Towards a general theory of action and time
Artificial Intelligence
Artificial Intelligence - Special issue on knowledge representation
Temporal logic and applications: a tutorial
Computer Networks and ISDN Systems - Special issue on protocol specification, testing and verification
A situated view of representation and control
Artificial Intelligence - Special volume on computational research on interaction and agency, part 2
Maintaining knowledge about temporal intervals
Communications of the ACM
Temporal Constraints: A Survey
Constraints
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Temporal reasoning with constraints
Temporal reasoning with constraints
A reactive planner for a model-based executive
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Fast planning through planning graph analysis
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Pushing the envelope: planning, propositional logic, and stochastic search
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Planning for temporally extended goals
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Temporal Bayesian Knowledge Bases - Reasoning about uncertainty with temporal constraints
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
In this work we describe causal temporal constraint networks (CTCN) as a new computable model for representing temporal information and efficiently handling causality. The proposed model enables qualitative and quantitative temporal constraints to be established, introduces the representation of causal constraints, and suggests mechanisms for representing inexact temporal knowledge. The temporal handling of information is achieved by structuring the information in different interpretation contexts, linked to each other through an inference mechanism which obtains interpretations that are consistent with the original temporal information. In carrying out inferences, we take into account the temporal relationships between events, the possible inexactitude associated with the events, and the atemporal or static information which affects the interpretation pattern being considered. The proposed schema is illustrated with an application developed using the CommonKADS methodology.