Coordination of Distributed Problem Solvers

1. Front Matter

   Pages i-ix

   PDF

2. Overview

   • Edmund H. Durfee

   Pages 1-26

3. Distributed Problem Solving and the DVMT

   • Edmund H. Durfee

   Pages 27-44

4. Identifying Local Goals Through Clustering

   • Edmund H. Durfee

   Pages 45-66

5. Planning Local Problem Solving

   • Edmund H. Durfee

   Pages 67-110

6. Local Planning: Experiments and Evaluation

   • Edmund H. Durfee

   Pages 111-130

7. Recognizing Partial Global Goals

   • Edmund H. Durfee

   Pages 131-158

8. Coordination Through Partial Global Planning

   • Edmund H. Durfee

   Pages 159-208

9. Partial Global Planning: Experiments and Evaluation

   • Edmund H. Durfee

   Pages 209-238

10. Conclusions

    • Edmund H. Durfee

    Pages 239-250

11. Back Matter

    Pages 251-269

    PDF

As artificial intelligence (AI) is applied to more complex problems and a wider set of applications, the ability to take advantage of the computational power of distributed and parallel hardware architectures and to match these architec­ tures with the inherent distributed aspects of applications (spatial, functional, or temporal) has become an important research issue. Out of these research concerns, an AI subdiscipline called distributed problem solving has emerged. Distributed problem-solving systems are broadly defined as loosely-coupled, distributed networks of semi-autonomous problem-solving agents that perform sophisticated problem solving and cooperatively interact to solve problems. N odes operate asynchronously and in parallel with limited internode commu­ nication. Limited internode communication stems from either inherent band­ width limitations of the communication medium or from the high computa­ tional cost of packaging and assimilating information to be sent and received among agents. Structuring network problem solving to deal with consequences oflimited communication-the lack of a global view and the possibility that the individual agents may not have all the information necessary to accurately and completely solve their subproblems-is one of the major focuses of distributed problem-solving research. It is this focus that also is one of the important dis­ tinguishing characteristics of distributed problem-solving research that sets it apart from previous research in AI.

• agents
• artificial intelligence
• autonom
• communication
• control
• intelligence
• knowledge
• problem solving
• simulation

• University of Massachusetts at Amherst, USA

  Edmund H. Durfee

Policies and ethics