Towards a general theory of action and time
Artificial Intelligence
A logic-based calculus of events
New Generation Computing
A critical examination of Allen's theory of action and time
Artificial Intelligence
Moments and points in an interval-based temporal logic
Computational Intelligence
Temporal reasoning based on semi-intervals
Artificial Intelligence
Inductive logic programming and learnability
ACM SIGART Bulletin
Efficient top-down induction of logic programs
ACM SIGART Bulletin
A survey on temporal reasoning in artificial intelligence
AI Communications
Reasoning about temporal relations: a maximal tractable subclass of Allen's interval algebra
Journal of the ACM (JACM)
A Machine-Oriented Logic Based on the Resolution Principle
Journal of the ACM (JACM)
Maintaining knowledge about temporal intervals
Communications of the ACM
Temporal representation and reasoning in artificial intelligence: Issues and approaches
Annals of Mathematics and Artificial Intelligence
Learning Logical Definitions from Relations
Machine Learning
Machine Learning
Some Lower Bounds for the Computational Complexity of Inductive Logic Programming
ECML '93 Proceedings of the European Conference on Machine Learning
ECML '93 Proceedings of the European Conference on Machine Learning
Higher-order Concepts in a Tractable Knowledge Representation
GWAI '87 Proceedings of the 11th German Workshop on Artificial Intelligence
Actions and Events in Interval Temporal Logic
Actions and Events in Interval Temporal Logic
Introduction to the special issue on temporal information processing
ACM Transactions on Asian Language Information Processing (TALIP) - Special Issue on Temporal Information Processing
Linear Temporal Sequences and Their Interpretation Using Midpoint Relationships
IEEE Transactions on Knowledge and Data Engineering
Guest editorial: Temporal representation and reasoning
Annals of Mathematics and Artificial Intelligence
Algorithms for time series knowledge mining
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Artificial Intelligence in Medicine
A temporal description logic for reasoning about actions and plans
Journal of Artificial Intelligence Research
An interval-based representation of temporal knowledge
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 1
IJCAI'85 Proceedings of the 9th international joint conference on Artificial intelligence - Volume 1
IJCAI'87 Proceedings of the 10th international joint conference on Artificial intelligence - Volume 2
Temporal data mining for the quality assessment of hemodialysis services
Artificial Intelligence in Medicine
Modeling time in computing: A taxonomy and a comparative survey
ACM Computing Surveys (CSUR)
Coupled semi-supervised learning
Coupled semi-supervised learning
Fuzzifying Allen's Temporal Interval Relations
IEEE Transactions on Fuzzy Systems
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Time plays an important role in the vast majority of problems and, as such, it is a vital issue to be considered when developing computer systems for solving problems. In the literature, one of the most influential formalisms for representing time is known as Allen's Temporal Algebra based on a set of 13 relations basic and reversed that may hold between two time intervals. In spite of having a few drawbacks and limitations, Allen's formalism is still a convenient representation due to its simplicity and implementability and also, due to the fact that it has been the basis of several extensions. This paper explores the automatic learning of Allen's temporal relations by the inductive logic programming system FOIL, taking into account two possible representations for a time interval: i as a primitive concept and ii as a concept defined by the primitive concept of time point. The goals of the experiments described in the paper are 1 to explore the viability of both representations for use in automatic learning; 2 compare the facility and interpretability of the results; 3 evaluate the impact of the given examples for inducing a proper representation of the relations and 4 experiment with both representations under the assumption of a closed world CWA, which would ease continuous learning using FOIL. Experimental results are presented and discussed as evidence that the CWA can be a convenient strategy when learning Allen's temporal relations.