OBDD-based Universal Planning 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 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.

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

[2]  Drew McDermott,et al.  The 1998 AI Planning Systems Competition , 2000, AI Mag..

[3]  Richard S. Sutton,et al.  Introduction to Reinforcement Learning , 1998 .

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

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

[6]  Manuela M. Veloso,et al.  Efficient Goal-Directed Exploration , 1996, AAAI/IAAI, Vol. 1.

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

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

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

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

[11]  Manuela M. Veloso,et al.  Analogical Replay for Efficient Conditional Planning , 1997, AAAI/IAAI.

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

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

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

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

[16]  Jim Blythe,et al.  Planning Under Uncertainty in Dynamic Domains , 1998 .

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

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

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

[20]  Derek Long,et al.  Domain-lndependent Planner Compilation , 1998 .

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

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

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

[24]  Derek Long.  Domain-Independent Planner Compilation , 1998 .

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

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

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

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

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

[30]  Bart Selman,et al.  Unifying SAT-based and Graph-based Planning , 1999, IJCAI.

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

[32]  Bernhard Nebel,et al.  Extending Planning Graphs to an ADL Subset , 1997, ECP.

[33]  M. Benda,et al.  On Optimal Cooperation of Knowledge Sources , 1985 .

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

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

[36]  J. Blythe,et al.  Analogical Replay for E cient Conditional Planning , 1997 .

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

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

[39]  Giuseppe De Giacomo,et al.  Planning Via Model Checking : Some Experimental Results , 2000 .

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

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

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

[43]  Patrik Haslum,et al.  Admissible Heuristics for Optimal Planning , 2000, AIPS.

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

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

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

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

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

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

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

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

[52]  Manuela M. Veloso,et al.  Planning and Learning by Analogical Reasoning , 1994, Lecture Notes in Computer Science.

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