Selected Publications

(2019). Preprints:201901.0282. Artificial Swarm Intelligence and Cooperative Robotic Systems.

Preprint PDF

(2017). FTC 2017. Applying Deep Machine Learning for Psycho-Demographic Profiling of Internet Users using O.C.E.A.N. Model of Personality. Proceedings of the 2017 Future Technologies Conference (SAI) – IEEE, Vancouver, Canada, ISBN (USB) 978-1-5386-1744-1, pp. 375-384.

Preprint PDF Code Project Source Document

Recent Publications

. Preprints:201901.0282. Artificial Swarm Intelligence and Cooperative Robotic Systems, 2019.

Preprint PDF

. RG.2.2.20698.80328. Creation of Autonomous Artificial Intelligent Agents using Novelty Search method of fitness function optimization, 2018.

Preprint Code Project

. FTC 2017. Applying Deep Machine Learning for Psycho-Demographic Profiling of Internet Users using O.C.E.A.N. Model of Personality. Proceedings of the 2017 Future Technologies Conference (SAI) – IEEE, Vancouver, Canada, ISBN (USB) 978-1-5386-1744-1, pp. 375-384, 2017.

Preprint PDF Code Project Source Document

. arXiv:1708.01167. Applying advanced machine learning models to classify electro-physiological activity of human brain for use in biometric identification, 2017.

Preprint PDF Code Project Source Document

. arXiv:1703.06914. Applying Deep Machine Learning for psycho-demographic profiling of Internet users using O.C.E.A.N. model of personality, 2017.

Preprint PDF Code Project Source Document

Recent & Upcoming Talks

FTC 2017 paper presentation
Nov 29, 2017 1:30 PM

Recent Posts

For billions of years of evolution, biological intelligent agents have mastered the power to find optimal solutions in deceptive environments that we encounter in our daily interactions with the real world. We can easy navigate themselves through the maze of a big city subways and roadways. But for artificial intelligent systems, this is too difficult to be easily solved using the optimal computing resources. This is especially true for offline autonomous agents that are not backed by super power of cloud servers.

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It’s interesting to investigate combination of deep neuro-evolution and self-replication1 to evolve Artificial Neural Networks (ANNs) able to keep and complexify innate learned structures aimed to fulfill auxiliary tasks (orthogonal to the self-replication). In such a way, it may became possible to build evolutionary lineage tree of ANNs specialized to complete specific tasks under different environmental settings through subsequent training sessions. And the knowledge acquired during all these training sessions will accumulate not only in the form of learned connections’ weights, but in a neural network’s topology as well.

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The most popular method of Artificial Neural Networks (ANN) training - at the time of this essay writing - is to use some form of Gradient Descent (GD) combined with error back propagation w.r.t. objective function defining our learning goal. This methodology was invented about 30 years ago by Geoffrey Hinton and become a foundation of all modern research activities in the Deep Machine Learning (ML) and the Artificial Intelligence (AI).

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Projects

Go NEAT

The GOLang implementation of NeuroEvolution of Augmented Topologies (NEAT) method to grow and teach Artificial Neural Networks without back propagation

SSVEP Brain Hash Function

Our goal is to create Brain Hash Algorithm able to produce robust distinction between EEG signals of different humans under electro-physiological visual feedback based on steady-state visually evoked potential (SSVEP). It can be applied at variety of tasks from user authentication at online web services to user identification for physical access control systems.

Psistats

The project had a goal to provide working implementation of psycho-demographic profiling algorithm which can be used to profile Internet users based on digital footprints they leave by using various Internet services. We provide full source code implementation in R programming language of the algorithms described in corresponding research paper.

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