In a multi-agent system, agents are carrying out certain tasks by executing plans. Consequently, the problem of finding a plan, given a certain goal, has been given a lot of attention in the literature. Instead of concentrating on this problem, the focus of this paper is on cooperation between agents which already have constructed plans for their goals. By cooperating, agents might reduce the number of actions they have to perform in order to fulfill their goals. The key idea is that in carrying out a plan an agent possibly produces side products that can be used as resources by other agents. As a result, an other agent can discard some of its planned actions. This process of exchanging products, called plan merging, results in distributed plans in which agents become dependent on each other, but are able to attain their goals more efficiently.

Many day-to-day situations involve decision making: for example, a taxi company has some transportation tasks to be carried out, a large firm has to distribute a lot of complicated tasks among its subdivisions or subcontractors, and an air-traffic controller has to assign time slots to planes that are landing or taking off. Intelligent agents can aid in

This paper presents an argumentation-based approach to deliberation, the process by which two or more agents reach a consensus on a course of action. The kind of deliberation we are interested in combines both the selection of an overall goal, the reduction of this goal into sub-goals, and the formation of a plan to achieve the overall goal. We develop a mechanism for doing this and then proceed to describe how it can be integrated into a system of argumentation to provide a sound and complete deliberation system, before showing how the same process can be achieved through a multi-agent dialogue.

Abstract. In multi-agent planning problems agents are requested to jointly solve a complex task consisting of a set of interrelated tasks. Since none of the agents is capable to solve the whole task on its own, usually each of them is assigned to a subset of tasks. If agents are dependent upon each other via interrelated tasks they are assigned to, moderately-coupled teams of agents are called for. Such teams solve the task by coordinating during or after planning and revising their plans if necessary. In this paper we show that such complex tasks also can be solved by looselycoupled teams of agents that are able to plan independently, although the computational complexity of the coordination problems involved is high. We also investigate some of the factors influencing this complexity. Key words: Multi-agent system, complex tasks, task assignment, planning, coordination, computational complexity.

Current-generation AI planning technology lacks effective, flexible techniques for managing the planning process in the presence of changing information, and for coordinating multiple, distributed planning processes. However, these issues have been the focus of theoretical work in AI over roughly the past five years, and similar issues have been studied by researchers developing distributed and real-time operating systems. The DIPART project is aimed at adapting relevant techniques developed in operating systems research, and combining them with techniques from real-time and distributed AI, to support plan generation in dynamic, multi-agent environments. Towards this end, we are building DIPART---the Distributed, Interactive Planner's Assistant for Real-time Transportation planning---a prototype simulation system that includes a network of agents, each of which assists a human planner, and a simulated dynamic environment, which implements Reece and Tate's Pacifica NEO scenario [34]. In...

Parallelization of image analysis tasks forms a basic key for processing huge image data in realtime. At this, suitable subtasks for parallel processing have to be extracted and mapped to components of a distributed system. Basically, this task should be done by the processing system and not by the user, as automatical parallelization allows a flexible resource management and reduces time for developing image analysis programs. This paper describes a multi-agent based system for planning and performing image analysis tasks within a distributed system. It illustrates a method for modeling image analysis tasks under the viewpoint of parallel processing and explains the special design requirements for parallelizing agents. Furthermore, we describe concepts for agent cooperation and for using the agent's ability of learning to allow long term improvement of its planning and scheduling strategies. The presented image analysis system allows an architecture-independent parallel processing of ...

Abstract. We consider task planning problems where a number of noncooperative agents have to work on a joint problem. Such a problem consists in completing a set of interdependent, hierarchically ordered tasks. Each agent is assigned a subset of tasks to perform for which it has to construct a plan. Since the agents are non-cooperative, they insist on planning independently and do not want to revise their individual plans when the joint plan has to be assemled from the individual plans. We present a formal framework to study some computational aspects of this non-cooperative coordination problem. 1

This paper proposes a new multiagent planning approach to coordination synthesis that views distributed agents as discrete-event processes. The connection between discreteevent control synthesis and coordination planning is first established, thereby enabling the exploitation of the vast body of knowledge and associated software synthesis tools from `The Supervisory Control of Discrete-Event Systems' for automatic coordination synthesis of distributed agents. Importantly, these coordinating agents designed collectively generate a behaviour guaranteed not to contradict any specified inter-agent constraint, is nonblocking and optimal. A simple planning methodology is proposed in terms of procedures supported by CTCT, an existing, freely available design tool developed based on the control synthesis framework. A simple example illustrates the use of the CTCT-based methodology to synthesize coordination modules for distributed agents. Discussions in relation to previous work examine the relative significance of the new multiagent planning framework.

We consider the problem of coordinating autonomous agents that have to perform a joint task. This joint task consists of a set of elementary tasks, partially ordered by a set of precedence constraints. Each agent is assigned (using some given task allocation protocol) a subset of the available tasks. We assume that agents wish to be fully independent during the planning process, yet they are dependent on each other because of the precedences between tasks allocated to di#erent agents.

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