OBDD-based Universal Planning for Synchronized Agents in Non-Deterministic Domains

Recently model checking representation and search techniques were shown to be ef-ciently applicable to planning, in particular to non-deterministic planning. Such planning approaches use Ordered Binary Decision Diagrams (obdds) to encode a planning domain as a non-deterministic nite automaton and then apply fast algorithms from model checking to search for a solution. obdds can eeectively scale and can provide universal plans for complex planning domains. We are particularly interested in addressing the complexities arising in non-deterministic, multi-agent domains. In this article, we present umop, a new universal obdd-based planning framework for non-deterministic, multi-agent domains. We introduce a new planning domain description language, NADL, to specify non-deterministic, multi-agent domains. The language contributes the explicit deenition of controllable agents and uncontrollable environment agents. We describe the syntax and semantics of NADL and show how to build an eecient obdd-based representation of an NADL description. The umop planning system uses NADL and diierent obdd-based universal planning algorithms. It includes the previously developed strong and strong cyclic planning algorithms. In addition, we introduce our new optimistic planning algorithm that relaxes optimality guarantees and generates plausible universal plans in some domains where no strong nor strong cyclic solution exists. We present empirical results applying umop to domains ranging from deterministic and single-agent with no environment actions to non-deterministic and multi-agent with complex environment actions. Umop is shown to be a rich and eecient planning system.