Artificial Intelligence
A Nearest Hyperrectangle Learning Method
Machine Learning
Tolerating noisy, irrelevant and novel attributes in instance-based learning algorithms
International Journal of Man-Machine Studies - Special issue: symbolic problem solving in noisy and novel task environments
Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
Machine Learning as an Experimental Science
Machine Learning
Machine Learning
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
GIB: Steps Toward an Expert-Level Bridge-Playing Program
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Sequential Instance-Based Learning
AI '98 Proceedings of the 12th Biennial Conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Evaluating the effectiveness of exploration and accumulated experience in automatic case elicitation
ICCBR'05 Proceedings of the 6th international conference on Case-Based Reasoning Research and Development
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Finding sequential concepts, as in planning, is a complex task because of the exponential size of the search space. Empirical learning can be an effective way to find sequential concepts from observations. Sequential Instance-Based Learning (SIBL), which is presented here, is an empirical learning paradigm, modeled after Instance-Based Learning (IBL) that learns sequential concepts, ordered sequences of state-action pairs to perform a synthesis task. SIBL is highly effective and learns expert-level knowledge. SIBL demonstrates the feasibility of using an empirical learning approach to discover sequential concepts. In addition, this approach suggests a general framework that systematically extends empirical learning to learning sequential concepts. SIBL is tested on the domain of bridge.