Abstract
Since the early days of AI, automated reasoning has been a rather elusive goal. In fact, up till the early nineties, general inference beyond hundred variable problems appeared infeasible. Over the last decade, we have witness a qualitative change in the field: current reasoning engines can handle problems with over a million variables and several millions of constraints. I will discuss what led to such a dramatic scale-up, and how progress in reasoning technology has opened up a range of new applications in AI and computer science in general. I will also discuss initial progress on the use of learning techniques in reasoning engines and the remaining challenges for obtaining a true integration of learning and reasoning.
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© 2007 Springer-Verlag Berlin Heidelberg
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Selman, B. (2007). Integration of Learning and Reasoning Techniques. In: Muggleton, S., Otero, R., Tamaddoni-Nezhad, A. (eds) Inductive Logic Programming. ILP 2006. Lecture Notes in Computer Science(), vol 4455. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73847-3_4
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DOI: https://doi.org/10.1007/978-3-540-73847-3_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73846-6
Online ISBN: 978-3-540-73847-3
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