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Learning Models of Other Agents Using Influence Diagrams

  • Dicky Suryadi
  • Piotr J. Gmytrasiewicz
Part of the CISM International Centre for Mechanical Sciences book series (CISM, volume 407)

Abstract

We adopt decision theory as a descriptive paradigm to model rational agents. We use influence diagrams as a modeling representation of agents, which is used to interact with them and to predict their behavior. We provide a framework that an agent can use to learn the models of other agents in a multi-agent system (MAS) based on their observed behavior. Since the correct model is usually not known with certainty our agents maintain a number of possible models and assign them probabilities of being correct. When none of the available models is likely to be correct, we modify one of them to better account for the observed behaviors. The modification refines the parameters of the influence diagram used to model the other agent’s capabilities, preferences, or beliefs. The modified model is then allowed to compete with the other models and the probability assigned to it being correct can be arrived at based on how well it predicts the observed behaviors of the other agent.

Keywords

Utility Function Decision Node Learning Agent Influence Diagram Neural Network Learning 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 1999

Authors and Affiliations

  • Dicky Suryadi
    • 1
  • Piotr J. Gmytrasiewicz
    • 1
  1. 1.Department of Computer Science and EngineeringUniversity of Texas at ArlingtonUSA

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