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A stochastic language for modelling opponent agents

Published: 08 May 2006 Publication History

Abstract

There are numerous cases where a reasoning agent needs to reason about the behavior of an opponent agent. In this paper, we propose a hybrid probabilistic logic language within which we can express what actions an opponent may take in a given situation. We present the syntaxis and semantics of the language, and the concept of a Maximally Probable Course of Action.

References

[1]
Dix, J., Nanni, M., and Subrahmanian, V. 2000. Probabilistic agent reasoning. ACM Transactions of Computational Logic 1, 2, 201--245.
[2]
Dix, J., Kraus, S., and Subrahmanian, V. 2004. Heterogeneous temporal probabilistic agents. ACM Transactions of Computational Logic 5(3).
[3]
Gmytrasiewicz, P. and Durfee, E. 1992. A Logic of Knowledge and Belief for Recursive Modeling. In Proceedings of the 10th National Conference on Artificial Intelligence. AAAI Press/MIT Press, San Jose, CA, 628--634.
[4]
Hanks, S. and McDermott, D. 1994. Modeling a dynamic and uncertain world I: Symbolic and, probabilistic reasoning about change. Artificial Intelligence 65(2), 1--55.
[5]
Ng, R. and Subrahmanian, V. S. 1993. A Semantical Framework for Supporting Subjective and Conditional Probabilities in Deductive Databases. Journal of Automated Reasoning 10 (2), pps 191--235.

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  • (2012)Focused most probable world computations in probabilistic logic programsAnnals of Mathematics and Artificial Intelligence10.1007/s10472-012-9285-y64:2-3(113-143)Online publication date: 1-Mar-2012
  • (2012)Forecasting Group-Level Actions Using Similarity MeasuresHandbook of Computational Approaches to Counterterrorism10.1007/978-1-4614-5311-6_9(181-199)Online publication date: 8-Nov-2012
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cover image ACM Conferences
AAMAS '06: Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
May 2006
1631 pages
ISBN:1595933034
DOI:10.1145/1160633
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 May 2006

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Author Tags

  1. formal models of agency
  2. logic for agent systems

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

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Cited By

View all
  • (2021)The landscape of soft computing applications for terrorism analysisApplied Soft Computing10.1016/j.asoc.2021.107977113:PBOnline publication date: 1-Dec-2021
  • (2012)Focused most probable world computations in probabilistic logic programsAnnals of Mathematics and Artificial Intelligence10.1007/s10472-012-9285-y64:2-3(113-143)Online publication date: 1-Mar-2012
  • (2012)Forecasting Group-Level Actions Using Similarity MeasuresHandbook of Computational Approaches to Counterterrorism10.1007/978-1-4614-5311-6_9(181-199)Online publication date: 8-Nov-2012
  • (2011)A Computationally-Enabled Analysis of Lashkar-e-Taiba Attacks in Jammu and KashmirProceedings of the 2011 European Intelligence and Security Informatics Conference10.1109/EISIC.2011.61(224-229)Online publication date: 12-Sep-2011
  • (2008)Stochastic Opponent Modeling Agents: A Case Study with HezbollahSocial Computing, Behavioral Modeling, and Prediction10.1007/978-0-387-77672-9_6(37-45)Online publication date: 2008
  • (2008)The SOMA Terror Organization Portal (STOP): social network and analytic tools for the real-time analysis of terror groupsSocial Computing, Behavioral Modeling, and Prediction10.1007/978-0-387-77672-9_2(9-18)Online publication date: 2008

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