OBDD-based universal planning: specifying and solving planning problems for synchronized agents in non-deterministic domains

  • Authors:

  • Rune M. Jensen;Manuela M. Veloso

  • Affiliations:

  • Computer Science Department, Carnegie Mellon University, Pittsburgh, PA;Computer Science Department, Carnegie Mellon University, Pittsburgh, PA

  • Venue:
  • Artificial intelligence today
  • Year:
  • 1999

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Abstract

Recently model checking representation and search techniques were shown to be efficiently 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 finite automaton (NFA) and then apply fast algorithms from model checking to search for a solution. OBDDS can effectively 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 chapter, we present UMOP,1 a new universal OBDD-based planning framework for non-deterministic, multi-agent domains, which is also applicable to deterministic singleagent domains as a special case. We introduce a new planning domain description language, NADL,2 to specify non-deterministic multi-agent domains. The language contributes the explicit definition of controllable agents and uncontrollable environment agents. We describe the syntax and semantics of NADL and show how to build an efficient OBDD-based representation of an NADL description. The UMOP planning system uses NADL and different OBDD-based universal planning algorithms. It includes the previously developed strong and strong cyclic planning algorithms [9,10]. In addition, we introduce our new optimistic planning algorithm, which relaxes optimality guarantees and generates plausible universal plans in some domains where no strong or strong cyclic solution exist. We present empirical results from domains ranging from deterministic and single-agent with no environment actions to nondeterministic and multi-agent with complex environment actions. UMOP is shown to be a rich and efficient planning system.