Problems in formal temporal reasoning
Artificial Intelligence
Artificial Intelligence - Special issue on knowledge representation
A model and a language for the fuzzy representation and handling of time
Fuzzy Sets and Systems
Combining qualitative and quantitative constraints in temporal reasoning
Artificial Intelligence
A framework for knowledge-based temporal abstraction
Artificial Intelligence
A spectrum of definitions for temporal model-based diagnosis
Artificial Intelligence
Maintaining knowledge about temporal intervals
Communications of the ACM
Using Time-Oriented Data Abstraction Methods to Optimize Oxygen Supply for Neonates
AIME '01 Proceedings of the 8th Conference on AI in Medicine in Europe: Artificial Intelligence Medicine
Fuzzy constraint networks for signal pattern recognition
Artificial Intelligence - Special issue: Fuzzy set and possibility theory-based methods in artificial intelligence
Fuzzy theory approach for temporal model-based diagnosis: An application to medical domains
Artificial Intelligence in Medicine
Temporal factorisation: A unifying principle for dynamics of the world and of mental states
Cognitive Systems Research
User-centered visual analysis using a hybrid reasoning architecture for intensive care units
Decision Support Systems
Hi-index | 0.01 |
Understanding the behaviour of systems requires dealing with the raw data obtained from variables that define the system's state and using high-level knowledge for deep processing tasks. Therefore, intermediate processes to reduce this cognitive gap are needed. Besides, when dealing with dynamic systems, in which state variables evolves with time, the way in which time is perceived plays a crucial role and affects the performance of deep processing tasks. Such an intermediate process, which helps overcome this cognitive gap between raw data and deep knowledge processing levels, while taking into account the time dimension, is called temporal abstraction (TA). The final objective of TA is to provide an explanation that describes the behaviour of dynamic systems arising from the temporal evolution of state variables and to locate the data in their correct temporal context. In this article we propose a novel method for TA, based on an abductive strategy and a temporal constraint model as an underlying formalism for time representation and reasoning. The abductive vision of this problem is suitable for a general case in which data are heterogeneous and where there is temporal imprecision.