OBDD-based Universal Planning: Specifying and Solving Planning Problems for Synchronized Agents in Non-deterministic Domains

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.

[1]  Mark Drummond,et al.  Situated Control Rules , 1989, KR.

[2]  Enrico Giunchiglia,et al.  Representing Action: Indeterminacy and Ramifications , 1997, Artif. Intell..

[3]  Mauro Di Manzo,et al.  Planning via Model Checking in Deterministic Domains: Preliminary Report , 1998, AIMSA.

[4]  Austin Tate,et al.  O-Plan: The open Planning Architecture , 1991, Artif. Intell..

[5]  Amy L. Lansky,et al.  Reactive Reasoning and Planning , 1987, AAAI.

[6]  Enrico Giunchiglia,et al.  An Action Language Based on Causal Explanation: Preliminary Report , 1998, AAAI/IAAI.

[7]  Matthew L. Ginsberg,et al.  Universal Planning: An (Almost) Universally Bad Idea , 1989, AI Mag..

[8]  John D. Lowrance,et al.  Planning and reacting in uncertain and dynamic environments , 1995, J. Exp. Theor. Artif. Intell..

[9]  John L. Bresina,et al.  Anytime Synthetic Projection: Maximizing the Probability of Goal Satisfaction , 1990, AAAI.

[10]  Oren Etzioni,et al.  An Approach to Planning with Incomplete Information , 1992, KR.

[11]  Barry Richards,et al.  parcPlan: A Planning Architecture with Parallel Actions, Resources and Constraints , 1994, ISMIS.

[12]  Manuela M. Veloso,et al.  Towards collaborative and adversarial learning: a case study in robotic soccer , 1998, Int. J. Hum. Comput. Stud..

[13]  Leslie Pack Kaelbling,et al.  Planning under Time Constraints in Stochastic Domains , 1993, Artif. Intell..

[14]  Karen Zita Haigh,et al.  Planning, Execution and Learning in a Robotic Agent , 1998, AIPS.

[15]  Eugene Fink,et al.  Integrating planning and learning: the PRODIGY architecture , 1995, J. Exp. Theor. Artif. Intell..

[16]  Chitta Baral,et al.  Reasoning About Effects of Concurrent Actions , 1997, J. Log. Program..

[17]  Fahiem Bacchus,et al.  Using temporal logic to control search in a forward chaining planner , 1996 .

[18]  Avrim Blum,et al.  Fast Planning Through Planning Graph Analysis , 1995, IJCAI.

[19]  Blai Bonet,et al.  A Robust and Fast Action Selection Mechanism for Planning , 1997, AAAI/IAAI.

[20]  Andrew W. Moore,et al.  Reinforcement Learning: A Survey , 1996, J. Artif. Intell. Res..

[21]  Randal E. Bryant,et al.  Graph-Based Algorithms for Boolean Function Manipulation , 1986, IEEE Transactions on Computers.

[22]  Richard Fikes,et al.  STRIPS: A New Approach to the Application of Theorem Proving to Problem Solving , 1971, IJCAI.

[23]  Richard S. Sutton,et al.  Reinforcement Learning , 1992, Handbook of Machine Learning.

[24]  Stephan Merz,et al.  Model Checking , 2000 .

[25]  Kenneth L. McMillan,et al.  Symbolic model checking , 1992 .

[26]  Reid G. Simmons,et al.  Real-Time Search in Non-Deterministic Domains , 1995, IJCAI.

[27]  Mark A. Peot,et al.  Conditional nonlinear planning , 1992 .

[28]  Michael Gelfond,et al.  Representing Action and Change by Logic Programs , 1993, J. Log. Program..

[29]  Edmund M. Clarke,et al.  Symbolic Model Checking with Partitioned Transistion Relations , 1991, VLSI.

[30]  Manuela M. Veloso,et al.  Rationale-Based Monitoring for Planning in Dynamic Environments , 1998, AIPS.

[31]  Chitta Baral,et al.  Reasoning about Eeects of Concurrent Actions , 1996 .

[32]  Jørn Lind-Nielsen,et al.  BuDDy : A binary decision diagram package. , 1999 .

[33]  Paolo Traverso,et al.  Automatic OBDD-Based Generation of Universal Plans in Non-Deterministic Domains , 1998, AAAI/IAAI.

[34]  Daniel S. Weld,et al.  UCPOP: A Sound, Complete, Partial Order Planner for ADL , 1992, KR.

[35]  Paolo Traverso,et al.  Strong Planning in Non-Deterministic Domains Via Model Checking , 1998, AIPS.

[36]  A. R. Lingard,et al.  Planning Parallel Actions , 1998, Artif. Intell..

[37]  Edmund M. Clarke,et al.  Model Checking , 1999, Handbook of Automated Reasoning.

[38]  Michel Barbeau,et al.  Planning Control Rules for Reactive Agents , 1997, Artif. Intell..

[39]  Fausto Giunchiglia,et al.  Planning via Model Checking: A Decision Procedure for AR , 1997, ECP.

[40]  Marcel Schoppers,et al.  Universal Plans for Reactive Robots in Unpredictable Environments , 1987, IJCAI.

[41]  Bart Selman,et al.  Pushing the Envelope: Planning, Propositional Logic and Stochastic Search , 1996, AAAI/IAAI, Vol. 2.

[42]  Erann Gat,et al.  Integrating Planning and Reacting in a Heterogeneous Asynchronous Architecture for Controlling Real-World Mobile Robots , 1992, AAAI.

[43]  Daniel S. Weld.  Recent Advances in AI Planning , 1999, AI Mag..