Vladimir G. Red’ko

Modeling of Cognitive Evolution: Perspective Direction of Interdisciplinary Investigation☆

Abstract The new direction of investigation, namely, modeling of cognitive evolution is described. The cognitive evolution is evolution of animal cognitive abilities. Fundamental scientific problems that can be analyzed by means of modeling of cognitive evolution are outlined. Backgrounds of models of cognitive evolution, which are developed in two areas of investigations: (1) models of autonomous agents and (2) biological experiments on cognitive properties of animals, are characterized. The sketch program for future investigations of cognitive evolution is proposed. Interdisciplinary relations of modeling of cognitive evolution are characterized.

Principles of Functioning of Autonomous Agent- Physicist

An interesting approach towards human-level intelligence has been proposed at BICA 2011 [1]. Namely, it is proposed that an intelligent artificial BICA agent would be able to win a political election against human candidates.

Model of Plan Formation by New Caledonian Crows

Abstract The computer model of planning the rather complex behavior by New Caledonian crows is developed and investigated. The model characterizes the following processes: 1) analysis of predictions of elementary actions, 2) generation of a simple knowledge database that describes the set of initial situations, actions, and results of actions, 3) planning a concrete chain of consecutive actions. The model is inspired by the biological experiment on New Caledonian crows.

The Natural Way to Artificial Intelligence

The chapter argues that the investigations of evolutionary processes that result in human intelligence by means of mathematical/computer models can be a serious scientific basis of AI research. The “intelligent inventions” of biological evolution (unconditional reflex, habituation, conditional reflex, ...) to be modeled, conceptual background theories (the metasystem transition theory by V.F. Turchin and the theory of functional systems by P.K. Anokhin) and modern approaches (Artificial Life, Simulation of Adaptive Behavior) to such modeling are outlined. Two concrete computer models, “Model of Evolutionary Emergence of Purposeful Adaptive Behavior” and the “Model of Evolution of Web Agents” are described. The first model is a pure scientific investigation; the second model is a step to practical applications. Finally, a possible way from these simple models to implementation of high level intelligence is outlined.

Modeling of Cognitive Evolution: Agent-Based Investigations in Cognitive Science

The new direction of investigation, namely, modeling of cognitive evolution is described. The cognitive evolution is evolution of animal cognitive abilities. Investigation of cognitive evolution is based on models of autonomous agents. The sketch program for future investigations of cognitive evolution is proposed. Initial models, which were developed in accordance with the sketch program, are characterized. In particular, the model of agents that have several natural needs, models of agent movement in mazes, accumulation of knowledge, and formation of predictions, and the model of plan formation of rather complex behavior are described.

The Concept on Nonlinear Modelling of Dynamic Objects Based on State Transition Algorithm and Genetic Programming

In this paper a new hybrid method to determine parameters of time-variant non-linear models of dynamic objects is proposed. This method first uses the State Transition Algorithm to create many local models and then applies genetic programming in order to join and simplify those models. This allows to obtain simply model which is not computationally demanding and has high accuracy.

Learning and Evolution of Autonomous Adaptive Agents

We study a model of evolving populations of self-learning agents and analyze the interaction between learning and evolution. We consider agent-brokers that predict stock price changes and use these predictions for selecting actions. Each agent is equipped with a neural network adaptive critic design for behavioral adaptation. We discuss three cases in which either learning, or evolution, or both, are active in our model. We show that the Baldwin effect can be observed in our model, viz., originally acquired adaptive policy of best agent-brokers becomes inherited over the course of the evolution. Additionally, we analyze influence of neural network structure of adaptive critic design on learning processes.

Approaches to Modeling of Nontrivial Cognitive Behavior

The approaches to modeling of nontrivial cognitive behavior of animals have been analyzed. We consider this modeling in the context of investigation of cognitive evolution. Cognitive evolution is evolution of animal cognitive abilities. The important result of cognitive evolution is the human thinking, which is used in the scientific cognition of nature. The modeling of animal cognitive behavior should be based on the corresponding biological experiments. This paper characterizes briefly the results of biological experiments on cognitive behavior of New Caledonian crows. Schemes of modeling and some results of modeling of cognitive behavior of crows have been characterized. The general relations of our approach with other researches have been also analyzed.

Model of Interaction Between Learning and Evolution

The model of interaction between learning and evolutionary optimization is designed and investigated. The evolving population of modeled organisms is considered. The mechanism of the genetic assimilation of the acquired features during a number of generations of Darwinian evolution is studied. It is shown that the genetic assimilation takes place as follows: phenotypes of modeled organisms move towards the optimum at learning; then the selection takes place; genotypes of selected organisms also move towards the optimum. The hiding effect is also studied; this effect means that strong learning can inhibit the evolutionary search for the optimal genotype. The mechanism of influence of the learning load on the interaction between learning and evolution is analyzed. It is shown that the learning load can lead to a significant acceleration of evolution.

Processes of Self-organization in the Community of Investors and Producers

The paper analyzes the processes of self-organization in the economic system that consists of investors and producers. There is intensive information exchange between investors and producers in the considered community. The model that describes the economic processes has been developed. The model proposes a specific mechanism of distribution of investors capital between producers. The model considers the interaction mechanism between investors and producers in a decentralized economic system. The main element of the interaction is the iterative process. In this process, each investor takes into account the contributions of other investors into producers. The model is investigated by means of the computer simulation, which demonstrates the effectiveness of the considered mechanism.