Artificial Intelligence - Special volume on qualitative reasoning about physical systems
Tree clustering for constraint networks (research note)
Artificial Intelligence
Handbook of theoretical computer science (vol. B)
Qualitative reasoning: modeling and simulation with incomplete knowledge
Qualitative reasoning: modeling and simulation with incomplete knowledge
Focusing qualitative simulation using temporal logic: theoretical foundations
Annals of Mathematics and Artificial Intelligence
Solving Complexity and Ambiguity Problems in Qualitative Simulation
Solving Complexity and Ambiguity Problems in Qualitative Simulation
Trajectory constraints in qualitative simulation
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Model decomposition and simulation: a component based qualitative simulation algorithm
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Causal Simulation and Diagnosis of Dynamic Systems
AI*IA 01 Proceedings of the 7th Congress of the Italian Association for Artificial Intelligence on Advances in Artificial Intelligence
Qualitative simulation and related approaches for the analysis of dynamic systems
The Knowledge Engineering Review
Qualitative simulation for manager selection decision-making based on managerial self-efficacy
Proceedings of the 38th conference on Winter simulation
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Traditionally, constraint satisfaction problems (CSPs) are characterized using a finite set of constraints expressed within a common, shared constraint language. When reasoning across time, however, it is possible to express both temporal and state-based constraints represented within multiple constraint languages. Qualitative simulation provides an instance of this class of CSP in which, traditionally, all solutions to the CSP are computed. In this paper, we formally describe this class of temporally-extended CSPs and situate qualitative simulation within this description. This is followed by a description of the DecSIM algorithm which is used to incrementally generate all possible solutions to a temporally-extended CSP. DecSIM combines problem decomposition, a tree-clustering algorithm and ideas similar to directed arc--consistency to exploit structure and causality within a qualitative model resulting in an exponential speed-up in simulation time when compared to existing techniques.