Uncertainty in Artificial Intelligence
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Representing and Reasoning With Probabilistic Knowledge: A Bayesian Approach
Marie desJardins
Abstract:
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.
Keywords:
Pages: 227-234
PS Link:
PDF Link: /papers/93/p227-desjardins.pdf
BibTex:
@INPROCEEDINGS{desJardins93,
AUTHOR = "Marie desJardins ",
TITLE = "Representing and Reasoning With Probabilistic Knowledge: A Bayesian Approach",
BOOKTITLE = "Proceedings of the Ninth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-93)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1993",
PAGES = "227--234"
}


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