A general theory of discrimination learning
Production system models of learning and development
Artificial intelligence (2nd ed.)
Artificial intelligence (2nd ed.)
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Some studies in machine learning using the game of checkers
Computers & thought
A Connectionist Algorithm for Genetic Search
Proceedings of the 1st International Conference on Genetic Algorithms
Genetic Plans and the Probabilistic Learning System: Synthesis and Results
Proceedings of the 1st International Conference on Genetic Algorithms
Version spaces: an approach to concept learning.
Version spaces: an approach to concept learning.
An adaptive plan for state-space problems
An adaptive plan for state-space problems
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In real-world domains a concept to be learned may be unwieldy and the environment may be less than ideal. One combination of difficulties occurs if the concept is probabilistic and the learning situation is dynamic. In this case, the data may be noisy and biased. These difficulties arise when learning evaluation functions, which can be considered as concepts. A representative problem, the fifteen puzzle, is used to test six different learning systems: some that fit, count, or partition data in instance, space; some that optimize measures derived from data in hypothesis space; and some that perform combinations of such procedures. These six systems are described, tested, and analyzed. From quantitative differences in several experiments, we extract specific properties. By combining two or three kinds of techniques, we gauge the extent to which they complement each other. Combinations of strengths can overcome difficulties in domains that are simultaneously probabilistic, dynamic, noisy, and biased.