Evolution of Symbolisation in Chimpanzees and Neural Nets

from Introduction: Animal communication systems and human languages can be characterised by the type of cognitive abilities that are required. If we consider the main semiotic distinction between communication using icons, signals, or symbols (Peirce, 1955; Harnad, 1990; Deacon, 1997) we can identify different cognitive loads for each type of reference. The use and understanding of icons require instinctive behaviour (e.g. emotions) or simple perceptual processes (e.g. visual similarities between an icon and its meaning). Communication systems that use signals are characterised by referential associations between objects and visual or auditory signals. They require the cognitive ability to learn stimulus associations, such as in conditional learning. Symbols have double associations. Initially, symbolic systems require the establishment of associations between signals and objects. Secondly, other types of relationships are learned between the signals themselves. The use of rule for the logical combination of symbols is an example of symbolic relationship. Symbolisation is the ability to acquire and handle symbols and symbolic relationships

Unconventional Cognitive Intelligent Robotic Control: Quantum Soft Computing Approach in Human Being Emotion Estimation -- QCOptKB Toolkit Application

Strategy of intelligent cognitive control systems based on quantum and soft computing presented. Quantum self-organization knowledge base synergetic effect extracted from intelligent fuzzy controllers imperfect knowledge bases described. That technology improved of robustness of intelligent cognitive control systems in hazard control situations described with the cognitive neuro-interface and different types of robot cooperation. Examples demonstrated the introduction of quantum fuzzy inference gate design as prepared programmable algorithmic solution for board embedded control systems. The possibility of neuro-interface application based on cognitive helmet with quantum fuzzy controller for driving of the vehicle is shown

Design concepts for the development of cooperative problem-solving systems

There are many problem-solving tasks that are too complex to fully automate given the current state of technology. Nevertheless, significant improvements in overall system performance could result from the introduction of well-designed computer aids. We have been studying the development of cognitive tools for one such problem-solving task, enroute flight path planning for commercial airlines. Our goal was two-fold. First, we were developing specific systems designs to help with this important practical problem. Second, we are using this context to explore general design concepts to guide in the development of cooperative problem-solving systems. These designs concepts are described

Complex and Adaptive Dynamical Systems: A Primer

An thorough introduction is given at an introductory level to the field of quantitative complex system science, with special emphasis on emergence in dynamical systems based on network topologies. Subjects treated include graph theory and small-world networks, a generic introduction to the concepts of dynamical system theory, random Boolean networks, cellular automata and self-organized criticality, the statistical modeling of Darwinian evolution, synchronization phenomena and an introduction to the theory of cognitive systems. It inludes chapter on Graph Theory and Small-World Networks, Chaos, Bifurcations and Diffusion, Complexity and Information Theory, Random Boolean Networks, Cellular Automata and Self-Organized Criticality, Darwinian evolution, Hypercycles and Game Theory, Synchronization Phenomena and Elements of Cognitive System Theory.Comment: unformatted version of the textbook; published in Springer, Complexity Series (2008, second edition 2010

Bounded Rationality and Heuristics in Humans and in Artificial Cognitive Systems

In this paper I will present an analysis of the impact that the notion of “bounded rationality”, introduced by Herbert Simon in his book “Administrative Behavior”, produced in the field of Artificial Intelligence (AI). In particular, by focusing on the field of Automated Decision Making (ADM), I will show how the introduction of the cognitive dimension into the study of choice of a rational (natural) agent, indirectly determined - in the AI field - the development of a line of research aiming at the realisation of artificial systems whose decisions are based on the adoption of powerful shortcut strategies (known as heuristics) based on “satisficing” - i.e. non optimal - solutions to problem solving. I will show how the “heuristic approach” to problem solving allowed, in AI, to face problems of combinatorial complexity in real-life situations and still represents an important strategy for the design and implementation of intelligent systems

The Marital and Physician Privileges—A Reprint of a Letter to a Congressman

A design of computer systems, that effectively supports the user, is a major goal within human-computer interaction. To achieve this, we must understand and master several tasks. These tasks concern firstly what to develop and secondly how to develop the system. The design and implementation of effective and efficient user interfaces is a prerequisite for the successful introduction of computer support in the medical domain. We base our work on a fundamental understanding of cognitive aspects of human-computer interaction, as well as on detailed analysis of the specific needs and requirements of the end users, i.e., the medical professionals. This thesis presents several approaches for development of systems for computer-supported work in health care. The solutions described concern vital problem areas: (1) the focus on the work tasks to be performed, (2) the cost of software and the way competition works in a networked world. Solutions to these problems can lead to more usable systems from a user's perspective but may also change the nature of computer applications