Pagoda: a model for autonomous learning in probabilistic domains
Pagoda: a model for autonomous learning in probabilistic domains
Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
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This dissertation describes PAGODA (Probabilistic Autonomous GOal-Directed Agent), a model for an intelligent agent that learns autonomously in domains containing uncertainty. The model includes techniques for deciding what to learn, selecting a learning bias, inductive learning of probabilistic theories, and planning with the learned theories. PAGODA has been implemented and tested in a simulated robot environment. The model and the results of the tests are summarized, and future work and conclusions are presented briefly.