Backward reasoning through AND/OR trees to solve problems.

Whether travelling, playing games, or debugging code, any situation where an agent desires change can be framed as a problem. Despite this ubiquity, there is no unifying framework describing how people reason backwards when solving problems. We introduce AND/OR trees, which chain together subgoals and actions to attain them, as a way to represent this process. To investigate whether actions from AND/OR trees were predictive of human behavior, we conducted a study in which participants solved deterministic, long-horizon puzzles. AND/OR trees were able to explain most of the actions the participants took. Next, we modeled search through these trees using a psychologically plausible, single-parameter search algorithm. We fit this model to the data of individual participants and found that it captures trends in summary statistics of human play. Our results show the promise of AND/OR trees as a representation for backward reasoning in problem solving.