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.

Inhalt
1 Overview.- 1.1 Partial Global Planning: A Unified Approach to Dynamic Coordination.- 1.2 Research Issues.- 1.3 Relationship to Previous Research.- 1.4 Guides for the Reader.- 2 Distributed Problem Solving and the DVMT.- 2.1 The Experimental Domain.- 2.2 The Problem-Solving Knowledge.- 2.3 Control of Problem Solving.- 2.4 Coordination and Organization of Nodes.- 2.5 Specifying Problem-Solving Environments.- 2.6 Network Simulation.- 2.7 Problem-Solving Examples.- 2.8 Limitations of the DVMT.- 2.9 How This Work Builds on the DVMT.- 3 Identifying Local Goals Through Clustering.- 3.1 Background.- 3.2 Overview.- 3.3 Details.- 3.4 Generalizing.- 4 Planning Local Problem Solving.- 4.1 Background.- 4.2 Overview.- 4.3 Details.- 4.4 Generalizing.- 5 Local Planning: Experiments and Evaluation.- 5.1 Local Planning Experiments.- 5.2 Local Planning Evaluation.- 6 Recognizing Partial Global Goals.- 6.1 Background.- 6.2 Overview.- 6.3 Details.- 6.4 Generalizing.- 7 Coordination Through Partial Global Planning.- 7.1 Background.- 7.2 Overview.- 7.3 Details.- 7.4 Generalizing.- 8 Partial Global Planning: Experiments and Evaluation.- 8.1 Partial Global Planning Experiments.- 8.2 Evaluation.- 9 Conclusions.- 9.1 Summary.- 9.2 Research Issues Revisited.- 9.3 Future Research Directions.- 9.4 Contributions.- Acknowledgments.