TOWARDS EVOLUTIONARY DESIGN OF COMPLEX SYSTEMS INSPIRED BY NATURE

This paper presents first steps towards evolutionary design of complex autonomous systems. The approach is inspired in modularity of human brain and principles of evolution. Rather than evolving neural networks or neural-based systems, the approach focuses on evolving hybrid networks composed of heterogeneous sub-systems implementing various algorithms/behaviors. Currently, the evolutionary techniques are used to optimize weights between predefined blocks (so called Neural Modules) in order to find an agent architecture appropriate for given task. The framework, together with the simulator of such systems is presented. Then, examples of agent architectures represented as hybrid networks are presented. One architecture is hand-designed and one is automatically optimized by means of evolutionary algorithm. Even on such a simple experiment, it can be observed how the evolution is able to pick-up unexpected attributes of the task and exploit them when designing new architecture

Prvotní názory na vztah duše a těla, pojetí psychiky (duševní činnosti) v současné psychologii

Práce se zabývá názory na vztah duše a těla, pojetím psychiky v současné psychologii, psychickými procesy a stavy osobnosti.Dokončená práce s úspěšnou obhajobo

LEVELS OF CONSCIOUS AND UNCONSCIOUS ANTICIPATORY BEHAVIOUR FOR ARTIFICIAL CREATURES

Recently, anticipation and anticipatory learning systems have gained increasing attention in the field. The interest of researchers in anticipation did not started over night. Anticipation observed in the animals combined with the multi-agent systems and artificial life gave birth to the anticipatory behaviour. This is broad multidisciplinary topic, but there are little thoughts on relation of anticipation with the reactive behaviour, the similarities and where the boundary is. Reactive behaviour is still considered as the exact opposite for the anticipatory one. It was shown by us that reactive and anticipatory behaviour can be combined. Designed multi-level anticipatory behaviour approach is based on the current understanding of anticipation from both the artificial intelligence and the biology point of view. Original thought is that we use not one but multiple levels of unconscious and conscious anticipation in a creature design. The topic is quite comprehensive and is out of scope of a single article to describe all 8 levels of the 8-factor anticipation framework design. The aim is not to extensively present all the achieved results but to demonstrate the thinking behind. Primary industrial application of this approach is intelligent robotics

Visualization of dynamic behaviour of multi-agent systems

The extension of our research on analysis of a single agent or agent communities combining advanced methods of visualization with traditional AI techniques is presented in this paper. Even though this approach can be used for arbitrary Multi-Agent System (MAS), it was primarily developed to analyze systems falling into Artificial Life domain. Traditional methods are becoming insufficient as Multi-Agent Systems (MAS) are becoming more complex and therefore novel approaches are needed. In this paper we present an extension of our recent visualization tools suite. The previous approach was not suitable well to present the dynamics of the MAS, even though the development of MAS state parameters in time was presented. Our new technique, which is presented in this paper, addresses this problem by visualizing the changes of the MAS along with their quality and context. This transparent approach emphasizes MAS dynamics by providing means for discovery of changes in its tendencies or in behaviour of either single agent or agent communities. A simulated artificial life environment with intelligent agents has been used as a test bed. We have selected this particular domain because our longterm goal is to model life as it could be so as to understand life, as we know it

SELF-MOTIVATED AUTONOMOUSE AGENTS

Abstract In this paper, we describe our design of a behaviour-based control system for autonomous agents with accent on self-control and self-motivation aspects of the agent control. Agents could be represented by simulated agents – animates or by physical robots. The traditional agent behaviour is usually initialised by external signals and motivations. On the other side in the nature systems we can observe that behaviour is directly affected by internal motivational sources in the nature systems. Above all, we put emphasis on their co-ordination and mutual co-operation ability by the control of particular robot definition. To realise such type of control, we work with combination of reactive and deliberative methods. This concept of the agent architecture enables to define quite complex behaviour and consider multiple behaviour tendencies simultaneously in one moment. We will also demonstrate advantages of this motivationally based concept of the control system in an experimental task. Key words: autonomous agent, behaviour–based control systems, motivational system, self-control