A Unified Framework for Planning and Learning
Abstract
In this paper we present a computational framework for planning and learning that is constrained by knowledge of human behavior. We first describe DADALUS, 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 formulation. We report experimental studies of DADALUS' 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 on the idea in DADLUS. This architecture would store entire problem-solving traces in memory, which should report a number of additional capabilities, including the unification of search control knowledge and macrooperators, the interleaving of planning and execution, and the integration of closed loop and open loop processing.
Document Details
- Document Type
- Technical Report
- Publication Date
- Nov 30, 1990
- Accession Number
- ADA230977
Entities
People
- John A. Allen
- Pat Langley
Organizations
- University of California, Irvine