Skip navigation

Marco Dorigo

Marco Dorigo is a research director of the FNRS, the Belgian National Funds for Scientific Research, and co-director of IRIDIA, the artificial intelligence laboratory of the Université Libre de Bruxelles. He is the inventor of the ant colony optimization metaheuristic. His current research interests include swarm intelligence, swarm robotics, and metaheuristics for discrete optimization. He is the Editor-in-Chief of Swarm Intelligence, and an Associate Editor or member of the Editorial Boards of many journals on computational intelligence and adaptive systems. Dr. Dorigo is a Fellow of the ECCAI and of the IEEE. He was awarded the Italian Prize for Artificial Intelligence in 1996, the Marie Curie Excellence Award in 2003, the Dr. A. De Leeuw-Damry-Bourlart award in applied sciences in 2005, the Cajastur "Mamdani" International Prize for Soft Computing in 2007, and an ERC Advanced Grant in 2010.

Titles by This Author

The complex social behaviors of ants have been much studied by science, and computer scientists are now finding that these behavior patterns can provide models for solving difficult combinatorial optimization problems. The attempt to develop algorithms inspired by one aspect of ant behavior, the ability to find what computer scientists would call shortest paths, has become the field of ant colony optimization (ACO), the most successful and widely recognized algorithmic technique based on ant behavior. This book presents an overview of this rapidly growing field, from its theoretical inception to practical applications, including descriptions of many available ACO algorithms and their uses.The book first describes the translation of observed ant behavior into working optimization algorithms. The ant colony metaheuristic is then introduced and viewed in the general context of combinatorial optimization. This is followed by a detailed description and guide to all major ACO algorithms and a report on current theoretical findings. The book surveys ACO applications now in use, including routing, assignment, scheduling, subset, machine learning, and bioinformatics problems. AntNet, an ACO algorithm designed for the network routing problem, is described in detail. The authors conclude by summarizing the progress in the field and outlining future research directions. Each chapter ends with bibliographic material, bullet points setting out important ideas covered in the chapter, and exercises. Ant Colony Optimization will be of interest to academic and industry researchers, graduate students, and practitioners who wish to learn how to implement ACO algorithms.

An Experiment in Behavior Engineering

foreword by Lashon Booker


To program an autonomous robot to act reliably in a dynamic environment is a complex task. The dynamics of the environment are unpredictable, and the robots' sensors provide noisy input. A learning autonomous robot, one that can acquire knowledge through interaction with its environment and then adapt its behavior, greatly simplifies the designer's work. A learning robot need not be given all of the details of its environment, and its sensors and actuators need not be finely tuned.

Robot Shaping is about designing and building learning autonomous robots. The term "shaping" comes from experimental psychology, where it describes the incremental training of animals. The authors propose a new engineering discipline, "behavior engineering," to provide the methodologies and tools for creating autonomous robots. Their techniques are based on classifier systems, a reinforcement learning architecture originated by John Holland, to which they have added several new ideas, such as "mutespec," classifier system "energy," and dynamic population size. In the book they present Behavior Analysis and Training (BAT) as an example of a behavior engineering methodology.


Titles by This Editor

Over the past two decades, biological knowledge has grown at an unprecedented rate, giving rise to new disciplines such as systems biology, testimony of the striking progress of modeling and quantitative methods across the field. During the same period, highly speculative ideas have matured, and entire conferences and journals are now devoted to them. Synthesizing artificial cells, simulating large-scale biological networks, storing and making intelligent use of an exponentially growing amount of data (e.g., microarrays), exploiting biological substrates for computation and control, and deploying bio-inspired engineering are all cutting-edge topics today.

ECAL '11 leveraged this remarkable development of biological modeling and extended the topics of Artificial Life to the fundamental properties of living organisms: their multiscale patternforming morphodynamics, their autopoiesis, robustness, capacity to self-repair, cognitive capacities, and co-adaptation at all levels, including ecological ones. Bringing together a large interdisciplinary community of biologists, computer scientists, physicists, and mathematicians, the conference gave them a moment to reflect on how traditional boundaries between disciplines have become blurred, and to revisit in depth what constitutes "life."