Artificial Intelligence
Readings in qualitative reasoning about physical systems
Readings in qualitative reasoning about physical systems
A theory of interactions: unifying qualitative and quantitative algebraic reasoning
Artificial Intelligence - Special issue: Qualitative reasoning about physical systems II
Compositional modeling: finding the right model for the job
Artificial Intelligence - Special issue: Qualitative reasoning about physical systems II
Artificial Intelligence - Special issue: Qualitative reasoning about physical systems II
Readings in model-based diagnosis
Readings in model-based diagnosis
What's in SD?: Towards a theory of modeling for diagnosis
Readings in model-based diagnosis
Reasoning about model accuracy
Artificial Intelligence
Recent advances in qualitative physics
Recent advances in qualitative physics
Qualitative reasoning: modeling and simulation with incomplete knowledge
Qualitative reasoning: modeling and simulation with incomplete knowledge
Artificial Intelligence
Efficient compositional modeling for generating causal explanations
Artificial Intelligence
Model simplification by asymptotic order of magnitude reasoning
Artificial Intelligence
Automated modeling of complex systems to answer prediction questions
Artificial Intelligence
Qualitative and quantitative simulation: bridging the gap
Artificial Intelligence
Automated model selection for simulation based on relevance reasoning
Artificial Intelligence
Remote Agent: to boldly go where no AI system has gone before
Artificial Intelligence - Special issue: artificial intelligence 40 years later
A comparison of structural CSP decomposition methods
Artificial Intelligence
A prototype for model-based on board diagnosis of automotive systems
AI Communications
Automatic abstraction in component-based diagnosis driven by system observability
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Eliminating interchangeable values in constraint satisfaction problems
AAAI'91 Proceedings of the ninth National conference on Artificial intelligence - Volume 1
Test Strategy Generation Using Quantified CSPs
CP '08 Proceedings of the 14th international conference on Principles and Practice of Constraint Programming
Using Model Counting to Find Optimal Distinguishing Tests
CPAIOR '09 Proceedings of the 6th International Conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems
Dynamic domain abstraction through meta-diagnosis
SARA'07 Proceedings of the 7th International conference on Abstraction, reformulation, and approximation
Formalizing the abstraction process in model-based diagnosis
SARA'07 Proceedings of the 7th International conference on Abstraction, reformulation, and approximation
Constraint optimization and abstraction for embedded intelligent systems
CPAIOR'08 Proceedings of the 5th international conference on Integration of AI and OR techniques in constraint programming for combinatorial optimization problems
Automatic abstraction of time-varying system models for model based diagnosis
KI'05 Proceedings of the 28th annual German conference on Advances in Artificial Intelligence
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Automated problem-solving for engineered devices is based on models that capture the essential aspects of the behavior. In this paper, we deal with the problem of automatically abstracting behavior models such that their level of granularity is as coarse as possible, but still sufficiently detailed to carry out a given behavioral prediction or diagnostic task. A task is described by a behavior model, as composed from a library, a specified granularity of the possible observations, and a specified granularity of the desired results. The goal of task-dependent qualitative domain abstraction is to determine maximal partitions for the variables' domains (termed qualitative values) that retain all the necessary distinctions. We present a formalization of this problem within a relational (constraint-based) framework, and devise solutions to automatically determine qualitative values for a device model. The results enhance the ability to use a behavior model of a device as a common basis to support different tasks along its life cycle.