Artificial Intelligence
Common LISP: the language
Real-time knowledge-based systems
AI Magazine
The advanced architectures project
AI Magazine
Clustering Algorithms
The Parallel Solution of Classification Problems
The Parallel Solution of Classification Problems
SACON: a knowledge-based consultant for structural analysis
SACON: a knowledge-based consultant for structural analysis
Experiments with a Knowledge-Based System on a Multiprocessor
Experiments with a Knowledge-Based System on a Multiprocessor
Rule Based Expert Systems: The Mycin Experiments of the Stanford Heuristic Programming Project (The Addison-Wesley series in artificial intelligence)
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We have developed a problem-solving framework, called ConClass, that is capable of classifying continuous real-time problems dynamically and concurrently on a distributed system. ConClass provides an efficient development environment for describing and decomposing a classification problem and synthesizing solutions. In ConClass, decomposed concurrent subproblems specified by the application developer effectively correspond to the actual distributed hardware elements. This scheme is useful for designing and implementing efficient distributed processing, making it easier to anticipate and evaluate system behavior. The ConClass system provides an object replication feature that prevents any particular object from being overloaded. In order to deal with an indeterminate amount of problem data, ConClass dynamically creates object networks that justify hypothesized solutions, and thus achieves a dynamic load distribution. A number of efficient execution mechanisms that manage a variety of asynchronous aspects of distributed processing have been implemented without using schedulers or synchronization schemes that are liable to develop bottlenecks. We have confirmed the efficiency of parallel distributed processing and load balancing of ConClass with an experimental application.