Modeling reactive behaviour in vertically layered agent architectures
ECAI-94 Proceedings of the workshop on agent theories, architectures, and languages on Intelligent agents
Coordination techniques for distributed artificial intelligence
Foundations of distributed artificial intelligence
Distributed models for decision support
Multiagent systems
Asynchronous Weak-commitment Search for Solving Distributed Constraint Satisfaction Problems
CP '95 Proceedings of the First International Conference on Principles and Practice of Constraint Programming
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
Independent agents for urban traffic control problem with mobile-agent coordination
ACS'07 Proceedings of the 7th Conference on 7th WSEAS International Conference on Applied Computer Science - Volume 7
Configuring Systems of Massively Distributed, Autonomous and Interdependent Decision Makers
International Journal of Decision Support System Technology
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This paper presents the experimental TRYSA2 distributed decision support system, that has been designed for the management of the urban motorway network around Barcelona. It shows how different technologies in the area of intelligent agents can be combined to solve this real-world decision support problem: on the one hand, a pre-established distribution of the loci of decision-making and the nature of local traffic management tasks, suggested to apply "deliberate" problem-solving agents; on the other, the complexity of the coordination task called for an "emergent" approach, in order that the overall decision support functionality be the result of non-benevolent agent interactions. The paper sets out from a description of our particular traffic management problem. Subsequently, the architecture of TRYSA2 is outlined, pointing out the design strategy followed and describing how the different design steps have been realised. Finally, we discuss the lessons learnt from building this multiagent application.