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Products
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Incubation
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Incubation
QED Software has a long record of supporting pioneers in their quest for establishing or growing business based on Artificial Intelligence advantages.
Advanced Artificial Intelligence methods are the DNA of our associated pioneers’ offering and their differentiator on the market. We provide our associated pioneers with technical expertise that defines core functionality, competitive advantage, distinguishing features or enablement for a new type of functionality of their products.
Our associated pioneers apply our core competencies to develop privacy-preserving computer vision & edge computing solutions, as well as log analytics for cybersecurity.
Looking for a technological partner to enable your AI-based growth? Contact us
BlindBox AraHub ONSTAGE
Advanced Artificial Intelligence methods are the DNA of our associated pioneers’ offering and their differentiator on the market. We provide our associated pioneers with technical expertise that defines core functionality, competitive advantage, distinguishing features or enablement for a new type of functionality of their products.
Our associated pioneers apply our core competencies to develop privacy-preserving computer vision & edge computing solutions, as well as log analytics for cybersecurity.
Looking for a technological partner to enable your AI-based growth? Contact us
BlindBox AraHub ONSTAGE
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Services
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Services
If you could fix one thing in your business operations with AI, what would that be?
And what would the impact be?
And what would the impact be?
Request a demo
We can arrange a demo to show you the capabilities of our products
DEMONSTRATIVE REALIZATIONS
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.
Research
Andrzej Janusz, Łukasz Grad, Marek Grzegorowski,
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...
