Omni-directional catadioptric vision for soccer robots

This paper describes the design of a multi-part mirror catadioptric vision system and its use for self-localization and detection of relevant objects in soccer robots. The mirror and associated algorithms have been used in robots participating in the middle-size league of RoboCup — The World Cup of Soccer Robots.This work was supported by grant PRAXIS XXI BM/21091/99 of the Portuguese Foundation for Science and Technolog

Learning to compose fuzzy behaviors for autonomous agents.

In this paper, we present S-ELF, an evolutionary algorithm that we have developed to learn the context of activation of fuzzy logic controllers implementing fuzzy behaviors for autonomous agent. S-ELF learns context metarules that are used to coordinate basic behaviors in order to perform complex tasks in a partially and imprecisely known environment. Context metarules are expressed in terms of positive and negated fuzzy predicates. We also show how S-ELF can learn robust and portable behaviors, thus reducing the time and e ort to design behavior-based agent

Evaluation of genetic-fuzzy systems in the configuration space

We propose an approach to ground the design of learning systems on the analysis of the configuration space of the learning device (e.g., a robot) and on the interpretation of input data. In this paper, we focus on Learning Fuzzy Classifier Systems adopted to evolve behavioral controllers for autonomous robots. We show how it is possible to define some indexes to evaluate objectively both the learning process and the evolved system, thus supporting their designing with engineering principles

Soft Computing Applications (Advances in Intelligent and Soft Computing)

The papers collected in this book are concerned with the application of the so-called "soft-computing" techniques to the aim of defining flexible systems. The topics covered witness the actual research trend towards an integration of distinct formal techniques for defining flexible systems. The contributions in this volume mainly concern the definition of systems in several application fields, such as image processing, control, asset allocation, medicine, time series forecasting, qualitative modeling, support to design, reliability analysis, diagnosis, filtering, data analysis, land mines detection and so forth. The papers presented in this volume are organized into three main thematic sections: Fuzzy Systems, Neural Networks and Genetic and Evolutionary Algorithms, although, as outlined before, some works employ more than one technique from these fields

Reinforcement Distribution in Fuzzy Q-Learning

Q-learning is one of the most popular reinforcement learning methods that allows an agent to learn the relationship between interval-valued state and action spaces, through a direct interaction with the environment. Fuzzy Q-learning is an extension to this algorithm to enable it to evolve fuzzy inference systems (FIS) which range on continuous state and action spaces. In a FIS, the interaction among fuzzy rules plays a primary role to achieve good performance and robustness. Learning a system where this interaction is present gives to the learning mechanism problems due to eventually incoherent reinforcements coming to the same rule due to its interaction with other rules. In this paper, we will introduce different strategies to distribute reinforcement to reduce this undesired effect and to stabilize the obtained reinforcement. In particular, we will present two strategies: the former focuses on rewarding the actions chosen by each rule during the cooperation phase, the latter on rewarding the rules presenting actions closer to those actually executed rather than the rules that contributed to generate such actions