Integrating planning and learning: the PRODIGY architecture

DOI10.1080/09528139508953801zbMath0939.68830OpenAlexW2035078485MaRDI QIDQ4834396

Daniel Borrajo, Manuela M. Veloso, Alicia Pérez, Eugene Fink, Jaime G. Carbonell, Jim Blythe

Publication date: 4 July 2000

Published in: Journal of Experimental & Theoretical Artificial Intelligence (Search for Journal in Brave)

Full work available at URL: http://hdl.handle.net/10016/6770

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.) (68T20)

Related Items

Learning from planner performance, Proof planning with multiple strategies, Efficient interactive decision-making framework for robotic applications, Model-lite planning: case-based vs. model-based approaches, Automatically selecting and using primary effects in planning: Theory and experiments., Using temporal logics to express search control knowledge for planning, Knowledge-based proof planning, Multi-agent plan based information gathering, Planning under uncertainty as G<scp>OLOG</scp>programs, Computing programs for generalized planning using a classical planner, ACTION FAILURE RECOVERY VIA MODEL-BASED DIAGNOSIS AND CONFORMANT PLANNING, Using genetic programming to learn and improve control knowledge.

Uses Software

• Prodigy

Cites Work

• Acquiring search-control knowledge via static analysis
• Planning for conjunctive goals
• Theory and algorithms for plan merging
• Automatically generating abstractions for planning
• STRIPS: A new approach to the application of theorem proving to problem solving