Abstract
The growth in computer networks has created the potential to harness a great deal of computing power, but new models of distributed computing are often required. Cooperative distributed problem solving (CDPS) is the subfield of multi-agent systems (MAS) that is concerned with how large-scale problems can be solved using a network of intelligent agents working together. Building CDPS systems for real-world applications is still very difficult, however, in large part because the effects that domain and strategy characteristics have on the performance of CDPS systems are not well understood. This paper reports on the first results from a new simulation-based analysis system that has been created to study the performance of CDPS-based distributed sensor interpretation (DSI) and distributed diagnosis (DD). To demonstrate the kind of results that can be obtained, we have investigated how the monotonicity of a domain affects the performance of a potentially very efficient class of strategies for CDPS-based DSI/DD. Local solutions strategies attempt to limit communications among the agents by focusing on using the agents' local solutions to produce global solutions. While these strategies have been described as being important for effective CDPS-based DSI/DD, they need not perform well if a domain is nonmonotonic. We had previously suggested that the reason they have performed well in several research systems was that many DSI/DD domains are what we termed nearly monotonic. In this paper, we will provide quantitative results that relate the performance of local solutions strategies to the monotonicity of a domain. The experiments confirm that domain monotonicity can be important to consider, but they also show that it is possible for these strategies to be effective even when domains are relatively nonmonotonic. What is required is that the agents receive a significant fraction of the data that is relevant to their subproblems. This has important implications for the design of DSI/DD systems using local solutions strategies. In addition, while the work indicates that many DSI/DD domains are likely to be “nearly monotonic” according to our original definitions, it also shows that these measures are not as predictive of performance as other measures we define. This means that near monotonicity alone does not explain why local solutions strategies have performed well in previous systems. Instead, a likely explanation is that these systems typically involved only a small number of agents.
Similar content being viewed by others
References
Y. Bar-Shalom and T. Fortmann, Tracking and Data Association, Academic Press: Boston, MA, 1988.
R. Brooks and S. S. Iyengar, Multi-Sensor Fusion, Prentice Hall: Upper Saddle River, NJ, 1998.
N. Carver, Z. Cvetanovic, and V. Lesser, “Sophisticated cooperation in FA/C distributed problem solving systems, ” in Proceedings of AAAI-91, pp. 191–198, 1991.
N. Carver, V. Lesser, “The DRESUN testbed for research in FA/C distributed situation assessment: Extensions to the model of external evidence, ” in Proceedings of the International Conference on Multiagent Systems (ICMAS-95), pp. 33–40, 1995.
N. Carver, V. Lesser, and R. Whitehair, “Nearly monotonic problems: A key to effective FA/C Distributed sensor interpretation?, ” in Proceedings of AAAI-96, pp. 88–95, 1996.
N. Carver, F. Klassner, and V. Lesser, “Sensor interpretation in complex domains using RESUN: experiences with the IPUS sound understanding testbed, ” Technical Report 99–1, Computer Science Department, Southern Illinois University, http://cs.siu.edu/~carver, 1999.
E. Charniak and S. Shimony, “Cost-based abduction and MAP explanation, ” Artificial Intelligence, vol. 66, pp. 345–374, 1994.
K. Decker and V. Lesser, “Quantitative modeling of complex environments, ” International Journal of Intelligent Systems in Accounting, Finance, and Management, vol. 2, pp. 215–234, 1993.
K. Decker and V. Lesser, “Designing a family of coordination algorithms, ” in Proceedings of ICMAS-95, pp. 73–80, 1995.
E. Durfee and V. Lesser, “Incremental planning to control a time-constrained, blackboard-based problem solver, ” IEEE Transactions on Aerospace and Electronic Systems, vol. 24, no.5, pp. 647–662, 1988.
K. S. Fu, Syntactic Methods in Pattern Recognition, Academic Press: New York, NY, 1974.
D. Knuth, “Semantics of context-free languages, ” Mathematical Systems Theory, vol. 2, no.2, pp. 127–146, 1968.
K. Vipin and L. Kanal, “The CDP: A unifying formulation for heuristic search, dynamic programming, and branch-and-bound, ” in L. Kanal and V. Kumar, (eds.), Search in Artificial Intelligence, Springer-Verlag: New York, NY, 1988.
V. Lesser and L. Erman, “Distributed interpretation: A model and experiment, ” IEEE Transactions on Computers, vol. 29, no.12, pp. 1144–1163, 1980.
V. Lesser and D. Corkill, “Functionally accurate, cooperative distributed systems, ” IEEE Transactions on Systems, Man, and Cybernetics, vol. 11, no.1, pp. 81–96, 1981.
V. Lesser, “A retrospective view of FA/C distributed problem solving, ” IEEE Transactions on Systems, Man, and Cybernetics, Special Issue on Distributed Artificial Intelligence, vol. 21, no.6, pp. 1347–1362, 1991.
Z. Li and B. D'Ambrosio, “Efficient inference in Bayes nets as a combinatorial optimization problem, ” International Journal of Approximate Reasoning, vol. 11, no.1, pp. 55–81, 1994.
Y. Lin and M. Druzdzel, “Stochastic sampling and search in belief updating algorithms for very large Bayesian networks, ” Working Notes of the AAAI-1999 Spring Symposium on Search Techniques for Problem Solving Under Uncertainty and Incomplete Information, pp. 77–82, 1999.
M. Nadler and E. Smith, Pattern Recognition Engineering, John Wiley: New York, NY, 1993.
J. Pearl, Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference, Morgan Kaufmann: San Mateo, CA, 1988.
Y. Peng and J. Reggia, Abductive Inference Models for Diagnostic Problem-Solving, Springer-Verlag: New York, NY, 1990.
M. V. Nagendra Prasad, K. Decker, A. Garvey, and V. Lesser, “Exploring organizational design with TAEMS: A case study of distributed data processing, ” in Proceedings of ICMAS-96, pp. 283–290, 1996.
S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, Prentice Hall: Upper Saddle River, NJ, 1995.
S. Sen, “Predicting Tradeoffs in Contract-Based Distributed Scheduling, ” Ph.D. dissertation, University of Michigan, Department of Electrical Engineering and Computer Science, 1993.
S. Sen and E. Durfee, “Unsupervised surrogate agents and search bias change in flexible distributed scheduling, ” in Proceedings of the International Conference on Multiagent Systems (ICMAS-95), pp. 336–343, 1995.
S. Shimony, “Finding MAPs for belief networks is NP-hard, ” Artificial Intelligence, vol. 68, pp. 399–410, 1994.
R. Whitehair and V. Lesser, “A Framework for the analysis of sophisticated control in interpretation systems. ” Technical Report, 93–53, University of Massachusetts, Computer Science Department, http://dis.cs.umass.edu, 1993.
R. Whitehair, “A framework for the analysis of sophisticated control, Ph.D. dissertation, University of Massachusetts, Computer Science Department, 1996.
M. Wooldridge and N. Jennings, “Formalizing the cooperative problem solving process, ” in Proceedings of the Thirteenth International Workshop on DAI (DAI-94), 1994, pp. 403–417.
M. Wooldridge, “A knowledge-theoretic approach to distributed problem solving, ” in Proceedings of the 13th European Conference on Artificial Intelligence (ECAI98), pp. 308–312, 1998.
Y. Xiang, “A probabilistic framework for multi-agent distributed interpretation and optimization of communication, ” Artificial Intelligence, vol. 87, no.1–2, pp. 295–342, 1996.
M. Yokoo and E. Durfee, “Distributed search formalisms for distributed problem solving: Overview, ” in Proceedings of the Twelfth International Workshop on DAI (DAI-92), pp. 371–390, 1992.
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Carver, N., Lesser, V. Domain Monotonicity and the Performance of Local Solutions Strategies for CDPS-based Distributed Sensor Interpretation and Distributed Diagnosis. Autonomous Agents and Multi-Agent Systems 6, 35–76 (2003). https://doi.org/10.1023/A:1021713405822
Issue Date:
DOI: https://doi.org/10.1023/A:1021713405822