It is a drawback that arise when using learned knowledge cost of using overwhelms its benefit because the difficulty of storage and management the learned information and because of determining which information to use to solve a particular problem.
Published in Chapter:
Improving Automated Planning with Machine Learning
Susana Fernández Arregui (Universidad Carlos III de Madrid, Spain), Sergio Jiménez Celorrio (Universidad Carlos III de Madrid, Spain), and Tomás de la Rosa Turbides (Universidad Carlos III de Madrid, Spain)
Source Title: Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques
Copyright: © 2010|Pages: 22
DOI: 10.4018/978-1-60566-766-9.ch029
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.
