Uncertainty in Artificial Intelligence
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Probabilistic Models for Agents' Beliefs and Decisions
Brian Milch, Daphne Koller
Abstract:
Many applications of intelligent systems require reasoning about the mental states of agents in the domain. We may want to reason about an agent's beliefs, including beliefs about other agents; we may also want to reason about an agent's preferences, and how his beliefs and preferences relate to his behavior. We define a probabilistic epistemic logic (PEL) in which belief statements are given a formal semantics, and provide an algorithm for asserting and querying PEL formulas in Bayesian networks. We then show how to reason about an agent's behavior by modeling his decision process as an influence diagram and assuming that he behaves rationally. PEL can then be used for reasoning from an agent's observed actions to conclusions about other aspects of the domain, including unobserved domain variables and the agent's mental states.
Keywords: Bayesian networks, influence diagrams, agents, modal logic, epistemic logic, beliefs.
Pages: 389-396
PS Link: http://robotics.stanford.edu/~koller/papers/uai00mk.ps
PDF Link: /papers/00/p389-milch.pdf
BibTex:
@INPROCEEDINGS{Milch00,
AUTHOR = "Brian Milch and Daphne Koller",
TITLE = "Probabilistic Models for Agents' Beliefs and Decisions",
BOOKTITLE = "Proceedings of the Sixteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-00)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "2000",
PAGES = "389--396"
}


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