Artificial Intelligence
Structured induction in expert systems
Structured induction in expert systems
Generalized subsumption and its applications to induction and redundancy
Artificial Intelligence
Planning in games using approximately learned macros
Proceedings of the sixth international workshop on Machine learning
Indirect relevance and bias in inductive concept-learning
Knowledge Acquisition
Learning nonrecursive definitions of relations with LINUS
EWSL-91 Proceedings of the European working session on learning on Machine learning
The importance of basic musical knowledge for effective learning
Understanding music with AI
Compiling prior knowledge into an explicit basis
ML92 Proceedings of the ninth international workshop on Machine learning
Algorithmic Program DeBugging
Learning Logical Definitions from Relations
Machine Learning
A Study of Explanation-Based Methods for Inductive Learning
Machine Learning
Explanation-Based Generalization: A Unifying View
Machine Learning
Explanation-Based Learning: An Alternative View
Machine Learning
Machines that learn to play games
Journal of Artificial Intelligence Research
Modeling violin performances using inductive logic programming
Intelligent Data Analysis - Machine Learning and Music
Probabilistic and logic-based modelling of harmony
CMMR'10 Proceedings of the 7th international conference on Exploring music contents
Understanding expressive music performance using genetic algorithms
EC'05 Proceedings of the 3rd European conference on Applications of Evolutionary Computing
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It has been argued that much of human intelligence can beviewed as the process of matching stored patterns. In particular, itis believed that chess masters use a pattern–based knowledge toanalyze a position, followed by a pattern–based controlled search toverify or correct the analysis. In this paper, a first–order system,called PAL, that can learn patterns in the form of Horn clauses fromsimple example descriptions and general purpose knowledge isdescribed. The learning model is based on (i) a constrained leastgeneral generalization algorithm to structure the hypothesis space andguide the learning process, and (ii) a pattern–based representation knowledge to constrain the construction of hypothesis.It is shown how PAL can learn chess patterns which are beyond thelearning capabilities of current inductive systems. The samepattern–based approach is used to learn qualitative models of simpledynamic systems and counterpoint rules for two–voice musical pieces.Limitations of PAL in particular, and first–order systems in general,are exposed in domains where a large number of background definitionsmay be required for induction. Conclusions and future researchdirections are given.