Tactic-Based Learning is a new selection policy for statistical learning systems that has been tested with a Collective Learning Automaton which solves a simple, but representative problem. Current selection policies respond to immature stimuli that do not yet have high-confidence responses associated with them by selecting responses randomly. Albeit unbiased, this policy ignores any confident information already acquired for other well-trained stimuli. To exploit this confident information, Tactic-Based Learning hypothesizes that in the absence of a sufficiently confident response to a given stimulus, selecting a confident response to a different, but nonetheless well-trained stimulus is a better strategy than selecting a random response. Tactic-Based Learning does not require any feature comparison in search of an appropriate response. Preliminary results show that Tactic-Based Learning significantly accelerates learning and reduces error, especially when several stimuli share the same response, i.e. , when broad domain generalization is possible. Tactic-Based Learning reduces the use of pseudo-random number generators in the response selection process. Additionally, Tactic-Based Learning assists the recovery of learning performance when the problem evolves over time.
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|Commitee:||Becker, Glenn, Happel, Mark, Martin, C. Dianne, Youssef, Abdou|
|School:||The George Washington University|
|School Location:||United States -- District of Columbia|
|Source:||DAI-B 69/03, Dissertation Abstracts International|
|Subjects:||Statistics, Artificial intelligence|
|Keywords:||Cognitive development, Collective learning systems, Machine learning, Reinforcement learning, Statistical learning, Tactic-based learning|
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