research-article

An integrated possibilistic framework for goal generation in cognitive agents

Authors:

Célia da Costa Pereira,

Andrea G. B. TettamanziAuthors Info & Claims

AAMAS '10: Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1

Pages 1239 - 1246

Published: 10 May 2010 Publication History

Abstract

We propose an integrated theoretical framework, grounded in possibility theory, to account for all the aspects involved in representing and changing beliefs, representing and generating justified desires, and selecting goals based on current and uncertain beliefs about the world, and the preferences of the agent.

Beliefs and desires of a cognitive agent are represented as (two distinct) possibility distributions. This is the original part of the proposed framework in the sense that there does not really exist a full-fledged possibilistic approach to BDI. In the proposed framework: (i) the possibility distribution representing the qualitative utilities associated with desires are the result of a rule-based deliberative process, that is, they depend on the mental state of the agent and are not given a priori; and (ii) the criteria for choosing goals take into account not only the fact that goals must be consistent in the logical sense, but also in the cognitive sense.

References

[1]

D. Baker. Ambivalent desires and the problem with reduction. Philosophical Studies, Published online: 25 March, 2009.

[2]

J. Bell and Z. Huang. Dynamic goal hierarchies. In PRICAI '96, pages 88--103. Springer-Verlag, 1997.

Digital Library

[3]

S. Benferhat, D. Dubois, S. Kaci, and H. Prade. Bipolar possibility theory in preference modeling: Representation, fusion and optimal solutions. Inf. Fusion, 7(1):135--150, 2006.

Digital Library

[4]

S. Benferhat and S. Kaci. Logical representation and fusion of prioritized information based on guaranteed possibility measures: application to the distance-based merging of classical bases. Artif. Intell., 148(1--2):291--333, 2003.

[5]

J. Blee, D. Billington, and A. Sattar. Reasoning with levels of modalities in BDI logic. In PRIMA 2007, pages 410--415. Springer-Verlag, 2009.

Digital Library

[6]

C. Boutilier. Toward a logic for qualitative decision theory. In J. Doyle, E. Sandewall, and P. Torasso, editors, Principles of Knowledge Representation and Reasoning, pages 75--86, 1994.

[7]

J. Broersen, M. Dastani, J. Hulstijn, and L. van der Torre. Goal generation in the BOID architecture. Cognitive Science Quarterly Journal, 2(3--4):428--447, 2002.

[8]

J. T. Cacioppo and G. G. Berntson. The affect system: Architecture and operating characteristics. Current Directions in Psychological Science, 8:133--137, 1999.

[9]

A. Casali, L. Godo, and C. Sierra. Graded BDI models for agent architectures. In Computational logic in multiagent systems. Fifth international workshop, CLIMA V. Pre-proceedings, pages 18--33, 2004.

Digital Library

[10]

C. Castelfranchi and F. Paglieri. The role of beliefs in goal dynamics: Prolegomena to a constructive theory of intentions. Synthese, 155(2):237--263, 2007.

[11]

P. R. Cohen and H. J. Levesque. Intention is choice with commitment. Artif. Intell., 42(2--3):213--261, 1990.

Digital Library

[12]

C. da Costa Pereira and A. Tettamanzi. Goal generation and adoption from partially trusted beliefs. In Proceedings of ECAI 2008, pages 453--457. IOS Press, 2008.

Digital Library

[13]

C. da Costa Pereira and A. Tettamanzi. Goal generation with relevant and trusted beliefs. In Proceedings of AAMAS'08, pages 397--404. IFAAMAS, 2008.

Digital Library

[14]

R. da Silva Neves and E. Raufaste. A psychological study of bipolarity in the possibilistic framework. In IPMU'2004, pages 975--981, 2004.

[15]

M. Dastani, J. Hulstijn, and L. van der Torre. Bdi and qdt: a comparison based on classical decision theory. In In Proceedings of AAAI Spring Symposium GTDT'01, 2001.

[16]

B. De Baets, E. Tsiporkova, and R. Mesiar. Conditioning in possibility theory with strict order norms. Fuzzy Sets Syst., 106(2):221--229, 1999.

Digital Library

[17]

J. Delgrande, D. Dubois, and J. Lang. Iterated revision as prioritized merging. In KR, pages 210--220, 2006.

[18]

D. Dubois and H. Prade. A synthetic view of belief revision with uncertain inputs in the framework of possibility theory. International Journal of Approximate Reasoning, 17:295--324, 1997.

[19]

D. Dubois and H. Prade. Possibility theory, probability theory and multiple-valued logics: A clarification. Annals of Mathematics and Artificial Intelligence, 32(1--4):35--66, 2001.

Digital Library

[20]

D. Dubois and H. Prade. An overview of the asymmetric bipolar representation of positive and negative information in possibility theory. Fuzzy Sets Syst., 160(10):1355--1366, 2009.

Digital Library

[21]

P. Gärdenfors. Belief revision: A vademecum. In Meta-Programming in Logic, pages 1--10. Springer, Berlin, 1992.

Digital Library

[22]

J. Lang. Conditional desires and utilities: an alternative logical approach to qualitative decision theory. In ECAI, pages 318--322, 1996.

[23]

J. Lang, L. Van Der Torre, and E. Weydert. Utilitarian desires. Autonomous Agents and Multi-Agent Systems, 5(3):329--363, 2002.

Digital Library

[24]

S. Parsons and P. Giorgini. An approach to using degrees of belief in BDI agents. In Information, Uncertainty, Fusion. Kluwer, 1999.

[25]

A. S. Rao and M. P. Georgeff. Asymmetry thesis and side-effect problems in linear-time and branching-time intention logics. In IJCAI, pages 498--505, 1991.

Digital Library

[26]

S. Shapiro, Y. Lespérance, and H. J. Levesque. Goal change. In Proceedings of IJCAI'05, pages 582--588, 2005.

Digital Library

[27]

S. Tan and J. Pearl. Qualitative decision theory. In AAAI, pages 928--933, 1994.

Digital Library

[28]

L. A. Zadeh. Fuzzy sets. Information and Control, 8:338--353, 1965.

[29]

L. A. Zadeh. Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets and Systems, 1:3--28, 1978.

Cited By

Vanegas-Hernandez Mda Costa Pereira CMoreno DFusco GTettamanzi ARiveill MHernández JSheth ANgonga AWang yChang EŚlęzak DFranczyk BAlt RTao X(2017)A new urban segregation-growth coupled model using a belief-desire-intention possibilistic frameworkProceedings of the International Conference on Web Intelligence10.1145/3106426.3106486(340-347)Online publication date: 23-Aug-2017
https://dl.acm.org/doi/10.1145/3106426.3106486
Pereira CTettamanzi A(2011)A Syntactic Possibilistic Belief Change Operator for Cognitive AgentsProceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 0210.1109/WI-IAT.2011.54(38-45)Online publication date: 22-Aug-2011
https://dl.acm.org/doi/10.1109/WI-IAT.2011.54
Pereira CTettamanzi AVillata S(2011)Graded Reinstatement in Belief RevisionProceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 0210.1109/WI-IAT.2011.185(58-61)Online publication date: 22-Aug-2011
https://dl.acm.org/doi/10.1109/WI-IAT.2011.185
Show More Cited By

Index Terms

An integrated possibilistic framework for goal generation in cognitive agents
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
      1. Nonmonotonic, default reasoning and belief revision

Recommendations

A Framework for Iterated Belief Revision Using Possibilistic Counterparts to Jeffrey's Rule
Methodologies for Intelligent Systems

Intelligent agents require methods to revise their epistemic state as they acquire new information. Jeffrey's rule, which extends conditioning to probabilistic inputs, is appropriate for revising probabilistic epistemic states when new information comes ...
A belief-desire framework for goal revision
KES'07/WIRN'07: Proceedings of the 11th international conference, KES 2007 and XVII Italian workshop on neural networks conference on Knowledge-based intelligent information and engineering systems: Part I

A rational agent revises its goals when new information becomes available or its "desires" (e.g., tasks it is supposed to carry out) change. In this paper, we propose a logical framework, compatible with BDI theory, to represent changes in the mental ...
Jeffrey's rule of conditioning in a possibilistic framework

Conditioning, belief update and revision are important tasks for designing intelligent systems. Possibility theory is among the powerful uncertainty theories particularly suitable for representing and reasoning with uncertain and incomplete information. ...

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences

AAMAS '10: Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1

May 2010

1578 pages

ISBN:9780982657119

General Chairs:
Michael Luck
King's College London, UK
,
Sandip Sen
University of Tulsa

Sponsors

IFAAMAS

In-Cooperation

SIGAI: ACM Special Interest Group on Artificial Intelligence

Publisher

International Foundation for Autonomous Agents and Multiagent Systems

Richland, SC

Publication History

Published: 10 May 2010

Check for updates

Author Tags

Qualifiers

Research-article

Conference

AAMAS '10

Sponsor:

AAMAS '10: The Ninth International Conference on Autonomous Agents and Multiagent Systems

May 10 - 14, 2010

Toronto, Canada

Acceptance Rates

Overall Acceptance Rate 1,155 of 5,036 submissions, 23%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

4
Total Citations
View Citations
129
Total Downloads

Downloads (Last 12 months)2
Downloads (Last 6 weeks)0

Reflects downloads up to 13 Sep 2024

Other Metrics

View Author Metrics

Citations

Cited By

Vanegas-Hernandez Mda Costa Pereira CMoreno DFusco GTettamanzi ARiveill MHernández JSheth ANgonga AWang yChang EŚlęzak DFranczyk BAlt RTao X(2017)A new urban segregation-growth coupled model using a belief-desire-intention possibilistic frameworkProceedings of the International Conference on Web Intelligence10.1145/3106426.3106486(340-347)Online publication date: 23-Aug-2017
https://dl.acm.org/doi/10.1145/3106426.3106486
Pereira CTettamanzi A(2011)A Syntactic Possibilistic Belief Change Operator for Cognitive AgentsProceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 0210.1109/WI-IAT.2011.54(38-45)Online publication date: 22-Aug-2011
https://dl.acm.org/doi/10.1109/WI-IAT.2011.54
Pereira CTettamanzi AVillata S(2011)Graded Reinstatement in Belief RevisionProceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 0210.1109/WI-IAT.2011.185(58-61)Online publication date: 22-Aug-2011
https://dl.acm.org/doi/10.1109/WI-IAT.2011.185
da Costa Pereira CTettamanzi A(2010)Belief-Goal Relationships in Possibilistic Goal GenerationProceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence10.5555/1860967.1861093(641-646)Online publication date: 4-Aug-2010
https://dl.acm.org/doi/10.5555/1860967.1861093

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents