Towards a general theory of action and time
Artificial Intelligence
Reasoning about change: time and causation from the standpoint of artificial intelligence
Reasoning about change: time and causation from the standpoint of artificial intelligence
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Abductive inference models for diagnostic problem-solving
Abductive inference models for diagnostic problem-solving
Optimization
On the generation of alternative explanations with implications for belief revision
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
Planning and control
Artificial Intelligence - Special issue on knowledge representation
The temporal logic of reactive and concurrent systems
The temporal logic of reactive and concurrent systems
Temporal reasoning based on semi-intervals
Artificial Intelligence
The engineering of knowledge-based systems: theory and practice
The engineering of knowledge-based systems: theory and practice
Reasoning about time and probability
Reasoning about time and probability
Journal of the ACM (JACM)
The causal Markov condition, fact or artifact?
ACM SIGART Bulletin
Complexity, ontology, and the causal Markov assumption
ACM SIGART Bulletin
Maintaining knowledge about temporal intervals
Communications of the ACM
ACL '88 Proceedings of the 26th annual meeting on Association for Computational Linguistics
The design and experimental analysis of algorithms for temporal reasoning
Journal of Artificial Intelligence Research
Planning using a temporal world model
IJCAI'83 Proceedings of the Eighth international joint conference on Artificial intelligence - Volume 2
The role of fuzzy logic in the management of uncertainty in expert systems
Fuzzy Sets and Systems
Probabilistic semantics for cost based abduction
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 1
Weak representations of interval algebras
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 1
Probabilistic temporal reasoning with endogenous change
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in 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
Journal of Biomedical Informatics
Modeling time-varying uncertain situations using Dynamic Influence Nets
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
Bringing introspection into BlobSeer: Towards a self-adaptive distributed data management system
International Journal of Applied Mathematics and Computer Science - SPECIAL SECTION: Efficient Resource Management for Grid-Enabled Applications
Annotated Probabilistic Temporal Logic: Approximate Fixpoint Implementation
ACM Transactions on Computational Logic (TOCL)
Temporal Bayesian Knowledge Bases - Reasoning about uncertainty with temporal constraints
Expert Systems with Applications: An International Journal
Describing disease processes using a probabilistic logic of qualitative time
Artificial Intelligence in Medicine
Hi-index | 0.00 |
Complex real-world systems consist of collections of interacting processes/events. These processes change over time in response to both internal and external stimuli as well as to the passage of time itself. Many domains such as real-time systems diagnosis, story understanding, and financial forecasting require the capability to model complex systems under a unified framework to deal with both time and uncertainty. Current models for uncertainty and current models for time already provide rich languages to capture uncertainty and temporal information, respectively. Unfortunately, these semantics have made it extremely difficult to unify time and uncertainty in a way which cleanly and adequately models the problem domains at hand. Existing approaches suffer from significant trade offs between strong semantics for uncertainty and strong semantics for time. In this paper, we explore a new model, the Probabilistic Temporal Network (PTN), for representing temporal and atemporal information while fully embracing probabilistic semantics. The model allows representation of time constrained causality, of when and if events occur, and of the periodic and recurrent nature of processes.