distributed artificial intelligence (DAI) systems in which multiple problem-solving agents cooperate to achieve a common objective is a rapidly emerging and promising technology. However, as yet, there have been relat...
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distributed artificial intelligence (DAI) systems in which multiple problem-solving agents cooperate to achieve a common objective is a rapidly emerging and promising technology. However, as yet, there have been relatively few reported cases of such systems being employed to tackle real-world problems in realistic domains. One of the reasons for this is that DAI researchers have given virtually no consideration to the process of incorporating pre-existing systems into a community of cooperating agents. Yet reuse is a primary consideration for any organisation with a large software base. To redress the balance, this paper reports on an experiment undertaken at the CERN laboratories in which two pre-existing and standalone expert systems for diagnosing faults in a particle accelerator were transformed into a community of cooperating agents. The experiences and insights gained during this process provide a valuable first step towards satisfying the needs of potential users of DAI technology-identifying the types of changes required for cooperative problem solving, quantifying the effort involved in transforming standalone systems to ones suitable for cooperation and highlighting the benefits of a cooperating systems approach in a realistic industrial application.
distributed artificial intelligence is the subarea of AI that uses multiple processors. It has also been referred to as (cooperative) distributed problem solving. Cooperation is necessary because no node has sufficien...
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distributed artificial intelligence is the subarea of AI that uses multiple processors. It has also been referred to as (cooperative) distributed problem solving. Cooperation is necessary because no node has sufficient expertise, information or resources to solve the given problem. DAI studies intelligent coordination. There are several approaches to the achievement of coordination. The paper discusses distributed intelligent systems, and compares them with distributed processing and single agent problem solving systems. The systems are classified on the basis of different strategies for cooperation, and an algebraic style notation is proposed to describe a DIS precisely. This notation can also be used as input to or output from a machine. Certain essential features common to all DISs are also identified.
The objects of this study are to discuss an intelligent solution of dynamic unit-assignment problems in the decentralized cooperative operation for a new type of chemical batch plant, i.e. a pipeless batch plant, and ...
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The objects of this study are to discuss an intelligent solution of dynamic unit-assignment problems in the decentralized cooperative operation for a new type of chemical batch plant, i.e. a pipeless batch plant, and to propose a decentralized cooperative method of multiple-criteria decision-making in process planning. For these purposes, two techniques of distributed artificial intelligence (DAI) and the Analytic Hierarchy Process (AHP) are applied. Negotiation is realized by using message passing based on the contract net model, and a method of decentralized dynamic unit-assignment makes local scheduling possible. By integrating such multiple local schedules, cooperative operation and management of the whole plant may be achieved. The AHP is applied to the decision-making in selecting a suitable unit among alternative ones. Multiple functional units, such as process managers, reactors and stations, make game-like decisions based on multiple criteria of static evaluation and dynamic evaluation. Such a system is proposed to realize the flexibility and adaptability required for future chemical batch plants, rather than optimality.
A formal framework is presented that models communication and promises in multi-agent interactions. This framework generalizes previous work on co-operation without communication (Genesereth et al. 1984a, Genesereth e...
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A formal framework is presented that models communication and promises in multi-agent interactions. This framework generalizes previous work on co-operation without communication (Genesereth et al. 1984a, Genesereth et al. 1986), and shows the ability of communication to resolve conflicts among agents having disparate goals. Using a one-phase deal-making mechanism, agents are able to coordinate and cooperate more easily than in the communication-free model. In addition, there are certain types of interactions where communication makes possible mutually beneficial activity that is otherwise impossible to coordinate.
Coordination is a crucial behavior in cooperative distributed problem solving (CDPS). Analyzing coordination requires an understanding of the interplay between the agents, their problem, and their environment. The cor...
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distributed artificial intelligence attempts to integrate and coordinate the activities of multiple, intelligent problem solvers that come together to solve complex tasks in domains such as design, medical diagnosis, ...
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Knoblock and Korf have determined that abstraction can reduce search at a single agent from exponential to linear complexity (Knoblock 1991;Korf 1987). We extend their results by showing how concurrent problem solving...
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This article outlines, through a number of examples, a method that can be used by autonomous agents to decide among potential messages to send to other agents, without having to assume that a message must be truthful ...
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This paper reports on the design and development of a novel distributed Al-based framework for the automatic failure recovery of Flexible Production Systems (FPS). A distributed frame-based knowledge representation sc...
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ISBN:
(纸本)0444814841
This paper reports on the design and development of a novel distributed Al-based framework for the automatic failure recovery of Flexible Production Systems (FPS). A distributed frame-based knowledge representation scheme of the physical production system, the production process and the products being processed is used by a set of intelligent control agents with local perception and action capabilities to recover a system through cooperation. The recovery process is being carried out in an optimal way by optimizing numerically in a decentralised fashion, the trajectory from the error state to a required safe state.
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