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LEARNING TO SUPPORT CONSTRAINT PROGRAMMERS
Author(s) -
Epstein Susan L.,
Freuder Eugene C.,
Wallace Richard J.
Publication year - 2005
Publication title -
computational intelligence
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.353
H-Index - 52
eISSN - 1467-8640
pISSN - 0824-7935
DOI - 10.1111/j.1467-8640.2005.00277.x
Subject(s) - constraint satisfaction , computer science , constraint (computer aided design) , heuristics , constraint satisfaction problem , constraint learning , constraint programming , artificial intelligence , local consistency , machine learning , mathematical optimization , mathematics , operating system , geometry , probabilistic logic , stochastic programming
This paper describes the Adaptive Constraint Engine (ACE), an ambitious ongoing research project to support constraint programmers, both human and machine. The program begins with substantial knowledge about constraint satisfaction. The program harnesses a cognitively‐oriented architecture (FORR) to manage search heuristics and to learn new ones. ACE can transfer what it learns on simple problems to solve more difficult ones, and can readily export its knowledge to ordinary constraint solvers. It currently serves both as a learner and as a test bed for the constraint community.