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» Rule Combination in Inductive Learning
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JAIR
2010
131views more  JAIR 2010»
14 years 8 months ago
Automatic Induction of Bellman-Error Features for Probabilistic Planning
Domain-specific features are important in representing problem structure throughout machine learning and decision-theoretic planning. In planning, once state features are provide...
Jia-Hong Wu, Robert Givan
CEC
2010
IEEE
14 years 10 months ago
Grammatical rules for the automated construction of heuristics
— Developing a problem-domain independent methodology to automatically generate high performing solving strategies for specific problems is one of the challenging trends on hype...
Germán Terrazas, Natalio Krasnogor
KI
2007
Springer
15 years 3 months ago
Inductive Synthesis of Recursive Functional Programs
Abstract. We compare three systems for the task of synthesising functional recursive programs, namely Adate, an approach through evolutionary computation, the classification learn...
Martin Hofmann 0008, Andreas Hirschberger, Emanuel...
ICCBR
2003
Springer
15 years 2 months ago
Combining Case-Based and Model-Based Reasoning for Predicting the Outcome of Legal Cases
This paper presents an algorithm called IBP that combines case-based and model-based reasoning for an interpretive CBR application, predicting the outcome of legal cases. IBP uses ...
Stefanie Brüninghaus, Kevin D. Ashley
IDA
2005
Springer
15 years 3 months ago
Combining Bayesian Networks with Higher-Order Data Representations
Abstract. This paper introduces Higher-Order Bayesian Networks, a probabilistic reasoning formalism which combines the efficient reasoning mechanisms of Bayesian Networks with the...
Elias Gyftodimos, Peter A. Flach