Learning to search: From weak methods to domain-specific heuristics

Learning from experience involves three distinct components—generating behavior, assigning credit, and modifying behavior. We discuss these components in the context of learning search heuristics, along with the types of learning that can occur. We then focus on SAGE, a system that improves its search strategies with practice. The program is implemented as a production system, and learns by creating and strengthening rules for proposing moves. SAGE incorporates five different heuristics for assigning credit and blame, and employs a discrimination process to direct its search through the space of rules. The system has shown its generality by learning heuristics for directing search in six different task domains. In addition to improving its search behavior on practice problems, SAGE is able to transfer its expertise to scaled-up versions of a task, and in one case, transfers its acquired search strategy to problems with different initial and goal states.     

As the Lights Fade: A Grounded Theory of Male Professional Athletes’ Decision-Making and Transition to Retirement

Retirement can be particularly challenging for professional athletes, given their commitment and identification with their sport. The purpose of this study was to examine how American male professional athletes make the decision to retire and how that decision process affects transition in retirement. The researchers used constructivist grounded theory to analyze personal accounts from professional athletes who wrote about retirement, and a model on the decision-making and transition process of retirement was developed. The findings show that athletes struggle in retirement, but by relying on their social support, they can accept the conclusion of their career as a professional athlete.