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

TL;DR

This paper introduces UMOP, an OBDD-based framework for universal planning in non-deterministic, multi-agent domains, featuring a new domain language NADL and multiple planning algorithms, including a novel optimistic approach.

Contribution

The paper presents a new planning framework UMOP, a domain language NADL, and an optimistic planning algorithm for complex non-deterministic multi-agent scenarios.

Findings

UMOP effectively handles complex non-deterministic multi-agent domains.

NADL provides explicit control over agents and environment.

The optimistic algorithm generates plausible plans where others fail.

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 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 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 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. 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 efficient planning system.