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AUSAI
2004
Springer

A Learning-Based Algorithm Selection Meta-reasoner for the Real-Time MPE Problem

10 years 7 months ago
A Learning-Based Algorithm Selection Meta-reasoner for the Real-Time MPE Problem
Abstract. The algorithm selection problem aims to select the best algorithm for an input problem instance according to some characteristics of the instance. This paper presents a learning-based inductive approach to build a predictive algorithm selection system from empirical algorithm performance data of the Most Probable Explanation(MPE) problem. The learned model can serve as an algorithm selection meta-reasoner for the real-time MPE problem. Experimental results show that the learned algorithm selection models can help integrate multiple MPE algorithms to gain a better overall performance of reasoning.
Haipeng Guo, William H. Hsu
Added 01 Jul 2010
Updated 01 Jul 2010
Type Conference
Year 2004
Where AUSAI
Authors Haipeng Guo, William H. Hsu
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