A model for reasoning about persistence and causation
Computational Intelligence
Readings in qualitative reasoning about physical systems
Readings in qualitative reasoning about physical systems
Proceedings of the first international conference on Artificial intelligence planning systems
A computational scheme for reasoning in dynamic probabilistic networks
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
Modeling a dynamic and uncertain world I: symbolic and probabilistic reasoning about change
Artificial Intelligence
An algorithm for probabilistic planning
Artificial Intelligence - Special volume on planning and scheduling
Projecting plans for uncertain worlds
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Simulation-based inference for plan monitoring
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Learning Dynamic Bayesian Belief Networks Using Conditional Phase-Type Distributions
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Temporal Bayesian Network of Events for Diagnosis and Prediction in Dynamic Domains
Applied Intelligence
Privacy intrusion detection using dynamic Bayesian networks
ICEC '06 Proceedings of the 8th international conference on Electronic commerce: The new e-commerce: innovations for conquering current barriers, obstacles and limitations to conducting successful business on the internet
Rule discovery for event histories
Intelligent Data Analysis
Modeling time-varying uncertain situations using Dynamic Influence Nets
International Journal of Approximate Reasoning
Probabilistic hybrid action models for predicting concurrent percept-driven robot behavior
Journal of Artificial Intelligence Research
Probabilistic temporal networks: A unified framework for reasoning with time and uncertainty
International Journal of Approximate Reasoning
A framework for reasoning under uncertainty with temporal constraints
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
A temporal Bayesian network for diagnosis and prediction
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Continuous time bayesian networks
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Temporal context lie detection and generation
SDM'06 Proceedings of the Third VLDB international conference on Secure Data Management
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This paper presents a probabilistic model for reasoning about the state of a system as it changes over time, both due to exogenous and endogenous influences. Our target domain is a class of medical prediction problems that are neither so urgent as to preclude careful diagnosis nor progress so slowly as to allow arbitrary testing and treatment options. In these domains there is typically enough time to gather information about the patient's state and consider alternative diagnoses and treatments, but the temporal interaction between the timing of tests, treatments, and the course of the disease must also be considered. Our approach is to elicit a qualitative structural model of the patient from a human expert--the model identifies important attributes, the way in which exogenous changes affect attribute values, and the way in which the patient's condition changes endogenously. We then elicit probabilistic information to capture the expert's uncertainty about the effects of tests and treatments and the nature and timing of endogenous state changes. This paper describes the model in the context of a problem in treating vehicle accident trauma, and suggests a method for solving the model based on the technique of sequential imputation. A complementary goal of this work is to understand and synthesize a disparate collection of research efforts all using the name "probabilistic temporal reasoning." This paper analyzes related work and points out essential differences between our proposed model and other approaches in the literature.