Using General AI Planner to Understand Human Problem-Solving on Tower of London

Understanding problem solving or planning has been a shared challenge for both AI and cognitive science since the birth of both fields. We explore the extent to which modern planning algorithms from the field of AI can account for human performance on the Tower of London (TOL) task, a close relative of the Tower of Hanoi problem that has been extensively studied by psychologists. We characterize the task using the Planning Domain Definition Language (PDDL) and evaluate a family of well-known planners, including an online planner, optimal planners and satisfying planners with different heuristics on the TOL task. We also introduce a novel methodology that compares planner performance with human behavior using the number of nodes generated by the planner during the search process. We find that none of the planners evaluated is able to capture all of the qualitative properties of human performance identified by previous behavioral work on the TOL task. Our results suggest that humans may rely on an approach that goes beyond standard AI planners by considering both local and global properties of the task.