A Unified Framework for Planning and Learning.
Abstract
In this report, we present a computational framework for planning and learning that is constrained by knowledge of human behavior. We first describe DAEDALUS, a planning system that learns from successful problem-solving traces. The model stores plan knowledge in a probabilistic concept hierarchy, retrieves relevant operators through a process of heuristic classification, organizes search using a flexible version of means-ends analysis, and stores plan knowledge through an incremental process of concept formation. We report experimental studies of DAEDALUS' behavior that show learning improves solution quality and reduces search, but that also reveal increased retrieval cost and fewer solved problems. In addition, we find that the model accounts for a variety of qualitative phenomena observed in human problem solving. After this, we present our current designs for ICARUS, an integrated architecture for intelligent agents that extends the ideas in DAEDALUS. This architecture will store entire problem-solving traces in memory, which should support a number of additional capabilities, including the unification of search control knowledge and macro-operators, the interleaving of planning and execution, and the integration of closed-loop and open-loop processing. (AN)
Document Details
- Document Type
- Technical Report
- Publication Date
- Jun 01, 1995
- Accession Number
- ADA297367
Entities
People
- John A. Allen
- Pat Langley
Organizations
- University of California, Irvine