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Our products are used to:

_reduce the uncertainty of Machine Learning models,

_prioritize enterprise data efforts,

_support experts in the ML loop,

_improve the quality of ML models, especially in multi-class settings with complex ontologies,

_reduce data footprint and compactify ML models so as to be used by the Internet of Things applications,

_improve gaming experience via more challenging and realistic AI in games,

_create intelligent advisory systems from pre-compiled building blocks.


Maciej Świechowski · Konrad Godlewski · Bartosz Sawicki · Jacek Mańdziuk

Monte Carlo Tree Search: a review of recent modifications and applications

Monte Carlo Tree Search (MCTS) is a powerful approach to designing game-playing bots
or solving sequential decision problems. The method relies on intelligent tree search that
balances exploration and exploitation. MCTS performs random sampling in the form of
simulations and stores statistics of actions to make more educated choices in each subsequent iteration. The method has become a state-of-the-art technique for combinatorial games....
Andrzej Janusz, Łukasz Grad, Marek Grzegorowski,

Clash Royale Challenge: How to Select Training Decks for Win-rate Prediction

We summarize the sixth data mining competition organized at the Knowledge Pit platform in association with the Federated Conference on Computer Science and Information Systems series, titled Clash Royale Challenge: How to Select Training Decks for Win-rate Prediction. We outline the scope of this challenge and briefly present its results. We also discuss the problem of acquiring knowledge about new notions from video games through an active...
Maciej Świechowski, Jacek Mańdziuk

Fast Interpreter for Logical Reasoning in General Game Playing

In this paper, we present an efficient construction of the Game Description Language (GDL) interpreter. GDL is a first-order logic language used in the General Game Playing (GGP) framework. Syntactically, the language is a subset of Datalog and Prolog, and like those two, is based on facts and rules. Our aim was to achieve higher execution speed than anyone’s of the currently available tools, including other Prolog interpreters...
Tomasz Tajmajer, Andrzej Janusz, Maciej Świechowski

Helping AI to Play Hearthstone: AAIA’17 Data Mining Challenge

This paper summarizes the AAIA’17 Data MiningChallenge: Helping AI to Play Hearthstone which was held between March 23, and May 15, 2017 at the Knowledge Pit platform. We briefly describe the scope and background of this competition in the context of a more general project related to the development of an AI engine for video games, called Grail. We also discuss the outcomes of this challenge and demonstrate how predictive models for...
Andrzej Janusz, Sebastian Stawicki, Michał Drewniak, Krzysztof Ciebiera, Dominik Ślęzak, Krzysztof Stencel

How to Match Jobs and Candidates – A Recruitment Support System Based on Feature Engineering and Advanced Analytics

We describe a recruitment support system aiming to help recruiters in finding candidates who are likely to be interested in a given job offer. We present the architecture of that system and explain roles of its main modules. We also give examples of analytical processes supported by the system. In the paper, we focus on a data processing chain that utilizes domain knowledge for the extraction of meaningful features representing...
Tomasz Tajmajer, Andrzej Janusz, Maciej Świechowski

Improving Hearthstone AI by Combining MCTS and Supervised Learning Algorithms

We investigate the impact of supervised prediction models on the strength and efficiency of artificial agents that use the Monte-Carlo Tree Search (MCTS) algorithm to play a popular video game Hearthstone: Heroes of Warcraft. We overview our custom implementation of the MCTS that is well-suited for games with partially hidden information and random effects. We also describe experiments which we designed to quantify the performance of our...
Tomasz Tajmajer

Modular Multi-Objective Deep Reinforcement Learning with Decision Values

In this work we present a method for using Deep Q-Networks (DQNs) in multi-objective environments. Deep QNetworks provide remarkable performance in single objective problems learning from high-level visual state representations. However, in many scenarios (e.g in robotics, games), the agent needs to pursue multiple objectives simultaneously. We propose an architecture in which separate DQNs are used to control the agent’s behaviour...
Andrzej Janusz, Marek Grzegorowski, Marcin Michalak, Łukasz Wróbel, Marek Sikora, Dominik Ślęzak

Predicting seismic events in coal mines based on underground sensor measurements

In this paper, we address the problem of safety monitoring in underground coal mines. In particular, we investigate and compare practical methods for the assessment of seismic hazards using analytical models constructed based on sensory data and domain knowledge. For our case study, we use a rich data set collected during a period of over five years from several active Polish coal mines. We focus on comparing the prediction quality...
Tomasz Tajmajer, Andrzej Janusz, Maciej Świechowski, Łukasz Grad, Jacek Puczniewski, Dominik Ślęzak

Toward an Intelligent HS Deck Advisor: Lessons Learned from AAIA’18 Data Mining Competition

We summarize AAIA’18 Data Mining Competition organized at the Knowledge Pit platform. We explain the competition’s scope and outline its results. We also review several approaches to the problem of representing Hearthstone decks in a vector space. We divide such approaches into categories based on a type of the data about individual cards that they use. Finally, we outline experiments aiming to evaluate usefulness of...
Maciej Świechowski, HyunSoo Park , Jacek Mańdziuk, Kyung-Joong Kim

Recent Advances in General Game Playing

The goal of General Game Playing (GGP) has been to develop computer programs that can perform well across various game types. It is natural for human game players to transfer knowledge from games they already know how to play to other similar games. GGP research attempts to design systems that work well across different game types, including unknown new games. In this review, we present a survey of recent advances (2011 to 2014) in GGP...
Andrzej Janusz, Dominik Ślęzak

Rough Set Methods for Attribute Clustering and Selection

In this study we investigate methods for attribute clustering and their possible applications to the task of computation of decision reducts from information systems. We focus on high-dimensional datasets, that is, microarray data. For this type of data, the traditional reduct construction techniques either can be extremely computationally intensive or can yield poor performance in terms of the size of the resulting reducts. We propose two...
Maciej Świechowski, Jacek Mańdziuk

Self-Adaptation of Playing Strategies in General Game Playing

The term General Game Playing (GGP) refers to a subfield of Artificial Intelligence which aims at developing agents able to effectively play many games from a particular class (finite, deterministic). It is also the name of the annual competition proposed by Stanford Logic Group at Stanford University, which provides a framework for testing and evaluating GGP agents. In this paper we present our GGP player which managed to win 4...
Andrzej Janusz, Dominik Ślęzak, Sebastian Stawicki, Krzysztof Stencel

SENSEI: An Intelligent Advisory System for the eSport Community and Casual Players

In this article, we describe the SENSEI system. It helps players to improve their skills in popular eSports games. We discuss the main goals of the system and explain the associated challenges. We also present its conceptual architecture which aims at enabling full automation of the data acquisition and analytic processes. The system is expected to provide in-depth analytics of players’ performance and give practical advice regarding...

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