The ability to solve novel problems is a distinguishing feature
of human intelligence. This capacity has been replicated in both
cognitive architectures and AI planning systems, but previous work
has ignored its adaptive character. In this paper, we review HPS, a
cognitive architecture that searches a space of hierarchical problem
decompositions with parameters that support a variety of strategies.
Moreover, decisions made by these strategic parameters may be
conditioned on information available during search, such as the depth,
branching factor, and progress toward the goal description. We examine
three such parameters, one that decides whether to chain forward or
backward when retrieving operator instances, one that determines how
far to backtrack upon failure, and another that decides how deep to
search before backtracking. In each case, we describe adaptive methods
for making these decisions and report experiments which compare their
performance with that for fixed strategies. In closing, we recount prior
research on adaptive problem solving and propose some directions
for future work in this understudied area.
