTED ARTIFICIAL INTELLIGENCE.The MIT Press, Second printing, 2000.

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SARDINHA, J.; GARCIA, A.; MILIDIÚ, R.; LUCENA, C..The Agent Learning Pattern. SugarLoafPLoP'04, Fortaleza, Brazil, August 2004.

Ontologies for norm-regulated MAS

Alberto Sardinha

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Ontologies and Intelligent Agents

ANTONIOU, G.; HARMELEN, F.. A Semantic Web Primer MIT Press, ISBN 0-262-01210-3, 2004.

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Ontologies and Intelligent Agents

• Semantic Web Agents will make use of all these outlined technologies:

– Metadata will be used to IDENTIFY and EXTRACT information from web sources

– Ontologies will be used to assist in Web searches

• Interpret retrieved information

• Communicate with other agents

– Logic will be used for processing retrieved information and drawing conclusions

ANTONIOU, G.; HARMELEN, F.. A Semantic Web Primer MIT Press, ISBN 0-262-01210-3, 2004.

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Normative Ontologies

• Roles – a fundamental concept for open software systems

• Unknown entities increase the opportunity to happen unpredictable and non-desired situations

• Regulation over roles are needed!

• A generic ontology is presented to regulate agent actions

– Uses NORMS to police agents’ actions

FELICISSIMO, C.; LUCENA, C.; CARVALHO, G.; PAES, R.. Normative Ontologies to Define Regulations Over Roles in Open Multi-Agent Systems. In: AAAI Fall Symposium on Roles- an interdisciplinary perspective, 2005, Hyatt Crystal City in Arlington, Virginia.

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• Norms can control the action performed in an open MAS defining which agents are:

– PERMITTED

– OBLIGATED

– PROHIBITED

• An ontology can support norms regulation according to the Deontic Logic


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• Presents case study of a Urban Traffic Simulator System (UTTS)

• Three agent roles:

– Car Driver

– Police Officer

– Pedestrian


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• Ontology extension


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• Ontology Instantiation


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Normative Ontologies - Conclusions

• Normative approach to define regulations over roles in open MAS.

• Provides a semantic support for agents

– Reason about action selection

• More details on agent implementation is needed

– Details on the logic?

– What was used for processing retrieved information and drawing conclusions?

Conflict Resolution

Alberto Sardinha

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Conflict Resolution

• Agent decision models are typically based on an attempt to:

– Reach goals

– Satisfy desires

– Fulfill obligations

– Etc

• The main problem is to resolve the conflicts among attitudes:

– Which of the desires and obligations the agent will follow given his beliefs and intentions

BROERSEN, J.; DASTANI, M.; HULSTIJN, J.; HUANG, Z.; TORRE, L van der. The BOID architecture: conflicts between beliefs, obligations, intentions and desires. International Conference on Autonomous Agents, 2001.

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Conflict Resolution

• BOID (Beliefs-Obligations-Intentions-Desires)

– Feed-back loops to consider all effects of actions

– Mechanism to resolve conflicts between outputs of its four components

• Agent reasoning concepts are grounded into these four classes:

– Beliefs are informational attitudes (How the world is expected to be)

– Obligations and Desires are the external and internal motivational attitudes

– Intentions are the results of decision making


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Conflict Resolution

• BDI + Obligations

– Incorporate Obligations, Norms and Commitments of social agents and social rationality

• Presents 15 types of conflicts which can occur between these four concepts. Examples:

– BO conflict:

• It is obligatory to see my mother-in-law this weekend

• But I think I have no time to go


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Conflict Resolution

• Example of conflict:

– BOI conflict

• If I smoke, I should smoke in a smoking area

• I intend to smoke

• However, my office is a non-smoking area

• Agent types and conflict resolution

– Order of overruling

– Only considers the cases where the belief component overrules any motivational attitude component

• Instead of 24 cases, only 6 cases are left


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Conflict Resolution

• Agent types and conflict resolution

– Realistic = the six conflict type where beliefs override all other components

– Simple-minded (BIDO and BIOD) = prior intentions overrule desires and obligations

– Selfish (BDIO and BDOI) = desires overrule obligations

– Social (BIOD, BOID and BODI) = obligations overrule desires


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Conflict Resolution

• A set of observations W

– Can not be overridden

• Initial sets of defeasible rules for the components:

– B, O, I-,D

• An ordering function ρ to resolve conflicts

– Define a function ρ on rules that represents the type of agents

– In case of multiple applicable rules, the one with the lowest ρ value is applied

– ρ is complete: it assigns a unique value to each rule


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Conflict Resolution

• Example 1: (BD conflict)

– I would like to take a long vacation

– I would need to get time off from work to take a long vacation

– But I can´t get time off from work

• Prolog rules:

– b_rule(ex1, true -> ~time_off).

– b_rule(ex1, ~time_off -> ~vacation).

– d_rule(ex1, true -> vacation).


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Conflict Resolution

• We first derive all beliefs resulting in the following extension:

– [~time_off,~vacation]

• This extension is input to the desire component

• Because the only D-rule is not applicable, the final result remains the same:

– [~time_off,~vacation]


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Conflict Resolution

• Example 2: (BOID conflict)

– I intend to go to a conference

– It is obligatory for me not to spend too much money for the conference

– Either I should pay for a cheap flight ticket and stay in a better hotel, or I should pay for an expensive flight ticket and stay in a budget hotel

– I desire to stay in a better hotel

– But, I know that the secretary has booked an expensive flight ticket to me


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Conflict Resolution

• Prolog rules:

– b_rule(ex2, true -> expensive_ticket).

– b_rule(ex2, ~too_much_money -> ~cheap_hotel & ~expensive_ticket).

– b_rule(ex2, ~too_much_money -> cheap_hotel & expensive_ticket).

– i_rule(ex2, true -> conference).

– o_rule(ex2, conference -> ~too_much_money).

– d_rule(ex2, true -> ~cheap_hotel).


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Conflict Resolution

• Simple Minded agent of type ‘BIOD’

– First derive all beleifs and intentions

– [conference, expensive_ticket]

• Because it is a social agent, the obligation rule is applied first

– [~too_much_money,conference, expensive_ticket]

• The extension is FED BACK into the the B component where it trigger the third rule

– [cheap_hotel,~too_much_money,conference, expensive_ticket]


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Conclusions

• Resolve conflicts among informational and motivational attitudes

• The order of the derivations determines the type of an agent

• An important ingredient in the BOID architecture is the presence of feedback loops

Intelligent Agents for norm-regulated MAS Alberto Sardinha Ricardo Gralhoz José Viterbo Karin...

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