Complexity in Simplicity: Flexible Agent-Based State Space Exploration

In this paper, we describe a new flexible framework for state space exploration based on cooperating agents. The idea is to let various agents with different search patterns explore the state space individually and communicate information about fruitful subpaths of the search tree to each other. That way very complex global search behavior is achieved with very simple local behavior. As an example agent behavior, we propose a novel anytime randomized search strategy called frustration search. The effectiveness of the framework is illustrated in the setting of priced timed automata on a number of case studies.

[1]  Gerd Behrmann,et al.  Scheduling Lacquer Production by Reachability Analysis -- A Case Study , 2005 .

[2]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[3]  Rajeev Alur,et al.  A Temporal Logic of Nested Calls and Returns , 2004, TACAS.

[4]  Oded Maler,et al.  Task graph scheduling using timed automata , 2003, Proceedings International Parallel and Distributed Processing Symposium.

[5]  Yassine Lakhnech,et al.  Formal Techniques, Modelling and Analysis of Timed and Fault-Tolerant Systems , 2004, Lecture Notes in Computer Science.

[6]  Ansgar Fehnker,et al.  Citius, Vilius, Melius : guiding and cost-optimality in model checking of timed and hybrid systems , 2002 .

[7]  Nils J. Nilsson,et al.  A Formal Basis for the Heuristic Determination of Minimum Cost Paths , 1968, IEEE Trans. Syst. Sci. Cybern..

[8]  Gerd Behrmann,et al.  Production scheduling by reachability analysis - a case study , 2005, 19th IEEE International Parallel and Distributed Processing Symposium.

[9]  Fred Glover,et al.  Tabu Search - Part II , 1989, INFORMS J. Comput..

[10]  Véronique Bruyère,et al.  Model-Checking for Weighted Timed Automata , 2004, FORMATS/FTRTFT.

[11]  Juraj Hromkovic,et al.  Algorithmics for Hard Problems , 2002, Texts in Theoretical Computer Science An EATCS Series.

[12]  Robin Milner,et al.  On Observing Nondeterminism and Concurrency , 1980, ICALP.

[13]  Kim G. Larsen,et al.  Resource-Optimal Scheduling Using Priced Timed Automata , 2004, TACAS.

[14]  David Abramson,et al.  Scheduling Aircraft Landings - The Static Case , 2000, Transp. Sci..

[15]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[16]  D. Wolpert,et al.  No Free Lunch Theorems for Search , 1995 .

[17]  George J. Pappas,et al.  Optimal Paths in Weighted Timed Automata , 2001, HSCC.

[18]  Radu Grosu,et al.  Monte Carlo Model Checking , 2005, TACAS.

[19]  Thomas A. Henzinger,et al.  Hybrid Systems: Computation and Control , 1998, Lecture Notes in Computer Science.

[20]  Martijn Hendriks,et al.  Model checking timed automata : techniques and applications , 2006 .

[21]  Kim G. Larsen,et al.  As Cheap as Possible: Efficient Cost-Optimal Reachability for Priced Timed Automata , 2001, CAV.

[22]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[23]  Eric A. Hansen,et al.  Beam-Stack Search: Integrating Backtracking with Beam Search , 2005, ICAPS.

[24]  Kim G. Larsen,et al.  Optimal scheduling using priced timed automata , 2005, PERV.