Welcome to the InfoSci Platform
Improving Automated Planning with Machine Learning

Improving Automated Planning with Machine Learning

Susana Fernández Arregui, Sergio Jiménez Celorrio, Tomás de la Rosa Turbides
Copyright: © 2010 |Pages: 22
ISBN13: 9781605667669|ISBN10: 1605667668|EISBN13: 9781605667676
DOI: 10.4018/978-1-60566-766-9.ch029
Cite Chapter Cite Chapter

MLA

Fernández Arregui, Susana, et al. "Improving Automated Planning with Machine Learning." Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques, edited by Emilio Soria Olivas, et al., IGI Global, 2010, pp. 599-620. https://doi.org/10.4018/978-1-60566-766-9.ch029

APA

Fernández Arregui, S., Jiménez Celorrio, S., & de la Rosa Turbides, T. (2010). Improving Automated Planning with Machine Learning. In E. Olivas, J. Guerrero, M. Martinez-Sober, J. Magdalena-Benedito, & A. Serrano López (Eds.), Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques (pp. 599-620). IGI Global. https://doi.org/10.4018/978-1-60566-766-9.ch029

Chicago

Fernández Arregui, Susana, Sergio Jiménez Celorrio, and Tomás de la Rosa Turbides. "Improving Automated Planning with Machine Learning." In Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques, edited by Emilio Soria Olivas, et al., 599-620. Hershey, PA: IGI Global, 2010. https://doi.org/10.4018/978-1-60566-766-9.ch029

Export Reference

Mendeley
Favorite

Abstract

This chapter reports the last machine learning techniques for the assistance of automated planning. Recent discoveries in automated planning have opened the scope of planners, from toy problems to real-world applications, making new challenges come into focus. The planning community believes that machine learning can assist to address these new challenges. The chapter collects the last machine learning techniques for assisting automated planners classified in: techniques for the improvement of the planning search processes and techniques for the automatic definition of planning action models. For each technique, the chapter provides an in-depth analysis of their domain, advantages and disadvantages. Finally, the chapter draws the outline of the new promising avenues for research in learning for planning systems.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.