Integrating High-Level and Detailed Agent Coordination into a Layered Architecture

Coordination, which is the process that an agent reasons about its local actions and the (anticipated) actions of others to try to ensure the community acts in a coherent fashion, is an important issue in multi-agent systems. Coordination is a complicated process that typically consists of several operations: exchanging local information; detecting interactions; deciding whether or not to coordinate; proposing, analyzing, refining and forming commitments; sharing results, and so on. We argue that facets of these different operations can be separated and bundled into two different layers.The lowerlayer pertains to feasibility and implementation operations, i.e., the detailed analysis of candidate tasks and actions, the formation of detailed temporal/resource-specific commitments between agents, and the balancing of non-local and local problem solving activities. In contrast, the upper-layer pertains to domain specific coordination tasks such as the formation of high-level goals and objectives for the agent, and decisions about whether or not to coordinate with other agents to achieve particular goals or bring about particular objectives. Detailed domain state is used at this level to make these high-level coordination decisions. In contrast, decisions at the lower-level do not need to reason about this detailed domain state. However, reasoning about detailed models of the performance characteristics of activities, such as their temporal scope, quality, affects of resource usage on performance, is necessary at this level. In this view, the layers are interdependent activities that operate asynchronously.

[1]  Michael E. Bratman,et al.  Intention, Plans, and Practical Reason , 1991 .

[2]  Anand S. Rao,et al.  Modeling Rational Agents within a BDI-Architecture , 1997, KR.

[3]  Victor R. Lesser,et al.  Sophisticated Cooperation in FA/C Distributed Problem Solving Systems , 1991, AAAI.

[4]  Nicholas R. Jennings,et al.  Specification and Implementation of a Belief Desire-Joint_intention Architecture for Cooperative Problem Solving , 1993, Int. J. Cooperative Inf. Syst..

[5]  Victor Lesser,et al.  Quantitative Modeling of Complex Environments , 1993 .

[6]  Victor R. Lesser,et al.  Designing a Family of Coordination Algorithms , 1997, ICMAS.

[7]  Nicholas R. Jennings,et al.  Foundations of distributed artificial intelligence , 1996, Sixth-generation computer technology series.

[8]  Nicholas R. Jennings,et al.  Coordination techniques for distributed artificial intelligence , 1996 .

[9]  Sarit Kraus,et al.  Collaborative Plans for Complex Group Action , 1996, Artif. Intell..

[10]  Milind Tambe,et al.  Agent Architectures for Flexible , 1997 .

[11]  Leon J. Osterweil,et al.  Specifying Coordination in Processes Using Little-JIL , 1998 .

[12]  V. Lesser,et al.  BIG: A Resource-Bounded Information Gathering Agent , 1998, AAAI/IAAI.

[13]  Michael J. Prietula,et al.  Simulating organizations: computational models of institutions and groups , 1998 .

[14]  Victor R. Lesser,et al.  Criteria-directed task scheduling , 1998, Int. J. Approx. Reason..

[15]  Keith S. Decker,et al.  Task environment centered simulation , 1998 .

[16]  Victor R. Lesser,et al.  Relating Quantified Motivations for Organizationally Situated Agents , 1999, ATAL.

[17]  Leon J. Osterweil,et al.  Coordinating agent activities in knowledge discovery processes , 1999 .

[18]  Leon J. Osterweil,et al.  Coordinating agent activities in knowledge discovery processes , 1999, WACC '99.

[19]  Thomas Wagner,et al.  Toward Generalized Organizationally Contexted Agent Control , 1999 .

[20]  Victor R. Lesser,et al.  Investigating Interactions between Agent Conversations and Agent Control Components , 2000, Issues in Agent Communication.