Neurosymbolic Integration for Industrial Applications

Colloque avec actes et comité de lecture.Today, symbolic and connectionist artificial intelligence have proved their complementary efficiency for various aspects of cognitive processing. Both approaches can be seen as complementarily acting on specific parts of information, namely data and knowledge. Such a dichotomy can also be observed as one consider real world applications. A general theory is rarely available to build a complete knowledge based system. Conversely, data can generally be extracted from the problem but never cover the whole problem. Accordingly, the idea has emerged that the combination of symbolic and connectionist tools could be a way to benefit from the advantages of both approaches. Neurosymbolic integration that will be presented here is the domain whose goal is to define strategies and propose tools for the cooperation of symbolic and connectionist artificial intelligence

Frustration as a generical regulatory mechanism for motivated navigation

International audienceThis paper explores the use of a mechanism to auto-regulate the robot behavior in situations of persistent failures. In order to give more autonomy to a mobile robot, a generical frustration mechanism based on the automonitoring of the progress (in terms of goal distance reduction) is studied in different situations and on different parts of the robot architecture. To escape failure situations and deadlocks, the frustration reaction can inhibit the robot navigation strategies, goals or drives

Toward Psycho-robots

We try to perform geometrization of psychology by representing mental states, >, by points of a metric space, >. Evolution of ideas is described by dynamical systems in metric mental space. We apply the mental space approach for modeling of flows of unconscious and conscious information in the human brain. In a series of models, Models 1-4, we consider cognitive systems with increasing complexity of psychological behavior determined by structure of flows of ideas. Since our models are in fact models of the AI-type, one immediately recognizes that they can be used for creation of AI-systems, which we call psycho-robots, exhibiting important elements of human psyche. Creation of such psycho-robots may be useful improvement of domestic robots. At the moment domestic robots are merely simple working devices (e.g. vacuum cleaners or lawn mowers) . However, in future one can expect demand in systems which be able not only perform simple work tasks, but would have elements of human self-developing psyche. Such AI-psyche could play an important role both in relations between psycho-robots and their owners as well as between psycho-robots. Since the presence of a huge numbers of psycho-complexes is an essential characteristic of human psychology, it would be interesting to model them in the AI-framework

Learning Reactive and Planning Rules in a Motivationally Autonomous Animat

This work describes a control architecture based on a hierarchical classifier system. This system, which learns both reactive and planning rules, implements a motivationally autonomous animat that chooses the actions it performs according to its perception of the external environment, to its physiological or internal state, to the consequences of its current behavior, and to the expected consequences of its future behavior. The adaptive faculties of this architecture are illustrated within the context of a navigation task, through various experiments with a simulated and a real robot. I. Introduction The work presented in this paper fits into the so-called animat approach, which aims at designing animats, i.e., simulated animals or real robots whose rules of behavior are inspired by those of animals. The proximate goal of this approach is to discover architectures or working principles that allow an animal or a robot to exhibit an adaptive behavior and, thus, to survive or fulfill i..

An action selection architecture for autonomous virtual humans in persistent worlds

Nowadays, virtual humans such as non-player characters in computer games need to have a strong autonomy in order to live their own life in persistent virtual worlds. When designing autonomous virtual humans, the action selection problem needs to be considered, as it is responsible for decision making at each moment in time. Indeed action selection architectures for autonomous virtual humans need to be reactive, proactive, motivational, and emotional to obtain a high degree of autonomy and individuality. The thesis can be divided into three parts. In the first part, we define each word of our title to precise their sense and raise the problematic of this work. We describe also inspirations from several domains that we used to design our model because this thesis is highly multi-disciplinary. Indeed, decision-making is essential for every autonomous entity and is studied in ethology, robotics, computer graphics, computer sciences, and cognitive sciences. However, we have chosen specific techniques to implement our model: hierarchical classifier systems and a free flow hierarchy. The second part of this thesis describes in detail our model of action selection for autonomous virtual humans. We use overlapping hierarchical classifier systems, working in parallel, to generate coherent behavioral plans. They are associated with the functionalities of a free flow hierarchy for the spreading of activation to give reactivity and flexibility to the hierarchical system. Moreover several functionalities are added to enhance and facilitate the choice of the most appropriate action at every time according to the internal and external influences. Finally, in the third part of this thesis, a complex simulated environment is created for testing the model and its functionalities with many conflicting motivations. Results demonstrate that the model is sufficiently efficient, robust and flexible for designing motivational autonomous virtual humans in persistent worlds. Moreover, we have just started to investigate on the emotional level which has to be improved in the future to have more subjective and adaptive behaviors and also manage social interactions with other virtual humans or users. Applied to video games, non player characters are more interesting and believable because they live their own life when people don't interact with them

Sistemas inteligentes de navegação autonoma : uma abordagem modular e hierarquica com novos mecanismos de memoria e aprendizagem

Orientadores : Fernando Jose Von Zuben, Mauricio Fernandes FigueiredoDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de ComputaçãoMestrad