PAGODA: A Model for Autonomous Learning in Probabilistic Domains (1992)
| Citations: | 5 - 2 self |
BibTeX
@TECHREPORT{Desjardins92pagoda:a,
author = {Marie Desjardins},
title = {PAGODA: A Model for Autonomous Learning in Probabilistic Domains},
institution = {},
year = {1992}
}
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Abstract
as a testbed for designing intelligent agents. The system consists of an overall agent architecture and five components within the architecture. The five components are: 1. Goal-Directed Learning (GDL), a decision-theoretic method for selecting learning goals. 2. Probabilistic Bias Evaluation (PBE), a technique for using probabilistic background knowledge to select learning biases for the learning goals. 3. Uniquely Predictive Theories (UPTs) and Probability Computation using Independence (PCI), a probabilistic representation and Bayesian inference method for the agent's theories. 4. A probabilistic learning component, consisting of a heuristic search algorithm and a Bayesian method for evaluating proposed theories. 5. A decision-theoretic probabilistic planner, which searches through the probability space defined by the agent's current theory to select the best action. PAGODA is given as input an initial planning goal (its ove
