Distributed Constraint Reasoning under Unreliable Communication

We investigate how algorithms for Distributed Constraint Reasoning (DCR) can be modified to operate effectively over unreliable communication infrastructure. While DCR algorithms typically assume that communication is perfect, this assumption is problematic because unreliable communication is a common feature of many real-world multiagent domains. Limited bandwidth, interference, loss of line-of-sight are some reasons why communication can fail. We introduce a novel method for dealing with message loss in the context of a particular DCR algorithm named Adopt. The key idea in our approach is to let the DCR algorithm inform the lower error-correction software layer which key messages are important and which can be lost without significant problems. This allows the algorithm to flexibly and robustly deal with message loss. Results show that with a few modifications, Adopt can be guaranteed to terminate with the optimal solution even in the presence of message loss and that time to solution degrades gracefully as message loss probability increases. The results also suggest that artificially introducing message loss even when communication infrastructure is reliable could be beneficial in terms of the amount of work agents need to do to find the optimal solution.

[1]  Victor R. Lesser,et al.  Cooperative negotiation for soft real-time distributed resource allocation , 2003, AAMAS '03.

[2]  Makoto Yokoo.  Distributed constraint satisfaction: foundation and applications , 1998, Proceedings International Conference on Multi Agent Systems (Cat. No.98EX160).

[3]  Wei-Min Shen,et al.  Applying Constraint Reasoning to Real-world Distributed Task Allocation , 2002 .

[4]  Anthony Barrett,et al.  Enabling Efficient Conflict Resolution in Multiple Spacecraft Missions via DCSP , 2002 .

[5]  Christian Bessiere,et al.  Distributed Dynamic Backtracking , 2001, CP.

[6]  Victor Lesser,et al.  A Mediation Based Protocol for Distributed Constraint Satisfaction , 2003 .

[7]  相場 亮,et al.  Distributed Constraint Satisfaction : Foundations of Cooperation in Multi-Agent Systems, Mokoto Yokoo (著), Springer-Verlag, ISBN3540675965 , 2001 .

[8]  Makoto Yokoo,et al.  Distributed Constraint Satisfaction: Foundations of Cooperation in Multi-agent Systems , 2000 .

[9]  Marius-Calin Silaghi,et al.  Asynchronous Search with Aggregations , 2000, AAAI/IAAI.

[10]  Milind Tambe,et al.  Distributed Sensor Networks: A Multiagent Perspective , 2003 .

[11]  W. Richard Stevens.  Tcp/ip illustrated- volume 1 , 1994 .

[12]  Weixiong Zhang,et al.  Distributed breakout revisited , 2002, AAAI/IAAI.

[13]  Sebastian Müller,et al.  Distributed constraint optimization for medical appointment scheduling , 2001, AGENTS '01.

[14]  Leslie Lamport,et al.  Distributed snapshots: determining global states of distributed systems , 1985, TOCS.

[15]  Amnon Meisels,et al.  Comparing performance of distributed constraints process ing algorithms , 2002 .

[16]  Makoto Yokoo,et al.  Distributed Partial Constraint Satisfaction Problem , 1997, CP.

[17]  Makoto Yokoo,et al.  Distributed constraint satisfaction algorithm for complex local problems , 1998, Proceedings International Conference on Multi Agent Systems (Cat. No.98EX160).

[18]  Srinivasan Seshan,et al.  A comparison of mechanisms for improving TCP performance over wireless links , 1996, SIGCOMM 1996.

[19]  Bhaskar Krishnamachari,et al.  Communication and Computation in Distributed CSP Algorithms , 2002, CP.

[20]  Makoto Yokoo,et al.  An asynchronous complete method for distributed constraint optimization , 2003, AAMAS '03.

[21]  Nancy A. Lynch,et al.  Distributed Algorithms , 1992, Lecture Notes in Computer Science.

[22]  Donald F. Towsley,et al.  Modeling TCP throughput: a simple model and its empirical validation , 1998, SIGCOMM '98.