Extending Classical Planning for Time: Research Trends in Optimal and Suboptimal Temporal Planning

Extending Classical Planning for Time: Research Trends in Optimal and Suboptimal Temporal Planning

Antonio Garrido (Universidad Politécnica de Valencia, Spain) and Eva Onaindia (Universidad Politécnica de Valencia, Spain)
Copyright: © 2008 |Pages: 40
DOI: 10.4018/978-1-59904-705-8.ch002
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Abstract

The recent advances in AI automated planning algorithms have allowed to tackle with more realistic problems that involve complex features such as explicit management of time and temporal plans (durative actions and temporal constraints), more expressive models of actions to better describe real-world problems (conservative models of actions vs. non-conservative models), utilisation of heuristic techniques to improve performance (strategies to calculate estimations and guide the search), etc. In this chapter we focus on these features, and present a review of the most successful techniques for temporal planning. First, we start with the optimal planning-graph-based approach, we do a thorough review of the general methods, algorithms and planners and finish with heuristic state-based approaches, both optimal and suboptimal. Second, we discuss the inclusion of time features into a Partial Order Causal Link (POCL) approach. In such an approach, we analyse the possibility of mixing planning with Constraint Satisfaction Problems (CSPs), formulating the planning problem as a CSP and leaving the temporal features to a CSP solver. The ultimate objective here is to come up with an advanced, combined model of planning and scheduling. Third, we outline some techniques used in hybrid approaches that combine different techniques. Finally, we provide a synthesis of many well-known temporal planners and present their main techniques.

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Table of Contents
Preface
Dimitris Vrakas, Ioannis Vlahavas
Acknowledgment
Chapter 1
Johan Baltié, Eric Bensana, Patrick Fabiani, Jean-Loup Farges, Stéphane Millet, Philippe Morignot, Bruno Patin, Gerald Petitjean, Gauthier Pitois, Jean-Clair Poncet
This chapter deals with the issues associated with the autonomy of vehicle fleets, as well as some of the dimensions provided by an Artificial... Sample PDF
Multi-Vehicle Missions: Architecture and Algorithms for Distributed Online Planning
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Chapter 2
Antonio Garrido, Eva Onaindia
The recent advances in AI automated planning algorithms have allowed to tackle with more realistic problems that involve complex features such as... Sample PDF
Extending Classical Planning for Time: Research Trends in Optimal and Suboptimal Temporal Planning
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Chapter 3
Roman Barták
Solving combinatorial optimization problems such as planning, scheduling, design, or configuration is a non-trivial task being attacked by many... Sample PDF
Principles of Constraint Processing
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Chapter 4
Alexander Mehler
We describe a simulation model of language evolution which integrates synergetic linguistics with multiagent modelling. On the one hand, this... Sample PDF
Stratified Constraint Satisfaction Networks in Synergetic Multi-Agent Simulations of Language Evolution
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Chapter 5
Zhao Lu, Jing Sun
As an innovative sparse kernel modeling method, support vector regression (SVR) has been regarded as the state-of-the-art technique for regression... Sample PDF
Soft-constrained Linear Programming Support Vector Regression for Nonlinear Black-box Systems Identification
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Chapter 6
Ioannis Partalas, Dimitris Vrakas, Ioannis Vlahavas
This article presents a detailed survey on Artificial Intelligent approaches, that combine Reinforcement Learning and Automated Planning. There is a... Sample PDF
Reinforcement Learning and Automated Planning: A Survey
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Chapter 7
Stasinos Konstantopoulos, Rui Camacho, Nuno A. Fonseca, Vítor Santos Costa
This chapter introduces Inductive Logic Programming (ILP) from the perspective of search algorithms in Computer Science. It first briefly considers... Sample PDF
Induction as a Search Procedure
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Chapter 8
Kiruthika Ramanathan, Sheng Uei Guan
In this chapter we present a recursive approach to unsupervised learning. The algorithm proposed, while similar to ensemble clustering, does not... Sample PDF
Single- and Multi-order Neurons for recursive unsupervised learning
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Chapter 9
Malcolm J. Beynon
This chapter investigates the effectiveness of a number of objective functions used in conjunction with a novel technique to optimise the... Sample PDF
Optimising Object Classification: Uncertain Reasoning-Based Analysis Using CaRBS Systematic Research Algorithms
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Chapter 10
P. Vasant, N. Barsoum, C. Kahraman, G.M Dimirovski
This chapter proposes a new method to obtain optimal solution using satisfactory approach in uncertain environment. The optimal solution is obtained... Sample PDF
Application of Fuzzy Optimization in Forecasting and Planning of Construction Industry
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Chapter 11
Malcolm J. Beynon
This chapter investigates the modelling of the ability to improve the rank position of an alternative in relation to those of its competitors.... Sample PDF
Rank Improvement Optimization Using PROMETHEE and Trigonometric Differential Evolution
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Chapter 12
Iker Gondra
Genetic Algorithms (GA), which are based on the idea of optimizing by simulating the natural processes of evolution, have proven successful in... Sample PDF
Parallelizing Genetic Algorithms: A Case Study
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Chapter 13
Daniel Rivero, Miguel Varela, Javier Pereira
A technique is described in this chapter that makes it possible to extract the knowledge held by previously trained artificial neural networks. This... Sample PDF
Using Genetic Programming to Extract Knowledge from Artificial Neural Networks
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About the Contributors