Many researchers have shown that there is no single best organization or coordination mechanism for all environments. This paper discusses the design and implementation of an extendable family of coordination mechanisms, called Generalized Partial Global Planning (GPGP). The set of coordination mechanisms described here assists in scheduling activities for teams of cooperative computational agents. The GPGP approach has several unique features. First, it is not tied to a single domain. Each mechanism is defined as a response to certain features in the current task environment. We show that different combinations of mechanisms are appropriate for different task environments. Secondly, the approach works in conjunction with an agent's existing local planner/scheduler. Finally, the initial set of five mechanisms presented here generalizes and extends the Partial Global Planning (PGP) algorithm. In comparison to PGP, GPGP allows more agent heterogeneity, it exchanges less global ...

Coordination, the process by which an agent reasons about its local actions and the (anticipated) actions of others to try and ensure the community acts in a coherent manner, is perhaps the key problem of the discipline of Distributed Artificial Intelligence (DAI). In order to make advances it is important that the theories and principles which guide this central activity are uncovered and analysed in a systematic and rigourous manner. To this end, this paper models agent communities using a distributed goal search formalism, and argues that commitments (pledges to undertake a specific course of action) and conventions (means of monitoring commitments in changing circumstances) are the foundation of coordination in all DAI systems. 1. The Coordination Problem Participation in any social situation should be both simultaneously constraining, in that agents must make a contribution to it, and yet enriching, in that participation provides resources and opportunities which would otherwise ...

The Functionally-Accurate, Cooperative (FA/C) paradigm provides a model for task decomposition and agent interaction in a distributed problem-solving system. In this model, agents need not have all the necessary information locally to solve their subproblems, and agents interact through the asynchronous, co-routine exchange of partial results. This model leads to the possibility that agents may behave in an uncoordinated manner. This paper traces the development of a series of increasingly sophisticated cooperative control mechanisms for coordinating agents. They include integrating data- and goal-directed control, using static meta-level information specified by an organizational structure, and using dynamic meta-level information developed in partial global planning. The framework of distributed search motivates these developments. Major themes of this work are the importance of sophisticated local control, the interplay between local control and cooperative control, and the use of s...

Coordination, as the act of managing interdependencies between activities, is one of the central research issues in Distributed Artificial Intelligence. Many researchers have shown that there is no single best organization or coordination mechanism for all environments. Problems in coordinating the activities of distributed intelligent agents appear in many domains: the control of distributed sensor networks; multi-agent scheduling of people and/or machines; distributed diagnosis of errors in local-area or telephone networks; concurrent engineering; `software agents' for information gathering. The design of coordination mechanisms for group...

Automating routine organizational tasks, such as meeting scheduling, requires a careful balance between the individual (respecting his or her privacy and personal preferences) and the organization (making efficient use of time and other resources). We argue that meeting scheduling is an inherently distributed process, and that negotiating over meetings can be viewed as a distributed search process. Keeping the process tractable requires introducing heuristics to guide distributed schedulers' decisions about what information to exchange and whether or not to propose the same tentative time for several meetings. While we have intuitions about how such heuristics could affect scheduling performance and efficiency, verifying these intuitions requires a more formal model of the meeting schedule problem and process. We present our preliminary work toward this goal, as well as experimental results that validate some of the predictions of our formal model. We also investigate scheduling in ove...

Abstract. Distributed problem solving involves the collective effort of multiple problems solvers to combine their knowledge, information, and capabilities so as to develop solutions to problems that each could not have solved as well (if at all) alone. The challenge in distributed problem solving is thus in marshalling the distributed capabilities in the right ways so that the problem solving activities of each agent complement the activities of the others, so as to lead efficiently to effective solutions. Thus, while working together leads to distributed problem solving, there is also the distributed problem of how to work together that must be solved. We consider that problem to be a distributed planning problem, where each agent must formulate plans for what it will do that take into account (sufficiently well) the plans of other agents. In this paper, we characterize the variations of distributed problem solving and distributed planning, and summarize some of the basic techniques that have been developed to date. 1

Complex, real-world domains require a rethinking of traditional approaches to AI planning. Planning and executing the resulting plans in a dynamic environment requires a continual approachinwhich planning and execution are interleaved, there may be uncertaintyin the current and projected world state, and replanning may be required when the situation changes or planned actions fail. Furthermore, complex planning and execution problems may require multiple computational agents and human planners to collaborate on a solution. In this article, we describe a new paradigm for planning in complex, dynamic environments, whichweterm distributed,continual planning (DCP). We argue that developing DCP systems will be necessary in order for planning applications to be successful in these environments. We give a historical overview of research leading up to the current state of the art in DCP, and describe research in distributed and continual planning. The increasing emphasis on r...

The specific organization used by a multi-agent system is crucial for its effectiveness and efficiency. In dynamic environments, or when the objectives of the system shift, the organization must therefore be able to change as well. In this abstract we propose using a general diagnosis engine to drive this process of adaptation, using the TMS modeling language as the primary representation of organizational information. A complete version of this paper is at [1]. As the sizes of multi-agent systems grow in the number of their participants, the organization of those agents will be increasingly important. In such an environment, an organization is used to limit the range of control decisions agents must make, which is a necessary component of scalable systems. Are agent agents arranged in clusters, a hierarchy, a graph, or some other type of organization? Are the agents` activities or behaviors driven solely by local concerns, or do external peers or managers have direct influence as wel...

Scientific research and practice in multiagent systems focuses on constructing computational frameworks, principles, and models for how both small and large societies of intelligent, semiautonomous agents can interact effectively to achieve their goals. This article provides a personal view of the key application areas for cooperative multiagent systems, the major intellectual problems in building such systems, the underlying principles governing their design, and the major directions and challenges for future developments in this field. Index Terms---Multiagent systems, coordination, cooperation, distributed problem solving, distributed artificial intelligence, computational organizations. ------------------------------ ###p### ------------------------------ 1INTRODUCTION ULTIAGENT systems are computational systems in which two or more agents interact or work together to perform some set of tasks or to satisfy some set of goals. These systems may be comprised of homogeneous o...

Uncertain and complex environments demand that an agent be able to anticipate the actions of others in order to avoid resource conflicts with them and to realize its goals. Conflicts during plan execution can be avoided by reducing or eliminating interactions by localizing plan effects to particular agents and by merging/coordinating the individual plans of agents by introducing synchronization actions. We describe a method for coordinating plans at abstract levels that takes advantage of hierarchical representations of plan information and that retains the flexibility of plans used in robust plan execution systems such as procedural reasoning systems (PRS). In order to coordinate at abstract levels in plan hierarchies, information about how abstract plans can be refined must be available in order to identify and avoid potential conflicts. We address this by providing procedures for deriving summary information for non-primitive plans that capture the external preconditions and effects...