Using the Causal Graph to Enhance Translations to Solve Contingent Planning Problems

Planning with partial observation, an area called contingent planning, is a complex and challenging problem since it requires to keep track of belief states to search for a contingent plan of actions. Recent approaches considers the agent's knowledge about the world to compile a contingent planning problem into a full observable planning problem, described in an epistemic logic language, and then use an efficcient full observable planner to solve the translated problem. In this paper we use the concept of relevance and causality to propose a new translation based in a structure called Causal Graph that can improve the belief tracking task of contingent Planning problems described in a more general planning language, in particular problems envolving actions with uncertainty on its conditional effects.