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Researcher profile

Anton Kolonin

Anton Kolonin contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Artificial IntelligenceComputational Engineering, Finance, and ScienceMachine LearningComputation and LanguageInformation Retrievalmath.NAMultiagent SystemsNeurons and CognitionNumerical Analysisq-fin.STq-fin.TRSocial and Information Networks

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Published work

8 published item(s)

preprint2026arXiv

Interpretable experiential learning based on state history and global feedback

A new interpretable experiential learning model based on state history and global feedback is presented. It is capable of learning a behavioral model represented by a transition graph between sets of states, with transitions attributed with utility and evidence count. This model is expected to be suitable for solving reinforcement learning problem in resource-constrained environments. The model was thoroughly evaluated on the OpenAI Gym Atari Breakout benchmark, demonstrating performance comparable to some known neural network-based solutions.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Adaptive Multi-Strategy Market-Making Agent For Volatile Markets

Crypto-currency market uncertainty drives the need to find adaptive solutions to maximise gain or at least to avoid loss throughout the periods of trading activity. Given the high dimensionality and complexity of the state-action space in this domain, it can be treated as a "Narrow AGI" problem with the scope of goals and environments bound to financial markets. Adaptive Multi-Strategy Agent approach for market-making introduces a new solution to maximise positive "alpha" in long-term handling limit order book (LOB) positions by using multiple sub-agents implementing different strategies with a dynamic selection of these agents based on changing market conditions. AMSA provides no specific strategy of its own while being responsible for segmenting the periods of market-making activity into smaller execution sub-periods, performing internal backtesting on historical data on each of the sub-periods, doing sub- agent performance evaluation and re-selection of them at the end of each sub- period, and collecting returns and losses incrementally. With this approach, the return becomes a function of hyper-parameters such as market data granularity (refresh rate), the execution sub-period duration, number of active sub-agents, and their individual strategies. Sub-agent selection for the next trading sub-period is made based on return/loss and alpha values obtained during internal backtesting as well as real trading. Experiments with the AMSA have been performed under different market conditions relying on historical data and proved a high probability of positive alpha throughout the periods of trading activity in the case of properly selected hyper-parameters.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Brain Principles Programming

In the monograph, STRONG ARTIFICIAL INTELLIGENCE. On the Approaches to Superintelligence, published by Sberbank, provides a cross-disciplinary review of general artificial intelligence. As an anthropomorphic direction of research, it considers Brain Principles Programming, BPP) the formalization of universal mechanisms (principles) of the brain's work with information, which are implemented at all levels of the organization of nervous tissue. This monograph provides a formalization of these principles in terms of the category theory. However, this formalization is not enough to develop algorithms for working with information. In this paper, for the description and modeling of Brain Principles Programming, it is proposed to apply mathematical models and algorithms developed by us earlier that model cognitive functions, which are based on well-known physiological, psychological and other natural science theories. The paper uses mathematical models and algorithms of the following theories: P.K.Anokhin's Theory of Functional Brain Systems, Eleonor Rosh's prototypical categorization theory, Bob Rehter's theory of causal models and natural classification. As a result, the formalization of the BPP is obtained and computer examples are given that demonstrate the algorithm's operation.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Causal Analysis of Generic Time Series Data Applied for Market Prediction

We explore the applicability of the causal analysis based on temporally shifted (lagged) Pearson correlation applied to diverse time series of different natures in context of the problem of financial market prediction. Theoretical discussion is followed by description of the practical approach for specific environment of time series data with diverse nature and sparsity, as applied for environments of financial markets. The data involves various financial metrics computable from raw market data such as real-time trades and snapshots of the limit order book as well as metrics determined upon social media news streams such as sentiment and different cognitive distortions. The approach is backed up with presentation of algorithmic framework for data acquisition and analysis, concluded with experimental results, and summary pointing out at the possibility to discriminate causal connections between different sorts of real field market data with further discussion on present issues and possible directions of the following work.

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preprint2022arXiv

Cognitive Architecture for Decision-Making Based on Brain Principles Programming

We describe a cognitive architecture intended to solve a wide range of problems based on the five identified principles of brain activity, with their implementation in three subsystems: logical-probabilistic inference, probabilistic formal concepts, and functional systems theory. Building an architecture involves the implementation of a task-driven approach that allows defining the target functions of applied applications as tasks formulated in terms of the operating environment corresponding to the task, expressed in the applied ontology. We provide a basic ontology for a number of practical applications as well as for the subject domain ontologies based upon it, describe the proposed architecture, and give possible examples of the execution of these applications in this architecture.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Social Media Sentiment Analysis for Cryptocurrency Market Prediction

In this paper, we explore the usability of different natural language processing models for the sentiment analysis of social media applied to financial market prediction, using the cryptocurrency domain as a reference. We study how the different sentiment metrics are correlated with the price movements of Bitcoin. For this purpose, we explore different methods to calculate the sentiment metrics from a text finding most of them not very accurate for this prediction task. We find that one of the models outperforms more than 20 other public ones and makes it possible to fine-tune it efficiently given its interpretable nature. Thus we confirm that interpretable artificial intelligence and natural language processing methods might be more valuable practically than non-explainable and non-interpretable ones. In the end, we analyse potential causal connections between the different sentiment metrics and the price movements.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Controlled Language and Baby Turing Test for General Conversational Intelligence

General conversational intelligence appears to be an important part of artificial general intelligence. Respectively, it requires accessible measures of the intelligence quality and controllable ways of its achievement, ideally - having the linguistic and semantic models represented in a reasonable way. Our work is suggesting to use Baby Turing Test approach to extend the classic Turing Test for conversational intelligence and controlled language based on semantic graph representation extensible for arbitrary subject domain. We describe how the two can be used together to build a general-purpose conversational system such as an intelligent assistant for online media and social network data processing.

0 citations0 reviewsNo code linkedOpen access