Acquiring search-control knowledge via static analysis

A review of machine learning for automated planning

Recent discoveries in automated planning are broadening the scope of planners, from toy problems to real applications. However, applying automated planners to real-world problems is far from simple. On the one hand, the definition of accurate action models for planning is still a bottleneck. On the other hand, off-the-shelf planners fail to scale-up and to provide good solutions in many domains. In these problematic domains, planners can exploit domain-specific control knowledge to improve their performance in terms of both speed and quality of the solutions. However, manual definition of control knowledge is quite difficult. This paper reviews recent techniques in machine learning for the automatic definition of planning knowledge. It has been organized according to the target of the learning process: automatic definition of planning action models and automatic definition of planning control knowledge. In addition, the paper reviews the advances in the related field of reinforcement learning.

Improving Automated Planning with Machine Learning

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.

KNOWLEDGE GRANULARITY SPECTRUM, ACTION PYRAMID, AND THE SCALING PROBLEM

In this paper we introduce the concept of knowledge granularity and study the relationship between different knowledge representation schemes and the scaling problem. By scale to a task, we mean that an agent's planning system and knowledge representation scheme are able to generate the range of behaviors required by the task in a timely fashion. Action selection is critical to an agent performing a task in a dynamic, unpredictable environment. Knowledge representation is central to the agent's action selection process. It is important to study how an agent should adapt its methods of representation such that its performance can scale to different task requirements. Here we study the following issues. One is the knowledge granularity problem: to what detail should an agent represent a certain kind of knowledge if a single granularity of representation is to be used. Another is the representation scheme problem: to scale to a given task, should an agent represent its knowledge using a single granularity or a set of hierarchical granularities.

AI planning and scheduling in the medical hospital environment

Hospital management is a hard task due to the complexity of the organization, the costly infrastructure, the specialized services offered to different patients and the need for prompt reaction to emergencies. Artificial Intelligence planning and scheduling methods can offer substantial support to the management of hospitals, and help raising the standards of service. This editorial presents an overview of the achievements reported in therapy planning and hospital management together with a general roadmap of the published research in Artificial Intelligence planning and scheduling. Finally, a discussion for the future research and development in this area concludes the presentation.