Artificial Intelligence
Knowlege in action: logical foundations for specifying and implementing dynamical systems
Knowlege in action: logical foundations for specifying and implementing dynamical systems
History-based diagnosis templates in the framework of the situation calculus
AI Communications - Special issue on KI-2001
Diagnosis of discrete-event systems using satisfiability algorithms
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Highly dynamic adaptation in process management systems through execution monitoring
BPM'07 Proceedings of the 5th international conference on Business process management
DiscoverHistory: understanding the past in planning and execution
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Maintaining consistency in a robot's knowledge-base via diagnostic reasoning
AI Communications - Intelligent Engineering Techniques for Knowledge Bases
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The robot programming and plan language IndiGolog allows for on-line execution of actions and offline projections of programs in dynamic and partly unknown environments. Basic assumptions are that the outcomes of primitive and sensing actions are correctly modeled, and that the agent is informed about all exogenous events beyond its control. In real-world applications, however, such assumptions do not hold. In fact, an action's outcome is error-prone and sensing results are noisy. In this paper, we present a belief management system in IndiGolog that is able to detect inconsistencies between a robot's modeled belief and what happened in reality. The system furthermore derives explanations and maintains a consistent belief. Our main contributions are (1) a belief management system following a history-based diagnosis approach that allows an agent to actively cope with faulty actions and the occurrence of exogenous events; and (2) an implementation in IndiGolog and experimental results from a delivery domain.