Approximation of action theories and its application to conformant planning

This paper describes our methodology for building conformant planners, which is based on recent advances in the theory of action and change and answer set programming. The development of a planner for a given dynamic domain starts with encoding the knowledge about fluents and actions of the domain as an action theory D of some action language. Our choice in this paper is AL - an action language with dynamic and static causal laws and executability conditions. An action theory D of AL defines a transition diagram T(D) containing all the possible trajectories of the domain. A transition s, a, s belongs to T(D) iff the execution of the action a in the state s may move the domain to the state s . The second step in the planner development consists in finding a deterministic transition diagram Tlp(D) such that nodes of Tlp(D) are partial states of D, its arcs are labeled by actions, and a path in Tlp(D) from an initial partial state δ0 to a partial state satisfying the goal δf corres...

• Type: Journal
• Year: 2011
• Where: AI
• Authors: Phan Huy Tu, Tran Cao Son, Michael Gelfond, A. Ricardo Morales
• Keywords: AI 2011, Artificial Intelligence, Conformant Planners, Partial State, Transition Diagram