Artificial Intelligence
Design problem solving: knowledge structures and control strategies
Design problem solving: knowledge structures and control strategies
Task-structure analysis for knowledge modeling
Communications of the ACM - Special issue on analysis and modeling in software development
KADS: a modelling approach to knowledge engineering
Knowledge Acquisition - Special issue on the KADS approach to knowledge engineering
Foundations of distributed artificial intelligence
ARCHON: a distributed artificial intelligence system for industrial application
Foundations of distributed artificial intelligence
Processing production rules in DEVICE, an active knowledge base system
Data & Knowledge Engineering
Distributed models for decision support
Multiagent systems
A Scenario-Based Design Method and an Environment for the Development of Multiagent Systems
Proceedings of the First Australian Workshop on DAI: Distributed Artificial Intelligence: Architecture and Modelling
Social Structure in Artificial Agent Societies: Implications for Autonomous Problem-Solving Agents
ATAL '98 Proceedings of the 5th International Workshop on Intelligent Agents V, Agent Theories, Architectures, and Languages
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This paper presents the knowledge model of a distributed decision support system, that has been designed for the management of a national network in Ukraine. It shows how advanced Artificial Intelligence techniques (multiagent systems and knowledge modelling) have been applied to solve this real-world decision support problem: on the one hand its distributed nature, implied by different loci of decision-making at the network nodes, suggest the application of a multiagent solution; on the other, due to the complexity of problem-solving for local network administration, it was useful to apply knowledge modelling techniques, in order to structure the different knowledge types and reasoning processes involved. The paper sets out from a description of our particular management problem. Subsequently, our agent model is described, pointing out the local problem-solving and coordination knowledge models. Finally, the dynamics of the approach is illustrated by an example.