Computer Science
695 Park Ave.
NY, NY 10021
Susan L. Epstein
The CUNY Graduate School, Department
of Computer Science and
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FORR
FORR is a satisficing architecture for learning and problem solving. Its premise is that agreement among reactivity, heuristics, planning, and search is a valid decision- making principle. A FORR-based program learns to specialize general domain expertise into problem-class specific expertise. An implemented FORR shell supports domain-specific development. FORR explores the relationship among reactivity, heuristics, and search. Applications include Hoyle for game playing, Ariadne for robot path finding and ACE for constraint satisficing. Each of them has demonstrated substantial, learned expertise after relatively little training.
Current Work
- The integration of planning with high-level reasoning.
- Learning new heuristics.
- Autonomous restructuring of a decision hierarchy.
- Metaknowledge for heuristics.
- Multiagent solutions to multiple goal
problems with (CD).
Application Domains
Adaptive Constraint Engine (ACE)
Protein Sequence Alignment (CAPS)
Key references
Epstein, S.L. (2004). Metaknowledge for Autonomous Systems. In Proceedings of AAAI Spring Symposium on Knowledge Representation and Ontology for Autonomous Systems. AAAI.
Additional
references This
material is based upon work supported by the National Science Foundation
under Grant Nos. #IIS-0328743, 9423085, #IRI-9703475, 9222720, and #9001936, by the
New York State Technological Development Graduate Research and Technology
Initiative, and by the PSC-CUNY Research Foundation. Any
opinions, findings, and conclusions or recommendations expressed in this
material are those of the author(s) and do not necessarily reflect the
views of the National Science Foundation, New York State, or PSC-CUNY.