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ML
2008
ACM
150views Machine Learning» more  ML 2008»
13 years 8 months ago
Learning probabilistic logic models from probabilistic examples
Abstract. We revisit an application developed originally using Inductive Logic Programming (ILP) by replacing the underlying Logic Program (LP) description with Stochastic Logic Pr...
Jianzhong Chen, Stephen Muggleton, José Car...
ECAI
2000
Springer
14 years 10 days ago
Learning to Use Operational Advice
We address the problem of advice-taking in a given domain, in particular for building a game-playing program. Our approach to solving it strives for the application of machine lea...
Johannes Fürnkranz, Bernhard Pfahringer, Herm...
AAI
2005
117views more  AAI 2005»
13 years 8 months ago
Machine Learning in Hybrid Hierarchical and Partial-Order Planners for Manufacturing Domains
The application of AI planning techniques to manufacturing systems is being widely deployed for all the tasks involved in the process, from product design to production planning an...
Susana Fernández, Ricardo Aler, Daniel Borr...
UIST
2010
ACM
13 years 5 months ago
Gestalt: integrated support for implementation and analysis in machine learning
We present Gestalt, a development environment designed to support the process of applying machine learning. While traditional programming environments focus on source code, we exp...
Kayur Patel, Naomi Bancroft, Steven M. Drucker, Ja...
SIGSOFT
2008
ACM
14 years 8 months ago
Finding programming errors earlier by evaluating runtime monitors ahead-of-time
Runtime monitoring allows programmers to validate, for instance, the proper use of application interfaces. Given a property specification, a runtime monitor tracks appropriate run...
Eric Bodden, Patrick Lam, Laurie J. Hendren