Liljana Bozinovska

SELF-LEARNING AGENTS: A CONNECTIONIST THEORY OF EMOTION BASED ON CROSSBAR VALUE JUDGMENT

This article presents a computational theory of emotions, based on the theory of emotion as value judgment and appraisal. It assumes that what we feel about other people, events, and things, generally indicates how we evaluate them. The basic assumption is that emotion is an internal self-evaluation of something relevant for the existence of the agent, like self-evaluation of the global state the agent is in, and the behavior the agent is about to perform. This work presents an agent architecture which contains the three components of the control system in biological systems?the genetic, neural, and hormonal component. As distillate of the theory, a working architecture that implements value judgment is presented. The architecture is based on a crossbar connectionist adaptive array, which is designed in a way that it computes from the same crossbar memory elements, both emotions toward encountered situations and emotions toward action tendencies. In such a way it actually builds in hardware, inseparable c...

MEXYS2: A Fuzzy Reasoning Expert System Based on the Subject Emotions Consideration

An expert, system for personality and emotional profile assessment is developed and here reported. The expert knowledge and the basic computational procedure are based on a standard psychological test. Fuzzy reasoning and uncertainty assessment are also features of this system.

Human body parts making: Educational challenges for Engineered Physiology, biorobotics, and biofabrication

This short paper describes a Masters program design to meet the educational demand for body parts making, both from physiological and non-physiological nature (prostheses). The paper gives a new definition of an organ to allow both types of human body parts. It also introduces the concept of Engineered Physiology in the educational system.

Artificial chromosomes as genetic disks: A systems software metaphor for genetic engineering

This paper is a further elaboration on the operating system and systems software metaphor for understanding genetic information processing. Here the focus is on engineering artificial chromosomes: we propose understanding of the process in term of creating genetic files and genetic disks. The paper points out possibilities of using such disks in combination with stem cells for organ engineering. The paper also discusses array of transgenic disks, a human genomic library distributed over colony of bacteria.

Modeling Lamarckian Evolution: From Structured Genome to a Brain-Like System

The paper addresses development of a brain-like system based on Lamarckian view toward evolution. It describes a development of an artificial brain, from an artificial genome, through a neural stem cell. In the presented design a modulon level of genetic hierarchical control is used. In order to evolve such a system, two environments are considered, genetic and behavioral. The genome comes from the genetic environment, evolves into an artificial brain, and then updates the memory through interaction with the behavioral environment. The updated genome can then be sent back to the genetic environment. The memory units of the artificial brain are synaptic weights which in this paper represent achievement motivations of the agent, so they are updated in relation to particular achievement. A simulation of the process of learning by updating achievement motivations in the behavioral environment is also shown.

Evolving an emotion-based neural control architecture

This work addresses a central problem in evolvable systems - evolution of a neural based emotion integrated learning phenotype from an initial genotype. It describes morphogenesis of a cognitive system based on a neural network, starting from a single genotype having a modulon control structure. It further presents an axon growing mechanism as second step of neural evolution. The neural architecture presented here has the ability to evolve to an emotion integrated neural leaning architecture.

AV control system which makes use of environment stabilizations

In the present paper we propose a new, biologically inspired conceptual scheme for introducing parallelism, redundancy and learning in control systems for autonomous vehicles (AV). AV are regarded as a special class of autonomous agents (AA). Most generally stated, an AA is defined with the set of its percepts (S), the set of its elementary actions (A), and its internal structure. Within our scheme expectation is the key concept, and an agent is said to be aware of its environment if it can anticipate the effects of the actions (A) it applies in particular situations (S). If the environment stabilizes for awhile, the effects of application of a particular action will remain same for particular situation. We can take advantage of this fact and avoid recomputation of the optical action in that situation by applying instead, the action generated by the stored anticipations for that situation. In the introduction we give a brief overview of the present AA architectures in the domain of autonomous vehicles and outline the basics of our architecture. In the next section the expecting agent is described in more details. The last section is devoted to an example of implementation of this scheme within the domain of obstacle avoiding path finding autonomous vehicles control. The implementation details and the simulation results of three experiments are discussed.

Robot - assisted therapy for autistic children

Using robots as an assistive technology gave rise to Robot Assisted Therapy. It is one of the fastest-growing fields in the research area. Many target groups benefit from the rehabilitative interaction with robots, especially humanoid ones. One of the most important groups are the children suffering from Autism Spectrum Disorder (ASD). Over the past fifteen years there has been a lot of progress in treating autistic children through robotic interaction for therapeutic purposes. This paper presents the work done in implementing a NAO humanoid robot in a research experiment with ASD children.

Genetic attraction force and physical gene search

Genetic attraction force is introduced as concept explaining methods of search for a physical DNA or RNA fragments. Search molecules are discussed including molecular beacons. The discussion uses both robotics and systems software metaphor for understanding genetics and genetic engineering.

Genetic Engineering in a Computer Science Curriculum

Traditionally genetic engineering is understood as a molecular biology discipline. The tools used in molecular biology are specific, mostly used by people who come from biological or medical background, which made the discipline distant from classical Computer Science. In this paper we would like to address computer science auditorium and point out the importance of understanding genetic engineering.