Machine Learning for Adaptive Planning

Machine Learning for Adaptive Planning

Dimitris Vrakas (Aristotle University of Thessaloniki, Greece), Grigorios Tsoumakas (Aristotle University of Thessaloniki, Greece), Nick Bassiliakes (Aristotle University of Thessaloniki, Greece) and Ioannis Vlahavas (Aristotle University of Thessaloniki, Greece)
Copyright: © 2005 |Pages: 31
DOI: 10.4018/978-1-59140-450-7.ch003
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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.

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Table of Contents
Ioannis Vlahavas, Dimitris Vrakas
Chapter 1
Thomas Eiter, Wolfgang Faber, Gerald Pfeifer, Axel Polleres
This chapter introduces planning and knowledge representation in the declarative action language K. Rooted in the area of Knowledge Representation &... Sample PDF
Declarative Planning and Knowledge Representation in an Action Language
Chapter 2
Max Garagnani
This chapter describes a model and an underlying theoretical framework for hybrid planning. Modern planning domain description languages are based... Sample PDF
A Framework for Hybrid and Analogical Planning
Chapter 3
Dimitris Vrakas, Grigorios Tsoumakas, Nick Bassiliakes, Ioannis Vlahavas
This chapter is concerned with the enhancement of planning systems using techniques from Machine Learning in order to automatically configure their... Sample PDF
Machine Learning for Adaptive Planning
Chapter 4
José Luis Ambite, Craig A. Knoblock, Steven Minton
Planning by Rewriting (PbR) is a paradigm for efficient high-quality planning that exploits declarative plan rewriting rules and efficient local... Sample PDF
Plan Optimization by Plan Rewriting
Chapter 5
Nikos Avradinis, Themis Panayiotopoulos
This chapter discusses the application of intelligent planning techniques to virtual agent environments as a mechanism to control and generate... Sample PDF
Continuous Planning for Virtual Environments
Chapter 6
Jeroen Valk, Mathijs de Weerdt, Cees Witteveen
Multi-agent planning comprises planning in an environment with multiple autonomous actors. Techniques for multi-agent planning differ from... Sample PDF
Coordination in Multi-Agent Planning with an Application in Logistics
Chapter 7
Catherine C. Marinagi, Themis Panayiotopoulos, Constantine D. Spyropoulos
This chapter provides an overview of complementary research in the active research areas: AI planning technology and intelligent agents technology.... Sample PDF
AI Planning and Intelligent Agents
Chapter 8
Amedeo Cesta, Simone Fratini, Angelo Oddi
This chapter proposes to model a planning problem (e.g., the control of a satellite system) by identifying a set of relevant components in the... Sample PDF
Planning with Concurrency, Time and Resources: A CSP-Based Approach
Chapter 9
Martha E. Pollack, Ioannis Tsamardinos
The Simple Temporal Problem (STP) formalism was developed to encode flexible quantitative temporal constraints, and it has been adopted as a... Sample PDF
Efficiently Dispatching Plans Encoded as Simple Temporal Problems
Chapter 10
Roman Bartak
As the current planning and scheduling technologies are coming together by assuming time and resource constraints in planning or by allowing... Sample PDF
Constraint Satisfaction for Planning and Scheduling
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