On translation of conformant action planning to linear programming

Planning in Artificial Intelligence is a problem of finding a sequence of actions that transform given initial state of the problem to desired goal situation. In this work we consider computational difficulty of so called conformant planning. Conformant planning is a problem of searching for non-conditional plans that do not depend on sensory information, but still succeed no matter which of the possible initial states the world is actually in. Finding a plan of such problems is computationally difficult. To avoid this difficulty a transformation to Linear Programming Problem, illustrated by an example, is proposed.