Communications of the ACM
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
On agent-based software engineering
Artificial Intelligence
Multi-Agent Systems: An Introduction to Distributed Artificial Intelligence
Multi-Agent Systems: An Introduction to Distributed Artificial Intelligence
Privacy: The Achilles Heel of Pervasive Computing?
IEEE Pervasive Computing
Ubiquitous Computing in the Automotive Domain (Abstract)
Pervasive '02 Proceedings of the First International Conference on Pervasive Computing
The Belief-Desire-Intention Model of Agency
ATAL '98 Proceedings of the 5th International Workshop on Intelligent Agents V, Agent Theories, Architectures, and Languages
A new approach to the BDI agent-based modeling
Proceedings of the 2004 ACM symposium on Applied computing
IEA/AIE'2005 Proceedings of the 18th international conference on Innovations in Applied Artificial Intelligence
A hybrid logic for commonsense spatial reasoning
AI*IA'05 Proceedings of the 9th conference on Advances in Artificial Intelligence
Commonsense spatial reasoning for context–aware pervasive systems
LoCA'05 Proceedings of the First international conference on Location- and Context-Awareness
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This paper illustrates a conceptual framework for the development of Monitoring and Control Systems (MCS), based on a four level agent-based architecture. Traditional MCS are designed according to a three–level architectural pattern, in which intelligent devices are usually devoted to evaluate if data acquired by a set of sensors could be interpreted as anomalous or not. Possible mistakes in the evaluation process, due to faulty sensors or external factors, can cause the generation of undesirable false alarms. To solve this problem, our framework introduces a fourth level to the traditional MCS architecture, named correlation level, where an intelligent module, usually a Knowledge–Based System, collects the local interpretations made by each evaluation device building a global view of the monitored field. In this way, possible local mistakes are identified by the comparison with other local interpretations. The framework has been adopted for the development of Automotive MCSs.