A blackboard architecture for control
Artificial Intelligence
Actors: a model of concurrent computation in distributed systems
Actors: a model of concurrent computation in distributed systems
Object-oriented concurrent programming ABCL/1
OOPLSA '86 Conference proceedings on Object-oriented programming systems, languages and applications
Understanding Software Maintenance Work
IEEE Transactions on Software Engineering
Distributed Artificial Intelligence
Distributed Artificial Intelligence
Distributed Artificial Intelligence (Vol. 2)
Distributed Artificial Intelligence (Vol. 2)
The integration of computing and routine work
ACM Transactions on Information Systems (TOIS) - Special issue: selected papers from the conference on office information systems
ACM Transactions on Information Systems (TOIS) - Special issue: selected papers from the conference on office information systems
Using partial global plans to coordinate distributed problem solvers
IJCAI'87 Proceedings of the 10th international joint conference on Artificial intelligence - Volume 2
Pengi: an implementation of a theory of activity
AAAI'87 Proceedings of the sixth National conference on Artificial intelligence - Volume 1
What have we learnt from using real parallel machines to solve real problems?
C3P Proceedings of the third conference on Hypercube concurrent computers and applications - Volume 2
MACE: High-level distributed objects in a flexible testbed for distributed AI research
OOPSLA/ECOOP '88 Proceedings of the 1988 ACM SIGPLAN workshop on Object-based concurrent programming
An expert system for chemical structure elucidation implemented on a blackboard
IEA/AIE '90 Proceedings of the 3rd international conference on Industrial and engineering applications of artificial intelligence and expert systems - Volume 1
A distributed task environment for teaching artificial intelligence with agents
Proceedings of the 35th SIGCSE technical symposium on Computer science education
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Research in AI is slowly maturing, and body of accepted techniques for reasoning and for representing knowledge in simple, circumscribed domains now exists. But with the maturity of AI has come a growing awareness of the severe limitations of current techniques for constructing more complex problem solving or interpretation systems. We currently have inadequate means to gather, represent, store, organize, access, and manipulate the huge collections of knowledge required for complex problem solving. Existing systems can't reconfigure themselves in changing situations, nor can they incrementally adjust to new knowledge or new techniques. Large scale problem solvers (e.g. factory automation systems) cannot in principle completely model the world in which they exist, and must face problems of inconsistency, asynchrony, control and geographic distribution, etc. — they will have to work in “open systems.”Many solutions under consideration rely on concurrent computation, using either very fine grained “connectionist,” “neural computing” or “data parallel” approaches, or using larger grain collections of “objects,” “agents,” or “problem solving nodes” — techniques collectively termed “Distributed AI.” In this paper we characterize the needs for concurrency and parallelism in AI, with special attention to building medium to large grain adaptive problem solvers in open systems. In these systems the overriding concern is organizing the problem solving system's behavior — the “coordination problem.” Conventional distributed computing and parallel algorithms approaches allow a programmer to solve the coordination problem, and provide language constructs and concurrency control mechanisms with which a program can enact his solution. In Distributed AI, we attempt to improve adaptability by designing problem solvers which can both solve the coordination problem and enact the solution themselves.