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Title: Learning models of intelligent agents

Abstract

Agents that operate in a multi-agent system need an efficient strategy to handle their encounters with other agents involved. Searching for an optimal interactive strategy is a hard problem because it depends mostly on the behavior of the others. In this work, interaction among agents is represented as a repeated two-player game, where the agents` objective is to look for a strategy that maximizes their expected sum of rewards in the game. We assume that agents` strategies can be modeled as finite automata. A model-based approach is presented as a possible method for learning an effective interactive strategy. First, we describe how an agent should find an optimal strategy against a given model. Second, we present an unsupervised algorithm that infers a model of the opponent`s automaton from its input/output behavior. A set of experiments that show the potential merit of the algorithm is reported as well.

Authors:
;  [1]
  1. Computer Science Dept., Haifa (Israel)
Publication Date:
OSTI Identifier:
430635
Report Number(s):
CONF-960876-
TRN: 96:006521-0010
Resource Type:
Conference
Resource Relation:
Conference: 13. National conference on artifical intelligence and the 8. Innovative applications of artificial intelligence conference, Portland, OR (United States), 4-8 Aug 1996; Other Information: PBD: 1996; Related Information: Is Part Of Proceedings of the thirteenth national conference on artificial intelligence and the eighth innovative applications of artificial intelligence conference. Volume 1 and 2; PB: 1626 p.
Country of Publication:
United States
Language:
English
Subject:
99 MATHEMATICS, COMPUTERS, INFORMATION SCIENCE, MANAGEMENT, LAW, MISCELLANEOUS; INTERNET; INFORMATION RETRIEVAL; ARTIFICIAL INTELLIGENCE; LEARNING

Citation Formats

Carmel, D, and Markovitch, S. Learning models of intelligent agents. United States: N. p., 1996. Web.
Carmel, D, & Markovitch, S. Learning models of intelligent agents. United States.
Carmel, D, and Markovitch, S. 1996. "Learning models of intelligent agents". United States.
@article{osti_430635,
title = {Learning models of intelligent agents},
author = {Carmel, D and Markovitch, S},
abstractNote = {Agents that operate in a multi-agent system need an efficient strategy to handle their encounters with other agents involved. Searching for an optimal interactive strategy is a hard problem because it depends mostly on the behavior of the others. In this work, interaction among agents is represented as a repeated two-player game, where the agents` objective is to look for a strategy that maximizes their expected sum of rewards in the game. We assume that agents` strategies can be modeled as finite automata. A model-based approach is presented as a possible method for learning an effective interactive strategy. First, we describe how an agent should find an optimal strategy against a given model. Second, we present an unsupervised algorithm that infers a model of the opponent`s automaton from its input/output behavior. A set of experiments that show the potential merit of the algorithm is reported as well.},
doi = {},
url = {https://www.osti.gov/biblio/430635}, journal = {},
number = ,
volume = ,
place = {United States},
year = {Tue Dec 31 00:00:00 EST 1996},
month = {Tue Dec 31 00:00:00 EST 1996}
}

Conference:
Other availability
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