Abstract
Distributed hill-climbing algorithms are a powerful, practical technique for solving large Distributed Constraint Satisfaction Problems (DSCPs) such as distributed scheduling, resource allocation, and distributed optimization. Although incomplete, an ideal hill-climbing algorithm finds a solution that is very close to optimal while also minimizing the cost (i.e. the required bandwidth, processing cycles, etc.) of finding the solution. The Distributed Stochastic Algorithm (DSA) is a hill-climbing technique that works by having agents change their value with probability p when making that change will reduce the number of constraint violations. Traditionally, the value of p is constant, chosen by a developer at design time to be a value that works for the general case, meaning the algorithm does not change or learn over the time taken to find a solution. In this paper, we replace the constant value of p with different probability distribution functions in the context of solving graph-coloring problems to determine if DSA can be optimized when the probability values are agent-specific. We experiment with non-adaptive and adaptive distribution functions and evaluate our results based on the number of violations remaining in a solution and the total number of messages that were exchanged.
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References
Bacchus, F., van Beek, P.: On the conversion between non-binary constraint satisfaction problems. In: AAAI 1998/IAAI 1998: Proceedings of the Fifteenth National/Tenth Conference on Artificial Intelligence/Innovative Applications of Artificial Intelligence, pp. 311–318. American Association for Artificial Intelligence, Menlo Park (1998)
Bulatov, A., Krokhin, A., Jeavons, P.: The complexity of maximal constraint languages. In: STOC 2001: Proceedings of the Thirty-Third Annual ACM Symposium on Theory of Computing, pp. 667–674. ACM, New York (2001)
Faltings, B.: Distributed constraint programming. In: van Beek, P., Rossi, F., Walsh, T. (eds.) Handbook of Constraint Programming. Foundations of Artificial Intelligence, ch. 20, vol. 2, pp. 699–729. Elsevier (2006)
Fitzpatrick, S., Meertens, L.: Distributed Coordination Through Anarchic Optimization. In: Distributed Sensor Networks: A Multiagent Perspective, pp. 257–294. Kluwer Academic Publishers (2003)
Mailler, R.: Using prior knowledge to improve distributed hill climbing. In: Proceedings of the 2006 International Conference on Intelligent Agent Technology (IAT 2006) (2006)
Mailler, R., Lesser, V.: Using Cooperative Mediation to Solve Distributed Constraint Satisfaction Problems. In: Proceedings of Third International Joint Conference on Autonomous Agents and MultiAgent Systems (AAMAS 2004) (2004)
Meisels, A.: Distributed search by constrained agents: algorithms, performance, communication. Springer, Heidelberg (2008)
Moody, J., Darken, C.J.: Fast learning in networks of locally-tuned processing units. Neural Comput. 1(2), 281–294 (1989)
Morris, P.: The breakout method for escaping local minima. In: Proceedings of the Eleventh National Conference on Artificial Intelligence, pp. 40–45 (1993)
Richard, A.G.B., Sutton, S.: Reinforcement Learning: An Introduction. MIT Press, Cambridge (1999)
Selman, B., Kautz, H., Cohen, B.: Noise strategies for improving local search. In: Proceedings of the Twelfth National Conference on Artificial Intelligence (AAAI 1994), pp. 337–343 (1994)
Smith, M., Mailler, R.: Getting What You Pay For: Is Exploration in Distributed Hill Climbing Really Worth It?. In: Int’l Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT (2010)
Yokoo, M.: Asynchronous Weak-Commitment Search for Solving Distributed Constraint Satisfaction Problems. In: Montanari, U., Rossi, F. (eds.) CP 1995. LNCS, vol. 976, pp. 88–102. Springer, Heidelberg (1995)
Yokoo, M., Durfee, E.H., Ishida, T., Kuwabara, K.: Distributed constraint satisfaction for formalizing distributed problem solving. In: International Conference on Distributed Computing Systems, pp. 614–621 (1992)
Yokoo, M., Hirayama, K.: Distributed breakout algorithm for solving distributed constraint satisfaction problems. In: International Conference on Multi-Agent Systems, ICMAS (1996)
Zhang, W., Wang, G., Wittenburg, L.: Distributed stochastic search for constraint satisfaction and optimization: Parallelism, phase transitions and performance. In: Proceedings of the AAAI Workshop on Probabilistic Approaches in Search, pp. 53–59 (2002)
Zhang, W., Wittenburg, L.: Distributed breakout revisited. In: Proceedings of the Eighteenth National Conference on Artificial Intelligence (AAAI-2002), pp. 352–357 (2002)
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Smith, M., Sen, S., Mailler, R. (2012). Adaptive and Non-adaptive Distribution Functions for DSA. In: Desai, N., Liu, A., Winikoff, M. (eds) Principles and Practice of Multi-Agent Systems. PRIMA 2010. Lecture Notes in Computer Science(), vol 7057. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25920-3_5
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DOI: https://doi.org/10.1007/978-3-642-25920-3_5
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