Real-time knowledge-based systems
AI Magazine
Representing and using organizational knowledge in DAI systems
Distributed Artificial Intelligence (Vol. 2)
Methods and effectiveness of parallel rule firing
Proceedings of the sixth conference on Artificial intelligence applications
A framework for organizational self-design in distributed problem solving networks
A framework for organizational self-design in distributed problem solving networks
Intelligent monitoring and control
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
Using partial global plans to coordinate distributed problem solvers
IJCAI'87 Proceedings of the 10th international joint conference on Artificial intelligence - Volume 2
SIREDOJ: a legal assistance application about contracts in the building industry
ICAIL '91 Proceedings of the 3rd international conference on Artificial intelligence and law
Control issues in parallel rule-firing production systems
AAAI'91 Proceedings of the ninth National conference on Artificial intelligence - Volume 1
Computational & Mathematical Organization Theory
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Recently-developed techniques have improved the performance of production systems several times over. However, these techniques are not yet adequate for continuous problem solving in a dynamically changing environment. To achieve adaptive real-time performance in such environments, we use an organization of distributed production system agents, rather than a single monolithic production system, to solve problems. Organization self-design is performed to satisfy real-time constraints and to adapt to changing resource requirements. When overloaded, individual agents decompose themselves to increase parallelism, and when the load lightens the agents compose with each other to free hardware resources. In addition to increased performance, generalizations of our composition/ decomposition approach provide several new directions for organization self-design, a pressing concern in Distributed AI.