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The present study proposes a new selection hyper-heuristic providing several adaptive features to cope with the requirements of managing different heuristic sets. The approach suggested provides an intelligent way of selecting heuristics, determines effective heuristic pairs and adapts the parameters of certain heuristics online. In addition, an adaptive… (More)

- Peter Vrancx, Katja Verbeeck, Ann Nowé
- IEEE Trans. Systems, Man, and Cybernetics, Part B
- 2008

Learning automata (LA) were recently shown to be valuable tools for designing multiagent reinforcement learning algorithms. One of the principal contributions of the LA theory is that a set of decentralized independent LA is able to control a finite Markov chain with unknown transition probabilities and rewards. In this paper, we propose to extend this… (More)

- Karl Tuyls, Katja Verbeeck, Tom Lenaerts
- AAMAS
- 2003

Although well understood in the single-agent framework, the use of traditional reinforcement learning (RL) algorithms in multi-agent systems (MAS) is not always justified. The feedback an agent experiences in a MAS, is usually influenced by the other agents present in the system. Multi agent environments are therefore non-stationary and convergence and… (More)

- Katja Verbeeck, Ann Nowé, Johan Parent, Karl Tuyls
- Autonomous Agents and Multi-Agent Systems
- 2006

In this paper we introduce a new multi-agent reinforcement learning algorithm, called exploring selfish reinforcement learning (ESRL). ESRL allows agents to reach optimal solutions in repeated non-zero sum games with stochastic rewards, by using coordinated exploration. First, two ESRL algorithms for respectively common interest and conflicting interest… (More)

In the present study, a large scale, structured problem regarding the routing and rostering of security personnel is investigated. Structured problems are combinatorial optimization problems that encompass characteristics of more than one known problem in operational research. The problem deals with assigning the available personnel to visits associated… (More)

- Ann Nowé, Katja Verbeeck, Peter Vrancx
- ANTS Workshop
- 2004

The present article introduces the outdoor activity tour suggestion problem (OATSP). This problem involves finding a closed path of maximal attractiveness in a transportation network graph, given a target path length and tolerance. Total path attractiveness is evaluated as the sum of the average arc attractiveness and the sum of the vertex prizes in the… (More)

- Katja Verbeeck, Ann Nowé
- IEEE Trans. Systems, Man, and Cybernetics, Part B
- 2002

Originally, learning automata (LAs) were introduced to describe human behavior from both a biological and psychological point of view. In this paper, we show that a set of interconnected LAs is also able to describe the behavior of an ant colony, capable of finding the shortest path from their nest to food sources and back. The field of ant colony… (More)

- Steven de Jong, Karl Tuyls, Katja Verbeeck
- AAMAS
- 2008

Recent advances in technology allow multi-agent systems to be deployed in cooperation with or as a service for humans. Typically, those systems are designed assuming individually rational agents, according to the principles of classical game theory. However, research in the field of behavioral economics has shown that humans are not purely self-interested:… (More)

- Peter Vrancx, Katja Verbeeck, Ann Nowé
- Adaptive Agents and Multi-Agents Systems
- 2007

Learning Automata (LA) were recently shown to be valuable tools for designing Multi-Agent Reinforcement Learning algorithms. One of the principal contributions of LA theory is that a set of decentralized, independent learning automata is able to control a finite Markov Chain with unknown transition probabilities and rewards. This result was recently… (More)