Learning to solve problems by searching for macro-operators
Learning to solve problems by searching for macro-operators
The computer user as toolsmith: the use, reuse, and organization of computer-based tools
The computer user as toolsmith: the use, reuse, and organization of computer-based tools
Machine learning techniques to make computers easier to use
Artificial Intelligence - Special issue: artificial intelligence 40 years later
Sequence mining in categorical domains: incorporating constraints
Proceedings of the ninth international conference on Information and knowledge management
A Heuristic Approach to the Discovery of Macro-Operators
Machine Learning
ILP '00 Proceedings of the 10th International Conference on Inductive Logic Programming
Experiments in UNIX command prediction
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Relational Sequence Clustering for Aggregating Similar Agents
ISMIS '09 Proceedings of the 18th International Symposium on Foundations of Intelligent Systems
Relational Temporal Data Mining for Wireless Sensor Networks
AI*IA '09: Proceedings of the XIth International Conference of the Italian Association for Artificial Intelligence Reggio Emilia on Emergent Perspectives in Artificial Intelligence
Multi-Dimensional Relational Sequence Mining
Fundamenta Informaticae - Progress on Multi-Relational Data Mining
Mining first-order temporal interval patterns with regular expression constraints
DaWaK'07 Proceedings of the 9th international conference on Data Warehousing and Knowledge Discovery
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Analysing the use of a Unix command shell is one of the classic applications in the domain of adaptive user interfaces and user modelling. Instead of trying to predict the next command from a history of commands, we automatically produce scripts that automate frequent tasks. For this we use an ILP association rule learner. We show how to speedup the learning task by dividing it into smaller tasks, and the need for a preprocessing phase to detect frequent subsequences in the data. We illustrate this with experiments with real world data.