Distributed Artificial Intelligence (DAI) has existed as a subfield of AI for less than two decades. DAI is

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 ...

We are investigating techniques for developing distributed and adaptive collections of agents that coordinate to retrieve, filter and fuse information relevant to the user, task and situation, as well as anticipate a user's information needs. In our system of agents, information gathering is seamlessly integrated with decision support. The task for which particular information is requested of the agents does not remain in the user's head but it is explicitly represented and supported through agent collaboration. In this paper we present the distributed system architecture, agent collaboration interactions, and a reusable set of software components for constructing agents. We call this reusable multi-agent computational infrastructure RETSINA (Reusable Task Structure-based Intelligent Network Agents). It has three types of agents. Interface agents interact with the user receiving user specifications and delivering results. They acquire, model, and utilize user preferences to guide syste...

Design-to-time is an approach to problem-solving in resource-constrained domains where: multiple solution methods are available for tasks, those solution methods make tradeoffs in solution quality versus time, and satisficing solutions are acceptable. Design-to-time involves designing a solution to a problem that uses all available resources to maximize the solution quality within the available time. This paper defines the design-to-time approach in detail, contrasting it to the anytime algorithm approach, and presents a heuristic algorithm for designto -time real-time scheduling. Our blackboard architecture that implements the design-to-time approach is discussed and an example problem and solution from the Distributed Vehicle Monitoring Testbed (DVMT) is described in detail. Experimental results, generated using a simulation, show the effects of various parameters on scheduler performance. Finally we discuss future research goals and plans. 1 This work was partly supported by the Of...

The development of enabling infrastructure for the next generation of multi-agent systems consisting of large numbers of agents and operating in open environments is one of the key challenges for the multi-agent community. Current infrastructure support does not materially assist in the development of sophisticated agent coordination strategies. It is the need for and the development of such a high-level support structure that will be the focus of this paper. A domain-independent (generic) agent architecture is proposed that wraps around an agent’s problem-solving component in order to make problem-solving responsive to real-time constraints, available network resources and the need to coordinate — both in the large and small, with problem-solving activities of other agents. This architecture contains five components, local agent scheduling, multi-agent coordination, organizational design, detection and diagnosis and on-line learning, that are designed to interact so that a range of different situation-specific coordination strategies can be implemented and adapted as the situation evolves. The presentation of this architecture is followed by a more detailed discussion on the interaction among these components and the

Recent years are seeing an increasing need for on-line monitoring of teams of cooperating agents, e.g., for visualization, or performance tracking. However, in monitoring deployed teams, we often cannot rely on the agents to always communicate their state to the monitoring system. This paper presents

Hospital Patient Scheduling is an inherently distributed problem because of the way real hospitals are organized. As medical procedures have become more complex, and their associated tests and treatments have become interrelated, the current ad hoc patient scheduling solutions have been observed to break down [15]. We propose a multi-agent solution using the Generalized Partial Global Planning (GPGP) approach that preserves the existing human organization and authority structures, while providing better system-level performance (increased hospital unit throughput and decreased patient stay time). To do this, we extend GPGP with a new coordination mechanism to handle mutually exclusive resource relationships. Like the other GPGP mechanisms, the new mechanism can be applied to any problem with the appropriate resource relationship. We evaluate the this new mechanism in the context of the hospital patient scheduling problem, and examine the effect of increasing interrelations between task...

Global competition and rapidly changing customer requirements are forcing major changes in the production styles and configuration of manufacturing organizations. Traditional centralised manufacturing systems are not able to meet such requirements. This paper proposes an agent-based approach for dynamically creating and managing agent communities in such widely distributed and ever-changing manufacturing environments. After reviewing the research literature, an adaptive multi-agent manufacturing system architecture called MetaMorph is presented and its main features are described. Such architecture facilitates multi-agent coordination by minimising communication and processing overheads. Adaptation is facilitated through organizational structural change and two learning mechanisms: learning from past experiences and learning future agent interactions by simulating future dynamic, emergent behaviours. The MetaMorph architecture also addresses other specific requirements for next generat...

viewpoints. It is a tool for building and testing computational theories of coordination. TÆMS is compatible with both formal computational agent-centered approaches and experimental approaches. The framework allows us to both mathematically analyze (when possible) and quantitatively simulate the behavior of multi-agent systems with respect to interesting characteristics of the computational task environments of which they are part. We believe that it provides the correct level of abstraction for meaningfully evaluating centralized, parallel, and distributed control algorithms, negotiation strategies, and organizational designs. This chapter will briefly describe the TÆMS modeling framework for representing abstract task environments, concentrating particularly on its support for simulation. I will describe how to model each of several different multi-agent problem-solving environments, such as This work was supported by DARPA contract N0

The complexity of the modern information carrying landscape requires a sophisticated view in which information is acquired rather than simply retrieved; where the process must be dynamic, incremental, and constrained by resource limitations. In this paper, we describe a multi-agent architecture called MACRON based on the Cooperative Information Gathering (CIG) paradigm designed specifically for such complex information gathering environments. Top level user queries drive the creation of partially elaborated information gathering plans, resulting in the employment of multiple cooperative agents for the purpose of achieving goals and subgoals within those plans. Agents in MACRON incorporate capabilities to exploit subproblem interdependencies, manage uncertainty inherent in multi-agent search, intelligently trade-off solution quality for resource limitations and exploit or avoid redundancy as needed. 1 Introduction The complexity of the modern information carrying landscape requires a s...