Conformant Graphplan

Planning under uncertainty is a difficult task. If sensory information is available, it is possible to do contingency planning – that is, develop plans where certain branches are executed conditionally, based on the outcome of sensory actions. However, even without sensory information, it is often possible to develop useful plans that succeed no matter which of the allowed states the world is actually in. We refer to this type of planning as conformant planning . Few conformant planners have been built, partly because conformant planning requires the ability to reason about disjunction. In this paper we describe Conformant Graphplan (CGP), a Graphplan-based planner that develops sound (non-contingent) plans when faced with uncertainty in the initial conditions and in the outcome of actions. The basic idea is to develop separate plan graphs for each possible world. This requires some subtle changes to both the graph expansion and solution extraction phases of Graphplan. In particular, the solution extraction phase must consider the unexpected side effects of actions in other possible worlds, and must confront any undesirable effects. We show that CGP performs significantly better than two previous (probabilistic) conformant planners.