Artificial Intelligence for Advanced Problem Solving Techniques

Artificial Intelligence for Advanced Problem Solving Techniques

Ioannis Vlahavas (Aristotle University, Greece) and Dimitris Vrakas (Aristotle University, Greece)
Release Date: January, 2008|Copyright: © 2008 |Pages: 388
ISBN13: 9781599047058|ISBN10: 1599047055|EISBN13: 9781599047072|DOI: 10.4018/978-1-59904-705-8
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One of the most important functions of artificial intelligence, automated problem solving, consists mainly of the development of software systems designed to find solutions to problems. These systems utilize a search space and algorithms in order to reach a solution.

Artificial Intelligence for Advanced Problem Solving Techniques offers scholars and practitioners cutting-edge research on algorithms and techniques such as search, domain independent heuristics, scheduling, constraint satisfaction, optimization, configuration, and planning, and highlights the relationship between the search categories and the various ways a specific application can be modeled and solved using advanced problem solving techniques.

Topics Covered

  • Artificial Neural Networks
  • Automated Planning
  • CaRBS Systematic Search Algorithms
  • Constraint Satisfaction and Scheduling
  • Distributed Online Planning
  • Fuzzy Optimization
  • Genetic Algorithms and Programming
  • Language Evolution
  • Machine Learning
  • Optimal and Suboptimal Temporal Planning
  • Optimising Object Classification
  • Parallelizing Genetic Algorithms
  • Principles of Constraint Processing
  • Recursive Unsupervised Learning
  • Reinforcement Learning
  • Stratified Constraint Satisfaction Networks
  • Synergetic Multi-Agent Simulations
  • Trigonometric Differential Evolution
  • Uncertain Reasoning-Based Analysis

Reviews and Testimonials

This book aims to provide the user with knowledge about the principles of Artificial Intelligence and about innovative methodologies that utilize the effort spent by researchers in various different fields in order to build effective problem-solving systems.

– Dimitris Vrakas, Aristotle University, Greece

This collection offers scholars and practitioners cutting-edge research on algorithms and techniques such as search, domain independent heuristics, scheduling, constraint satisfaction, optimization, configuration, and planning.

– APADE (2008)

This book has been written to teach IT researchers, system engineers and students about the latest research on automated problem solving.

– Book News Inc. (June 2008)

The book is suitable for postgraduates, researchers and practitioners in planning, scheduling, optimization, machine learning, and advanced problem solving. The book is a valuable addition to any science library.

– H. Yousef, INVISTA Performance Technologies, Journal of the Operational Research Society (2010)

Table of Contents and List of Contributors

Search this Book:
Table of Contents
Dimitris Vrakas, Ioannis Vlahavas
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
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
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
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
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
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
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
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
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
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
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
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
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
About the Contributors


Problem Solving is a complex mental activity that occurs when an agent (biological, robotic or software) does not know how to proceed from a given state to a desired goal state. The nature of human problem solving has been studied by psychologists over the past hundred years and it has also been a subject of research in Artificial Intelligence from the early ‘60s with the General Problem Solver (GPS). Artificial Intelligence is concerned with two major topics in Problem Solving: representation of the problem and automatically extracting a solution to it. Automated Problem Solving is the area of Artificial Intelligence that is mainly concerned with the development of software systems that given a formal representation of a problem they find a solution to it.

These systems usually explore a search space, utilizing search algorithms in order to reach one or more goal states. These search problems are divided in five main categories: a) Automated Planning, b) Constraint Satisfaction and Scheduling, c) Machine Learning, d) Optimization, e) Genetic Algorithms and Genetic Programming.

The fist category, namely Planning, refers to problems where the solution is a sequence of actions that if applied to the initial state will eventually lead to a new state that satisfies the goals. Examples of this category includes robot control problems, navigation problems (e.g. path planning), logistics etc.

The second category is Constraint Satisfaction and Scheduling and is concerned with problems where the goal is to assign values to a set of variables in a way that a set of constraints is met, while optimizing a certain cost function. There are numerous examples of problems of this category like workers’ shifts, timetabling, resource allocation etc.

Machine Learning, which is the third category, is concerned with problems in which an artificial agent must learn. The major focus of Machine learning is to extract information from data automatically by computational and statistical methods. The applications of Machine Learning mainly concern data analysis tasks in decision support systems Data Mining is the application of Machine Learning in large data bases.

The fourth category is optimization problems, where there is a function that measures the appropriateness of each state and the goal of the problem is to find a state that either maximizes or minimizes this function. In contrast to the other categories, no information concerning the goals state is included in the problem representation. A representative example of this category is the Travelling Salesman Person (TSP) problem.

The last category includes genetic algorithms and genetic programming. Genetic algorithms are a particular class of evolutionary algorithms that use techniques inspired by evolutionary biology such as inheritance, mutation, selection, and crossover in order to solve optimization and search problems. Genetic programming is an area of genetic algorithms which is concerned with the automatic creation of computer programs that perform a specific user-defined task.

This edited volume, entitled Artificial Intelligence for Advanced Problem Solving Techniques, consists of 13 chapters, bringing together a number of modern approaches on the area of Automated or Advanced Problem Solving. The book presents in detail a number of state-of-the-art algorithms and systems mainly for solving search problems. Apart from the thorough analysis and implementation details, each chapter of the book also provides extensive background information about its subject and presents and comments similar approaches done in the past.

Author(s)/Editor(s) Biography

Dr. Ioannis Vlahavas is a professor at the Department of Informatics at the Aristotle University of Thessaloniki. He received his Ph.D. degree in Logic Programming Systems from the same University in 1988. During the first half of 1997 he was a visiting scholar at the Department of CS at Purdue University. He specializes in logic programming, knowledge based and AI systems and he has published over 100 papers, 9 book chapters and co-authored 4 books in these areas. He teaches logic programming, AI, expert systems, and DSS. He has been involved in more than 15 research projects, leading most of them. He was the chairman of the 2nd Hellenic Conference on AI and the local organizer of the 2nd International Summer School on AI Planning. He is leading the Logic Programming and Intelligent Systems Group (LPIS Group, (more information at
Dimitris Vrakas is finishing his PhD in Distributed Planning and Scheduling at the Dept of Informatics of the Aristotle University of Thessaloniki. His interests also include Machine Learning, Problem Solving and Heuristic Search Algorithms. He has published several articles and presented various papers on important aspects of Automated Planning such as Learning aided Planning Systems. He has taken part in several projects such as PacoPlan, a web–based system combining Planning and Constraint Programming. He is a member of the American Association for Artificial Intelligence, the Association of Greek Informaticians and the Hellenic Society for Artificial Intelligence.