ABSTRACT
When faced with a novel problem, people can sometimes decide what to do by imagining alternative sequences of actions and then taking the sequence that solves the problem. In many problems, however, various constraints, such as working memory capacity, limit the amount of internal lookahead that people can do. This paper describes Bottom-Up Recognition Learning (BURL), a model of limited-lookahead learning based on final first learning and knowledge compilation. In BURL, knowledge compilation of limited-lookahead search over successive problem-solving trials transfers knowledge from the leaf nodes of a problem space to the top node. Two experiments test BURL’s predictions. The first compares the Soar implementation of BURL to human subjects learning to play two Tic-Tac-Toe isomorphs. This experiment shows that BURL can account for learning that occurs when subjects can perform a limited lookahead. The second experiment studies transfer between two strategy acquisition tasks for one isomorph. This experiment shows that BURL must be used in conjunction with other learning methods to fully explain skill acquisition on limited-lookahead tasks.
