Adaptive Behaviour in Evolving Robots
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In this thesis, the evolution of adaptive behaviour in artificial agents is studied. More specifically, two types of adaptive behaviours are studied: articulated and cognitive ones. Chapter 1 presents a general introduction together with a brief presentation of the research area of this thesis, its main goals and a brief overview of the experimental studies done, the results and conclusions obtained. On chapter 2, I briefly present some promising methods that automatically generate robot controllers and/or body plans and potentially could help in the development of adaptive robots. Among these methods I present in details evolutionary robotics, a method inspired on natural evolution, and the biological background regarding adaptive behaviours in biological organisms, which provided inspiration for the studies presented in this thesis. On chapter 3, I present a detailed study regarding the evolution of articulated behaviours, i.e., behaviours that are organized in functional sub-parts, and that are combined and used in a sequential and context-dependent way, regardless if there is a structural division in the robot controller or not. The experiments performed with a single goal task, a cleaning task, showed that it is possible to evolve articulated behaviours even in this condition and without structural division of the robot controller. Also the analysis of the results showed that this type of integrated modular behaviours brought performance advantages compared to structural divided controllers. Analysis of robots' behaviours helped to clarify that the evolution of this type of behaviour depended on the characteristics of the neural network controllers and the robot's sensorimotor capacities, that in turn defined the capacity of the robot to generate opportunity for actions, which in psychological literature is often called affordances. In chapter 4, a study seeking to understand the role of reactive strategies in the evolution of cognitive solutions, i.e. those capable of integrating information over time encoding it on internal states that will regulate the robot's behaviour in the future, is presented. More specifically I tried to understand whether the existence of sub-optimal reactive strategies prevent the development of cognitive solutions, or they can promote the evolution of solutions capable of combining reactive strategies and the use of internal information for solving a response delayed task, the double t-maze. The results obtained showed that reactive strategies capable of offloading cognitive work to the agent/environmental relation can promote, rather than prevent the evolution of solutions relying on internal information. The analysis of these results clarified how these two mechanisms interact producing a hybrid superior and robust solution for the delayed response task.
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