Abstract — Two forms of cooperation in distributed problem solving are considered: task-sharing and result-sharing. In the former, nodes assist each other by sharing the computational load for the execution of subtasks of the overall problem. In the latter, nodes assist each other by sharing partial results which are based on somewhat different perspectives on the overall problem. Different perspectives arise because the nodes use different knowledge sources (KS’s) (e.g., syntax versus acoustics in the case of a speech-understanding system) or different data (e.g., data that is sensed at different locations in the case of a distributed sensing system). Particular attention is given to control and to internode communication for the two forms of cooperation. For each, the basic methodology is presented and systems in which it has been used are described. The two forms are then compared and the types of applications for which they are suitable are considered. I. DISTRIBUTED PROBLEM SOLVING

Introduction Cooperative Distributed Problem-Solving (CDPS) studies how a loosely-coupled network of problem solvers can work together to solve problems that are beyond their individual capabilities. Each problem-solving node in the network is capable of sophisticated problem solving and can work independently, but the problems faced by the nodes cannot be completed without cooperation. Cooperation is necessary because no single node has sufficient expertise, resources, and information to solve a problem, and different nodes might have expertise for solving different parts of the problem. For example, if the problem is to design a house, one node might have expertise on the strength of structural materials, another on the space requirements for different types of rooms, another on plumbing, another on electrical wiring, and so on. Different nodes might have different resources: some might be very fast at computation, others might have connections that speed communication, whil

This paper is intended as a contribution to analysis of the implications of viewing offices as open systems. It takes a prescriptive stance on how to estalish the informationprocessing foundations for taking action and making decisions in office work from an open systems perspective. We propose due process as a central activity in organizational information processing. Computer systems are beginning to play important roles in mediating the ongoing activities of organizations. We expect that these roles will gradually increase in importance as computer systems take on more of the authority and responsibility for ongoing activities. At the same time, we expect computer systems to acquire more of the characteristics and structure of human organizations.

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For distributed sensor network applications, a practical approach to generating complete interpretations from distributed data must coordinate how separate, concurrently-running systems form, exchange, and fuse their individual hypotheses to form consistent interpretations. Partial global planning provides a framework for coordinating multiple AI systems that are cooperating in a distributed sensor network. By combining a variety of coordination techniques into a single, unifying framework, partial global planning enables separate AI systems to reason about their roles and responsibilities as part of group problem solving, and to modify their planned processing and communication actions to act as a more coherent team. Partial global planning is uniquely suited for coordinating systems that are working in continuous, dynamic, and unpredictable domains because it interleaves coordination with action and allows systems to make effective decisions despite incomplete and possibly obsolete i...

We investigate the semantics for Knowledge Query Manipulation Language (KQML) and we propose a semantic framework for the language. KQML is a language and a protocol to support communication between software agents. Based on ideas from speech act theory,we propose a semantic description for KQML that associates descriptions of the cognitive states of agents with the use of the language 's primitives (performatives). We use this approachto describe the semantics for the basic set of KQML performatives. We also investigate implementation issues related to our semantic approach. We suggest that KQML can o#er an all purpose communication language for software agents that requires no limiting pre-commitments on the agents' structure and implementation. KQML can provide the Distributed AI, Cooperative Distributed Problem Solving and Software Agents communities with an all purpose language and environment for intelligent inter-agent communication.

In the last few years we have witnessed a surge of business-to-consumer and business-to-business commerce operated on the Internet. However, most current electronic commerce systems are little more than electronic catalogues that allow a user to purchase a product under predetermined and inflexible terms and conditions. We believe that in the next few years we will see a new generation of electronic commerce systems emerge, based on automated negotiation. In this paper, we identify the main parameters on which any automated negotiation depends. To show the applicability of our classification framework, we use it to categorise a representative sample of some of the most prominent negotiation models that exist in the literature.

A Dissertation Presented by TUOMAS W. SANDHOLM

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...

. Adaptation in open, multi-agent information gathering systems is important for several reasons. These reasons include the inability to accurately predict future problem-solving workloads, future changes in existing information requests, future failures and additions of agents and data supply resources, and other future task environment characteristic changes that require system reorganization. We have developed a multi-agent distributed system infrastructure, Retsina (REusable Task Structure-based Intelligent Network Agents) that handles adaptation in an open Internet environment. Adaptation occurs both at the individual agent level as well as at the overall agent organization level. The Retsina system 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 system coordination in support of the user's tasks. Task agents help users perform tasks by formulating p...