Representing and Reasoning With Probabilistic Knowledge: A Bayesian
  Approach
  PAGODA (Probabilistic Autonomous Goal-Directed Agent) is a model for
autonomous learning in probabilistic domains [desJardins, 1992] that
incorporates innovative techniques for using the agent's existing knowledge to
guide and constrain the learning process and for representing, reasoning with,
and learning probabilistic knowledge. This paper describes the probabilistic
representation and inference mechanism used in PAGODA. PAGODA forms theories
about the effects of its actions and the world state on the environment over
time. These theories are represented as conditional probability distributions.
A restriction is imposed on the structure of the theories that allows the
inference mechanism to find a unique predicted distribution for any action and
world state description. These restricted theories are called uniquely
predictive theories. The inference mechanism, Probability Combination using
Independence (PCI), uses minimal independence assumptions to combine the
probabilities in a theory to make probabilistic predictions.
