Max Talanov

A cognitive architecture for the implementation of emotions in computing systems

In this paper we present a new neurobiologically-inspired affective cognitive architecture: NEUCOGAR (NEUromodulating COGnitive ARchitecture). The objective of NEUCOGAR is the identification of a mapping from the influence of serotonin, dopamine and noradrenaline to the computing processes based on Von Neumans architecture, in order to implement affective phenomena which can operate on the Turings machine model. As basis of the modeling we use and extend the Lovheim Cube of Emotion with parameters of the Von Neumann architecture. Validation is conducted via simulation on a computing system of dopamine neuromodulation and its effects on the Cortex. In the experimental phase of the project, the increase of computing power and storage redistribution due to emotion stimulus modulated by the dopamine system, confirmed the soundness of the model.

Neuromodulating Cognitive Architecture: Towards Biomimetic Emotional AI

This paper introduces a new model of artificial cognitive architecture for intelligent systems, the Neuromodulating Cognitive Architecture (NEUCOGAR). The model is bio mimetically inspired and adapts the neuromodulators role of human brains into computational environments. This way we aim at achieving more efficient Artificial Intelligence solutions based on the biological inspiration of the deep functioning of human brain, which is highly emotional. The analysis of new data obtained from neurology, psychology philosophy and anthropology allows us to generate a mapping of monoamine neuro modulators and to apply it to computational system parameters. Artificial cognitive systems can then better perform complex tasks (regarding information selection and discrimination, attention, innovation, creativity) as well as engaging in affordable emotional relationships with human users.

Towards Anthropo-Inspired Computational Systems: The P^3 Model

This paper proposes a model which aim is providing a more coherent framework for agents design. We identify three closely related anthropo-centered domains working on separate functional levels. Abstracting from human physiology, psychology, and philosophy we create the

The Emotional Mechanisms in NARS

P^3

Simulation of a Fear-like State on a Model of Dopamine System of Rat Brain

model to be used as a multi-tier approach to deal with complex class of problems. The three layers identified in this model have been named PhysioComputing, MindComputing, and MetaComputing. Several instantiations of this model are finally presented related to different IT areas such as artificial intelligence, distributed computing, software and service engineering.

Thinking Lifecycle as an Implementation of Machine Understanding in Software Maintenance Automation Domain

This paper explains the conceptual design and experimental implementation of the components of NARS that are directly related to emotion. It is argued that emotion is necessary for an AGI system that has to work with insufficient knowledge and resources. This design is also compared to the other approaches in AGI research, as well as to the relevant aspects in the human brain.

“Thinking-Understanding” Approach in Spiking Reasoning System

In this paper we present the following hypothesis: the neuromodulatory mechanisms that control the emotional states of mammals could be translated and re-implemented in a computer by means of controlling the computational performance of a hosted computational system. In our specific implementation we represent the simulation of the fear-like state based on the three dimensional neuromodulatory model of affects (here the basic emotional inborn states) that we have inherited from works of Hugo Lovheim. We have managed to simulate 1000 ms of work of the dopamine system using NEST Neural Simulation Tool and the rat brain as the model. We also present the results of that simulation and evaluate them to validate the overall correctness of our hypothesis.

TU Framework in Automatic Formatting a Digital Library

The main goal of our work is to test the feasibility study of automation of incident processing in Infrastructure as Service domain to optimize the operational costs of management services that are delivered remotely. This paper also describes a framework that authors have developed to deliver an integrated incident, problem solution and resolution approach as an event-driven Automated Incident Solving System, for Remote Infrastructure Management (RIM) Model. Current approaches are mainly automated scripts, but this is a specific approach for one specific problem. Those systems can’t think. Our approach is a system that exploits a thinking model thus can think and can learn. In other words system is capable of recombining its knowledge to solve new problems. Based on Minsky [11] thinking model we have created a machine understanding prototype which is capable of learning and understanding primitive incident description texts.

Modeling Inhibitory and Excitatory Synapse Learning in the Memristive Neuron Model.

In this position paper we propose the approach to use “Thinking-Understanding” architecture for the management of the real-time operated robotic system. Based on the “Robot dream” architecture, the robotic system digital input is been translated in form of “pseudo-spikes” and provided to a simulated spiking neural network, then elaborated and fed back to a robotic system as updated behavioural strategy rules. We present the reasoning rule-based system for intelligent spike processing translating spikes into software actions or hardware signals is thus specified. The reasoning is based on pattern matching mechanisms that activates critics that in their turn activates other critics or ways to think inherited from the work of Marvin Minsky “The emotion machine” [7].

The memristive artificial neuron high level architecture for biologically inspired robotic systems

Intelligent search in the digital libraries is very important. It is very important for efficient research to obtain relevant information quickly. In the paper, we propose methods for automatic processing of online resources for the institution library using scanned copies or/and pdf files to make MathML model and provide extended search capacity. The key idea is to use Thinking–Understanding framework to provide automatic document-type detection and processing using the thinking flow to combine different open-source engines like OCR and approaches like Word2vec.