Artificial Intelligence
A theory of diagnosis from first principles
Artificial Intelligence
Monte-Carlo approximation algorithms for enumeration problems
Journal of Algorithms
Abductive inference models for diagnostic problem-solving
Abductive inference models for diagnostic problem-solving
A spectrum of logical definitions of model-based diagnosis
Computational Intelligence
Readings in model-based diagnosis
Readings in model-based diagnosis
Characterizing diagnoses and systems
Artificial Intelligence
Neural Computation
Building problem solvers
Analysis of notions of diagnosis
Artificial Intelligence
Model-based diagnostics and probabilistic assumption-based reasoning
Artificial Intelligence
The Oracular Constraints Method
Proceedings of the 5th International Symposium on Abstraction, Reformulation and Approximation
Counting Models Using Connected Components
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Back to the Future for Consistency-Based Trajectory Tracking
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
"Physical negation": integrating fault models into the general diagnostic engine
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
Explanation in the situation calculus
IJCAI'93 Proceedings of the 13th international joint conference on Artifical intelligence - Volume 1
A model-based approach to reactive self-configuring systems
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Fast context switching in real-time propositional reasoning
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Diagnosis of continuous valued systems in transient operating regions
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Hi-index | 0.00 |
The task of model-based diagnosis is to find a suitable assignment to the behavior modes of components (and/or transition variables) in a system given some observations made on it. A complete diagnosiscandidate is an assignment of behavior modes to all the components in the system and a partial diagnosis-candidate is an assignment of behavior modes to only a subset of them. Corresponding to different characterizations of complete diagnosis-candidates (Bayesian model selection, consistency-based, model counting etc.), partial diagnosis-candidates play different roles. In the Bayesian model selection framework for example, they signify marginal probabilities, while in the consistency-based framework they are used to "represent" complete diagnosis-candidates. In this paper, we provide an information-theoretic characterization of diagnosis-candidates in a more general form -- viz. "disjunction of partial assignments". This approach is motivated by attempting to bridge the gap between previous formalizations and to address the problems associated with them. We argue that the task of diagnosis actually consists of two separate problems, the second of which occurs more generally in hypothesis selection -- (1) to characterize the space of complete or partial assignments (like in a posterior probability distribution), and (2) to abstract and approximate the information content of such a space into a representational form that can support tractable answering of diagnosisqueries and decision-making.