Appearing in Aips-98 Conditional Eeects in Graphplan

Graphplan has attracted considerable interest because of its extremely high performance, but the algorithm's inability to handle action representations more expressive than STRIPS is a major limitation. In particular , extending Graphplan to handle conditional eeects is a surprisingly subtle enterprise. In this paper, we describe the space of possible alternatives, and then concentrate on one particular approach we call fac-tored expansion. Factored expansion splits an action with conditional eeects into several new actions called components, one for each conditional eeect. Because these action components are not independent, fac-tored expansion complicates both the mutual exclusion and backward chaining phases of Graphplan. As compensation, factored expansion often produces dramatically smaller domain models than does the more obvious full-expansion into exclusive STRIPS actions. We present experimental results showing that factored expansion dominates full expansion on large problems.