Analyzing the Need for Meta-Level Communication

In naturally distributed, homogeneous, cooperative problem solving environments where welldefined tasks arrive at multiple locations, decisions must be made about the extent of, and overlap between, each agent’s area of responsibility—the agents’ organization. The organization may be constructed statically by a system designer, or dynamically by the agents during problem solving. No one organization is optimal across environments or even specific problem solving instances [6, 7, 8]. This paper presents an analysis of static and dynamic organizational structures for this class of environments, exemplified by distributed sensor networks. We first show how the performance of any static organization can be statistically described, and then show under what conditions dynamic organizations do better and worse than static ones. Finally, we show how the variance in the agents’ performance leads to uncertainty about whether a dynamic organization will perform better than a static one given only agent a priori expectations. In these cases, we show when meta-level communication about the actual state of problem solving will be useful to agents in constructing a dynamic organizational structure that outperforms a static one. Viewed in its entirety, this paper also presents a methodology for answering questions about the design of distributed problem solving systems by analysis and simulation of the characteristics of a complex environment rather than by relying on single-instance examples. Portions of this technical report appeared in the proceedings of IJCAI-93 ‘An approach to analyzing the need for meta-level communication’, and AAAI-93 ‘A one-shot dynamic coordination algorithm for distributed sensor networks’. This work was supported by DARPA contract N00014-92-J-1698, Office of Naval Research contract N00014-92-J-1450, and NSF contract CDA 8922572. The content of the information does not necessarily reflect the position or the policy of the Government and no official endorsement should be inferred.

[1]  Charles J. Petrie An Approach to Modeling Environment and Task Characteristics for Coordination , 1992 .

[2]  Randall Davis,et al.  Negotiation as a Metaphor for Distributed Problem Solving , 1988, Artificial Intelligence.

[3]  Victor Lesser,et al.  An approach to modeling environment and task characteristics for coordination , 1992 .

[4]  Yoav Shoham,et al.  AGENT0: A Simple Agent Language and Its Interpreter , 1991, AAAI.

[5]  Daniel D. Corkill,et al.  THE DISTRIBUTED VEHICLE MONITORING TESTBED , 1983 .

[6]  Edmund H. Durfee,et al.  Coordination as distributed search in a hierarchical behavior space , 1991, IEEE Trans. Syst. Man Cybern..

[7]  J. Kleijnen Statistical tools for simulation practitioners , 1986 .

[8]  Jasmina Pavlin,et al.  Predicting the Performance of Distributed Knowledge-Based Systems: A Modeling Approach , 1983, AAAI.

[9]  T. Moe The New Economics of Organization , 1984 .

[10]  Victor R. Lesser,et al.  Generalizing the Partial Global Planning Algorithm , 1992, Int. J. Cooperative Inf. Syst..

[11]  Victor R. Lesser,et al.  Quantitative Modeling of Complex Computational Task Environments , 1993, AAAI.

[12]  A. Stinchcombe Information and Organizations , 2019 .

[13]  Hector J. Levesque,et al.  Intention is Choice with Commitment , 1990, Artif. Intell..

[14]  Victor Lesser,et al.  Analyzing a quantitative coordination relationship , 1993 .

[15]  Edmund H. Durfee,et al.  Coherent Cooperation Among Communicating Problem Solvers , 1987, IEEE Transactions on Computers.