Proactive Dynamic DCOPs

The current approaches to model dynamism in DCOPs solve a sequence of static problems, reacting to the changes in the environment as the agents observe them. Such approaches, thus, ignore possible predictions on the environment evolution. To overcome such limitations, we introduce the Proactive Dynamic DCOP (PD-DCOP) model, a novel formalism to model dynamic DCOPs in the presence of exogenous uncertainty. In contrast to reactive approaches, PD-DCOPs are able to explicitly model the possible changes to the problem, and take such information into account proactively, when solving the dynamically changing problem.

[1]  Joël Quinqueton,et al.  Distributed Intelligent Backtracking , 1998, ECAI.

[2]  Steven Okamoto,et al.  Distributed constraint optimization for teams of mobile sensing agents , 2014, Autonomous Agents and Multi-Agent Systems.

[3]  Milind Tambe,et al.  A Family of Graphical-Game-Based Algorithms for Distributed Constraint Optimization Problems , 2006 .

[4]  Thomas Schiex,et al.  Mixed Constraint Satisfaction: A Framework for Decision Problems under Incomplete Knowledge , 1996, AAAI/IAAI, Vol. 1.

[5]  Makoto Yokoo,et al.  Adopt: asynchronous distributed constraint optimization with quality guarantees , 2005, Artif. Intell..

[6]  François Charpillet,et al.  Producing efficient error-bounded solutions for transition independent decentralized mdps , 2013, AAMAS.

[7]  Robert N. Lass,et al.  Dynamic Distributed Constraint Reasoning , 2008, AAAI.

[8]  Shimon Whiteson,et al.  Incremental Clustering and Expansion for Faster Optimal Planning in Dec-POMDPs , 2013, J. Artif. Intell. Res..

[9]  Shlomo Zilberstein,et al.  Dynamic Programming for Partially Observable Stochastic Games , 2004, AAAI.

[10]  Nicholas R. Jennings,et al.  Decentralised coordination of low-power embedded devices using the max-sum algorithm , 2008, AAMAS.

[11]  Milind Tambe,et al.  Taking DCOP to the real world: efficient complete solutions for distributed multi-event scheduling , 2004, Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems, 2004. AAMAS 2004..

[12]  Makoto Yokoo,et al.  Coalition Structure Generation based on Distributed Constraint Optimization , 2010, AAAI.

[13]  François Charpillet,et al.  MAA*: A Heuristic Search Algorithm for Solving Decentralized POMDPs , 2005, UAI.

[14]  Eugene C. Freuder,et al.  Stable Solutions for Dynamic Constraint Satisfaction Problems , 1998, CP.

[15]  Toby Walsh,et al.  Stochastic Constraint Programming: A Scenario-Based Approach , 2009, Constraints.

[16]  Hoong Chuin Lau,et al.  Decentralized Multi-Agent Reinforcement Learning in Average-Reward Dynamic DCOPs , 2014, AAAI.

[17]  Toby Walsh,et al.  Stochastic Constraint Programming , 2002, ECAI.

[18]  Neil Immerman,et al.  The Complexity of Decentralized Control of Markov Decision Processes , 2000, UAI.

[19]  Boi Faltings,et al.  Superstabilizing, Fault-Containing Distributed Combinatorial Optimization , 2005, AAAI.

[20]  Claudia V. Goldman,et al.  Solving Transition Independent Decentralized Markov Decision Processes , 2004, J. Artif. Intell. Res..

[21]  Makoto Yokoo,et al.  Distributed Problem Solving , 2012, AI Mag..

[22]  Boi Faltings,et al.  Optimal Solution Stability in Dynamic, Distributed Constraint Optimization , 2007, IAT.

[23]  Barry O'Sullivan,et al.  Weighted Super Solutions for Constraint Programs , 2005, AAAI.

[24]  Boi Faltings,et al.  A Scalable Method for Multiagent Constraint Optimization , 2005, IJCAI.

[25]  Boi Faltings,et al.  S-DPOP: Superstabilizing, Fault-containing Multiagent Combinatorial Optimization , 2005, AAAI 2005.