Probabilistic Models for Agents' Beliefs and Decisions
  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.
