Machine Learning for Adaptive Planning

Abstract

This chapter is concerned with the enhancement of planning systems using techniques from Machine Learning in order to automatically configure their planning parameters according to the morphology of the problem in hand. It presents two different adaptive systems that set the planning parameters of a highly adjustable planner based on measurable characteristics of the problem instance. The planners have acquired their knowledge from a large data set produced by results from experiments on many problems from various domains. The first planner is a rule-based system that employs propositional rule learning to induce knowledge that suggests effective configuration of planning parameters based on the problem’s characteristics. The second planner employs instance-based learning in order to find problems with similar structure and adopt the planner configuration that has proved in the past to be effective on these problems. The validity of the two adaptive systems is assessed through experimental results that demonstrate the boost in performance in problems of both known and unknown domains. Comparative experimental results for the two planning systems are presented along with a discussion of their advantages and disadvantages.

Prerequisite knowledge‐based automated course planning with semantics consideration

“…Their research is based on the IEEE LOM (Learning Object Metadata) specification [3] for machine readability and uses the HAP EDU planner as a planning engine, where HAP stands for Highly Adjustable Planner. The planner is a heuristic planner extended from the HAP RC planner [36] to support abstraction hierarchies, which is closely related to HSR among knowledge terms in this article. Since it is based on a heuristic algorithm, PASER does not guarantee optimality.…”

Algorithm Selection for Combinatorial Search Problems: A Survey

“…There are several machine‐learning techniques that facilitate this, as the learned models are represented in a form that is easy to understand by humans. Carbonell et al (1991), Brodley (1993), and Vrakas et al (2003) learn classification rules that guide the selector. Vrakas and colleagues (2003) note that the decision to use a classification rule leaner was not so much guided by the performance of the approach, but the easy interpretability of the result.…”