A new analysis method for dynamic distributed constraint satisfaction

There has been an increasing recognition that a number of key computational problems require distributed solution techniques. To facilitate the creation and advancement of these techniques, researchers have developed the distributed constraint satisfaction (DCSP) formalism with the understanding that many critical real-world problems can be represented using it. Consequently, this formalism has led to the creation of myriad protocols for solving problems in this class. However, this formalism ignores a critical feature of many environments: problems change over time. The dynamic, DCSP (DynDCSP) formalism was invented to address this deficiency, but this model has received inadequate attention from the research community. A key impediment to advancing this research area is the lack of a compelling theoretical underpinning to the analysis of these problems and the evaluation of the protocols used to solve them. This work creates a mapping of the DynDCSP formalism onto thermodynamic systems. Under this mapping, it shows that DynDCSPs obey the three laws of thermodynamics. Utilizing these laws, this work develops, for the first time, a method for characterizing the impact that dynamics has on a distributed problem as well as a technique for predicting the expected performance of distributed protocols under various levels of dynamics.

[1]  M. Mézard,et al.  A replica analysis of the travelling salesman problem , 1986 .

[2]  Florent Krzakala,et al.  Phase Transitions in the Coloring of Random Graphs , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[3]  Eva Onaindia,et al.  A distributed CSP approach for collaborative planning systems , 2008, Eng. Appl. Artif. Intell..

[4]  R. Monasson,et al.  Statistical Mechanics of the K--Satisfiability Model , 1996, cond-mat/9606215.

[5]  Tom Holvoet,et al.  The DynCOAA algorithm for dynamic constraint optimization problems , 2006, AAMAS '06.

[6]  Peter van Beek,et al.  On the conversion between non-binary constraint satisfaction problems , 1998, AAAI 1998.

[7]  V. R. Lesser,et al.  Asynchronous Partial Overlay: A New Algorithm for Solving Distributed Constraint Satisfaction Problems , 2011, J. Artif. Intell. Res..

[8]  Richard R. Brooks,et al.  Distributed Sensor Networks: A Multiagent Perspective , 2008 .

[9]  Gérard Verfaillie,et al.  Constraint Solving in Uncertain and Dynamic Environments: A Survey , 2005, Constraints.

[10]  Rémi Monasson,et al.  Determining computational complexity from characteristic ‘phase transitions’ , 1999, Nature.

[11]  M. Yokoo,et al.  Distributed Breakout Algorithm for Solving Distributed Constraint Satisfaction Problems , 1996 .

[12]  Patrick Prosser,et al.  HYBRID ALGORITHMS FOR THE CONSTRAINT SATISFACTION PROBLEM , 1993, Comput. Intell..

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

[14]  K. Deimling.  Fixed Point Theory , 2008 .

[15]  Monasson,et al.  Entropy of the K-satisfiability problem. , 1996, Physical review letters.

[16]  Makoto Yokoo,et al.  Distributed constraint satisfaction for formalizing distributed problem solving , 1992, [1992] Proceedings of the 12th International Conference on Distributed Computing Systems.

[17]  M. Mézard,et al.  Replicas and optimization , 1985 .

[18]  Weixiong Zhang,et al.  A Comparative Study of Distributed Constraint Algorithms , 2003 .

[19]  Victor R. Lesser,et al.  Multistage negotiation for distributed constraint satisfaction , 1991, IEEE Trans. Syst. Man Cybern..

[20]  Roger Mailler.  Comparing two approaches to dynamic, distributed constraint satisfaction , 2005, AAMAS '05.

[21]  Roger Mailler,et al.  Improving Asynchronous Partial Overlay , 2012, 2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology.

[22]  Norman M. Sadeh,et al.  Distributed constrained heuristic search , 1991, IEEE Trans. Syst. Man Cybern..

[23]  Rina Dechter,et al.  Belief Maintenance in Dynamic Constraint Networks , 1988, AAAI.

[24]  S. Sitharama Iyengar,et al.  Distributed Sensor Networks , 2004 .

[25]  N. Metropolis,et al.  Equation of State Calculations by Fast Computing Machines , 1953, Resonance.