Adam Żychowski | Warsaw University of Technology (original) (raw)

Papers by Adam Żychowski

Research paper thumbnail of Coevolutionary Algorithm for Building Robust Decision Trees under Minimax Regret

Proceedings of the ... AAAI Conference on Artificial Intelligence, Mar 24, 2024

In recent years, there has been growing interest in developing robust machine learning (ML) model... more In recent years, there has been growing interest in developing robust machine learning (ML) models that can withstand adversarial attacks, including one of the most widely adopted, efficient, and interpretable ML algorithms-decision trees (DTs). This paper proposes a novel coevolutionary algorithm (CoEvoRDT) designed to create robust DTs capable of handling noisy high-dimensional data in adversarial contexts. Motivated by the limitations of traditional DT algorithms, we leverage adaptive coevolution to allow DTs to evolve and learn from interactions with perturbed input data. CoEvoRDT alternately evolves competing populations of DTs and perturbed features, enabling construction of DTs with desired properties. CoEvoRDT is easily adaptable to various target metrics, allowing the use of tailored robustness criteria such as minimax regret. Furthermore, CoEvoRDT has potential to improve the results of other state-of-the-art methods by incorporating their outcomes (DTs they produce) into the initial population and optimize them in the process of coevolution. Inspired by the game theory, CoEvoRDT utilizes mixed Nash equilibrium to enhance convergence. The method is tested on 20 popular datasets and shows superior performance compared to 4 state-of-the-art algorithms. It outperformed all competing methods on 13 datasets with adversarial accuracy metrics, and on all 20 considered datasets with minimax regret. Strong experimental results and flexibility in choosing the error measure make CoEvoRDT a promising approach for constructing robust DTs in real-world applications.

Research paper thumbnail of Coevolutionary Algorithm for Building Robust Decision Trees under Minimax Regret

arXiv (Cornell University), Dec 13, 2023

Research paper thumbnail of Human-Level Melodic Line Harmonization

Lecture Notes in Computer Science, 2022

Research paper thumbnail of Duel-based neuroevolutionary method for Stackelberg Security Games with boundedly rational Attacker

Research paper thumbnail of Coevolution of players strategies in security games

Journal of Computational Science

Research paper thumbnail of Coevolutionary Approach to Sequential Stackelberg Security Games

Lecture Notes in Computer Science, 2022

Research paper thumbnail of Toward human-level tonal and modal melody harmonizations

Journal of Computational Science

Research paper thumbnail of Evolutionary Approach to Melodic Line Harmonization

Lecture Notes in Computer Science, 2023

Research paper thumbnail of Sequential Stackelberg Games with bounded rationality

Research paper thumbnail of Evolutionary Approach to Security Games with Signaling

Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence

Green Security Games have become a popular way to model scenarios involving the protection of nat... more Green Security Games have become a popular way to model scenarios involving the protection of natural resources, such as wildlife. Sensors (e.g. drones equipped with cameras) have also begun to play a role in these scenarios by providing real-time information. Incorporating both human and sensor defender resources strategically is the subject of recent work on Security Games with Signaling (SGS). However, current methods to solve SGS do not scale well in terms of time or memory. We therefore propose a novel approach to SGS, which, for the first time in this domain, employs an Evolutionary Computation paradigm: EASGS. EASGS effectively searches the huge SGS solution space via suitable solution encoding in a chromosome and a specially-designed set of operators. The operators include three types of mutations, each focusing on a particular aspect of the SGS solution, optimized crossover and a local coverage improvement scheme (a memetic aspect of EASGS). We also introduce a new set of b...

Research paper thumbnail of Addressing Expensive Multi-objective Games with Postponed Preference Articulation via Memetic Co-evolution

This paper presents algorithmic and empirical contributions demonstrating that the convergence ch... more This paper presents algorithmic and empirical contributions demonstrating that the convergence characteristics of a co-evolutionary approach to tackle Multi-Objective Games (MOGs) with postponed preference articulation can often be hampered due to the possible emergence of the so-called Red Queen effect. Accordingly, it is hypothesized that the convergence characteristics can be significantly improved through the incorporation of memetics (local solution refinements as a form of lifelong learning), as a promising means of mitigating (or at least suppressing) the Red Queen phenomenon by providing a guiding hand to the purely genetic mechanisms of co-evolution. Our practical motivation is to address MOGs of a time-sensitive nature that are characterized by computationally expensive evaluations, wherein there is a natural need to reduce the total number of true function evaluations consumed in achieving good quality solutions. To this end, we propose novel enhancements to co-evolutiona...

Research paper thumbnail of A Generic Metaheuristic Approach to Sequential Security Games

ArXiv, 2020

The paper introduces a generic approach to solving Sequential Security Games (SGs) which utilizes... more The paper introduces a generic approach to solving Sequential Security Games (SGs) which utilizes Evolutionary Algorithms. Formulation of the method (named EASG) is general and largely game-independent, which allows for its application to a wide range of SGs with just little adjustments addressing game specificity. Comprehensive experiments performed on 3 different types of games (with 300 instances in total) demonstrate robustness and stability of EASG, manifested by repeatable achieving optimal or near-optimal solutions in the vast majority of the cases. The main advantage of EASG is time efficiency. The method scales visibly better than state-of-the-art approaches and consequently can be applied to SG instances which are beyond capabilities of the existing methods. Furthermore, due to anytime characteristics, EASG is very well suited for time-critical applications, as the method can be terminated at any moment and still provide a reasonably good solution - the best one found so far.

Research paper thumbnail of A TCART-M — Tuned CARTesian-based error function for multilabel classification with the MLP

2017 International Joint Conference on Neural Networks (IJCNN), 2017

In 2006 Zhang and Zhou proposed a multilabel classification model based on the MLP network, which... more In 2006 Zhang and Zhou proposed a multilabel classification model based on the MLP network, which was subsequently improved by Grodzicki et al. This paper further improves both these approaches by introducing a scaling parameter responsible for maintaining a balance between the impacts of particular components of the MLP's error function in the training process. The newly-proposed parameter is autonomously fine-tuned by the system in the nested cross validation process. The proposed approach is tested on a set of well-established benchmarks and demonstrates its superiority over the baseline methods for 16 different error measures used in the experiments. Furthermore, the method proves competitive to 12 other state-of-the-art machine learning approaches which are used for further comparisons. In the combined score composed of ranking positions for all benchmarks and all error functions, the proposed neural network system gains the leading position among all tested methods.

Research paper thumbnail of Anchoring Theory in Sequential Stackelberg Games

ArXiv, 2020

An underlying assumption of Stackelberg Games (SGs) is perfect rationality of the players. Howeve... more An underlying assumption of Stackelberg Games (SGs) is perfect rationality of the players. However, in real-life situations (which are often modeled by SGs) the followers (terrorists, thieves, poachers or smugglers) -- as humans in general -- may act not in a perfectly rational way, as their decisions may be affected by biases of various kinds which bound rationality of their decisions. One of the popular models of bounded rationality (BR) is Anchoring Theory (AT) which claims that humans have a tendency to flatten probabilities of available options, i.e. they perceive a distribution of these probabilities as being closer to the uniform distribution than it really is. This paper proposes an efficient formulation of AT in sequential extensive-form SGs (named ATSG), suitable for Mixed-Integer Linear Program (MILP) solution methods. ATSG is implemented in three MILP/LP-based state-of-the-art methods for solving sequential SGs and two recently introduced non-MILP approaches: one relying...

Research paper thumbnail of DeepIQ: A Human-Inspired AI System for Solving IQ Test Problems

This paper presents a neural network approach to solving the most common type of human IQ test pr... more This paper presents a neural network approach to solving the most common type of human IQ test problems – Raven’s Progressive Matrices (RMs). The proposed DeepIQ system is composed of three modules: a deep autoencoder which is trained to learn a feature-based representation of various figure images used in IQ tests, an ensemble of shallow multilayer perceptrons applied to detection of feature differences, and a scoring module use for assessment of candidate answers. DeepIQ is able to learn the underlying principles of solving RMs (the importance of similarity of figures in shape, rotation, size or shading) in a domain-independent way, that allows its subsequent application to test instances constructed based on a different set of figures, never seen before, or another type of IQ problem, with no requirement for additional training. This transfer learning property is of paramount importance due to scarce availability of the real data, and is demonstrated in the paper on two different...

Research paper thumbnail of Travel Time Prediction for Trams in Warsaw

Advances in Intelligent Systems and Computing, 2017

The paper presents a comparison between different prediction methods for trams time travels in Wa... more The paper presents a comparison between different prediction methods for trams time travels in Warsaw. Predictions are constructed based on historical trams GPS positions. Three different prediction approaches were implemented and compared with the official timetables and real time travels. Obtained results show that the official timetables provides only approximated time travel especially in rush hours. Proposed prediction methods outperform the official schedule in the term of time travel precision and may be used as a more accurate source of travel time for passengers.

Research paper thumbnail of Dimensionality Reduction in Multilabel Classification with Neural Networks

2019 International Joint Conference on Neural Networks (IJCNN), 2019

A new neural network method for Dimensionality Reduction (DR) of the input feature space in Multi... more A new neural network method for Dimensionality Reduction (DR) of the input feature space in Multilabel Classification (MC) problems is proposed and experimentally evaluated in this paper. The method (abbreviated as TCART-MR) can be used in two possible scenarios: either as a stand-alone DR pre-processing phase, preceding subsequent application of any particular MC algorithm, or as a compact MC approach in which TCART-MR is applied twice - first to DR task and then to MC problem with reduced input space.Extensive experimental results proved statistically relevant advantage of TCART-MR over three state-of-the-art approaches in DR domain (in the context of MC), as well as its superiority over 10 state-of-the-art MC algorithms listed in a recent MC survey paper. The MC tests were performed on a set of 9 benchmark problems and 16 evaluation measures (leading to 144 experimental cases in total).

Research paper thumbnail of Travel time prediction for trams in Warsaw

The paper presents a comparison between different prediction methods for trams time travels in Wa... more The paper presents a comparison between different prediction methods for trams time travels in Warsaw. Predictions are constructed based on historical trams GPS positions. Three different prediction approaches were implemented and compared with the official timetables and real time travels. Obtained results show that the official timetables provides only approximated time travel especially in rush hours. Proposed prediction methods outperform the official schedule in the term of time travel precision and may be used as a more accurate source of travel time for passengers.

Research paper thumbnail of DeepIQ A Human-Inspired AI System for Solving IQ Test Problems

This paper presents a neural network approach to solving the most common type of human IQ test pr... more This paper presents a neural network approach to solving the most common type of human IQ test problems-Raven's Progressive Matrices (RMs). The proposed DeepIQ system is composed of three modules: a deep autoencoder which is trained to learn a feature-based representation of various figure images used in IQ tests, an ensemble of shallow multilayer perceptrons applied to detection of feature differences, and a scoring module use for assessment of candidate answers. DeepIQ is able to learn the underlying principles of solving RMs (the importance of similarity of figures in shape, rotation, size or shading) in a domain-independent way, that allows its subsequent application to test instances constructed based on a different set of figures, never seen before, or another type of IQ problem, with no requirement for additional training. This transfer learning property is of paramount importance due to scarce availability of the real data, and is demonstrated in the paper on two different RM data sets, as well as two distinct types of IQ tasks (solving RMs and odd-one-out problems). Experimental results are promising, excelling human average scores by a large margin on the most challenging subset of RM instances and exceeding 90% accuracy in odd-one-out tests.

Research paper thumbnail of Dimensionality Reduction in Multilabel Classification with Neural Networks

A new neural network method for Dimensionality Reduction (DR) of the input feature space in Multi... more A new neural network method for Dimensionality Reduction (DR) of the input feature space in Multilabel Classification (MC) problems is proposed and experimentally evaluated in this paper. The method (abbreviated as TCART-MR) can be used in two possible scenarios: either as a stand-alone DR pre-processing phase, preceding subsequent application of any particular MC algorithm, or as a compact MC approach in which TCART-MR is applied twice-first to DR task and then to MC problem with reduced input space. Extensive experimental results proved statistically relevant advantage of TCART-MR over three state-of-the-art approaches in DR domain (in the context of MC), as well as its superiority over 10 state-of-the-art MC algorithms listed in a recent MC survey paper. The MC tests were performed on a set of 9 benchmark problems and 16 evaluation measures (leading to 144 experimental cases in total).

Research paper thumbnail of Coevolutionary Algorithm for Building Robust Decision Trees under Minimax Regret

Proceedings of the ... AAAI Conference on Artificial Intelligence, Mar 24, 2024

In recent years, there has been growing interest in developing robust machine learning (ML) model... more In recent years, there has been growing interest in developing robust machine learning (ML) models that can withstand adversarial attacks, including one of the most widely adopted, efficient, and interpretable ML algorithms-decision trees (DTs). This paper proposes a novel coevolutionary algorithm (CoEvoRDT) designed to create robust DTs capable of handling noisy high-dimensional data in adversarial contexts. Motivated by the limitations of traditional DT algorithms, we leverage adaptive coevolution to allow DTs to evolve and learn from interactions with perturbed input data. CoEvoRDT alternately evolves competing populations of DTs and perturbed features, enabling construction of DTs with desired properties. CoEvoRDT is easily adaptable to various target metrics, allowing the use of tailored robustness criteria such as minimax regret. Furthermore, CoEvoRDT has potential to improve the results of other state-of-the-art methods by incorporating their outcomes (DTs they produce) into the initial population and optimize them in the process of coevolution. Inspired by the game theory, CoEvoRDT utilizes mixed Nash equilibrium to enhance convergence. The method is tested on 20 popular datasets and shows superior performance compared to 4 state-of-the-art algorithms. It outperformed all competing methods on 13 datasets with adversarial accuracy metrics, and on all 20 considered datasets with minimax regret. Strong experimental results and flexibility in choosing the error measure make CoEvoRDT a promising approach for constructing robust DTs in real-world applications.

Research paper thumbnail of Coevolutionary Algorithm for Building Robust Decision Trees under Minimax Regret

arXiv (Cornell University), Dec 13, 2023

Research paper thumbnail of Human-Level Melodic Line Harmonization

Lecture Notes in Computer Science, 2022

Research paper thumbnail of Duel-based neuroevolutionary method for Stackelberg Security Games with boundedly rational Attacker

Research paper thumbnail of Coevolution of players strategies in security games

Journal of Computational Science

Research paper thumbnail of Coevolutionary Approach to Sequential Stackelberg Security Games

Lecture Notes in Computer Science, 2022

Research paper thumbnail of Toward human-level tonal and modal melody harmonizations

Journal of Computational Science

Research paper thumbnail of Evolutionary Approach to Melodic Line Harmonization

Lecture Notes in Computer Science, 2023

Research paper thumbnail of Sequential Stackelberg Games with bounded rationality

Research paper thumbnail of Evolutionary Approach to Security Games with Signaling

Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence

Green Security Games have become a popular way to model scenarios involving the protection of nat... more Green Security Games have become a popular way to model scenarios involving the protection of natural resources, such as wildlife. Sensors (e.g. drones equipped with cameras) have also begun to play a role in these scenarios by providing real-time information. Incorporating both human and sensor defender resources strategically is the subject of recent work on Security Games with Signaling (SGS). However, current methods to solve SGS do not scale well in terms of time or memory. We therefore propose a novel approach to SGS, which, for the first time in this domain, employs an Evolutionary Computation paradigm: EASGS. EASGS effectively searches the huge SGS solution space via suitable solution encoding in a chromosome and a specially-designed set of operators. The operators include three types of mutations, each focusing on a particular aspect of the SGS solution, optimized crossover and a local coverage improvement scheme (a memetic aspect of EASGS). We also introduce a new set of b...

Research paper thumbnail of Addressing Expensive Multi-objective Games with Postponed Preference Articulation via Memetic Co-evolution

This paper presents algorithmic and empirical contributions demonstrating that the convergence ch... more This paper presents algorithmic and empirical contributions demonstrating that the convergence characteristics of a co-evolutionary approach to tackle Multi-Objective Games (MOGs) with postponed preference articulation can often be hampered due to the possible emergence of the so-called Red Queen effect. Accordingly, it is hypothesized that the convergence characteristics can be significantly improved through the incorporation of memetics (local solution refinements as a form of lifelong learning), as a promising means of mitigating (or at least suppressing) the Red Queen phenomenon by providing a guiding hand to the purely genetic mechanisms of co-evolution. Our practical motivation is to address MOGs of a time-sensitive nature that are characterized by computationally expensive evaluations, wherein there is a natural need to reduce the total number of true function evaluations consumed in achieving good quality solutions. To this end, we propose novel enhancements to co-evolutiona...

Research paper thumbnail of A Generic Metaheuristic Approach to Sequential Security Games

ArXiv, 2020

The paper introduces a generic approach to solving Sequential Security Games (SGs) which utilizes... more The paper introduces a generic approach to solving Sequential Security Games (SGs) which utilizes Evolutionary Algorithms. Formulation of the method (named EASG) is general and largely game-independent, which allows for its application to a wide range of SGs with just little adjustments addressing game specificity. Comprehensive experiments performed on 3 different types of games (with 300 instances in total) demonstrate robustness and stability of EASG, manifested by repeatable achieving optimal or near-optimal solutions in the vast majority of the cases. The main advantage of EASG is time efficiency. The method scales visibly better than state-of-the-art approaches and consequently can be applied to SG instances which are beyond capabilities of the existing methods. Furthermore, due to anytime characteristics, EASG is very well suited for time-critical applications, as the method can be terminated at any moment and still provide a reasonably good solution - the best one found so far.

Research paper thumbnail of A TCART-M — Tuned CARTesian-based error function for multilabel classification with the MLP

2017 International Joint Conference on Neural Networks (IJCNN), 2017

In 2006 Zhang and Zhou proposed a multilabel classification model based on the MLP network, which... more In 2006 Zhang and Zhou proposed a multilabel classification model based on the MLP network, which was subsequently improved by Grodzicki et al. This paper further improves both these approaches by introducing a scaling parameter responsible for maintaining a balance between the impacts of particular components of the MLP's error function in the training process. The newly-proposed parameter is autonomously fine-tuned by the system in the nested cross validation process. The proposed approach is tested on a set of well-established benchmarks and demonstrates its superiority over the baseline methods for 16 different error measures used in the experiments. Furthermore, the method proves competitive to 12 other state-of-the-art machine learning approaches which are used for further comparisons. In the combined score composed of ranking positions for all benchmarks and all error functions, the proposed neural network system gains the leading position among all tested methods.

Research paper thumbnail of Anchoring Theory in Sequential Stackelberg Games

ArXiv, 2020

An underlying assumption of Stackelberg Games (SGs) is perfect rationality of the players. Howeve... more An underlying assumption of Stackelberg Games (SGs) is perfect rationality of the players. However, in real-life situations (which are often modeled by SGs) the followers (terrorists, thieves, poachers or smugglers) -- as humans in general -- may act not in a perfectly rational way, as their decisions may be affected by biases of various kinds which bound rationality of their decisions. One of the popular models of bounded rationality (BR) is Anchoring Theory (AT) which claims that humans have a tendency to flatten probabilities of available options, i.e. they perceive a distribution of these probabilities as being closer to the uniform distribution than it really is. This paper proposes an efficient formulation of AT in sequential extensive-form SGs (named ATSG), suitable for Mixed-Integer Linear Program (MILP) solution methods. ATSG is implemented in three MILP/LP-based state-of-the-art methods for solving sequential SGs and two recently introduced non-MILP approaches: one relying...

Research paper thumbnail of DeepIQ: A Human-Inspired AI System for Solving IQ Test Problems

This paper presents a neural network approach to solving the most common type of human IQ test pr... more This paper presents a neural network approach to solving the most common type of human IQ test problems – Raven’s Progressive Matrices (RMs). The proposed DeepIQ system is composed of three modules: a deep autoencoder which is trained to learn a feature-based representation of various figure images used in IQ tests, an ensemble of shallow multilayer perceptrons applied to detection of feature differences, and a scoring module use for assessment of candidate answers. DeepIQ is able to learn the underlying principles of solving RMs (the importance of similarity of figures in shape, rotation, size or shading) in a domain-independent way, that allows its subsequent application to test instances constructed based on a different set of figures, never seen before, or another type of IQ problem, with no requirement for additional training. This transfer learning property is of paramount importance due to scarce availability of the real data, and is demonstrated in the paper on two different...

Research paper thumbnail of Travel Time Prediction for Trams in Warsaw

Advances in Intelligent Systems and Computing, 2017

The paper presents a comparison between different prediction methods for trams time travels in Wa... more The paper presents a comparison between different prediction methods for trams time travels in Warsaw. Predictions are constructed based on historical trams GPS positions. Three different prediction approaches were implemented and compared with the official timetables and real time travels. Obtained results show that the official timetables provides only approximated time travel especially in rush hours. Proposed prediction methods outperform the official schedule in the term of time travel precision and may be used as a more accurate source of travel time for passengers.

Research paper thumbnail of Dimensionality Reduction in Multilabel Classification with Neural Networks

2019 International Joint Conference on Neural Networks (IJCNN), 2019

A new neural network method for Dimensionality Reduction (DR) of the input feature space in Multi... more A new neural network method for Dimensionality Reduction (DR) of the input feature space in Multilabel Classification (MC) problems is proposed and experimentally evaluated in this paper. The method (abbreviated as TCART-MR) can be used in two possible scenarios: either as a stand-alone DR pre-processing phase, preceding subsequent application of any particular MC algorithm, or as a compact MC approach in which TCART-MR is applied twice - first to DR task and then to MC problem with reduced input space.Extensive experimental results proved statistically relevant advantage of TCART-MR over three state-of-the-art approaches in DR domain (in the context of MC), as well as its superiority over 10 state-of-the-art MC algorithms listed in a recent MC survey paper. The MC tests were performed on a set of 9 benchmark problems and 16 evaluation measures (leading to 144 experimental cases in total).

Research paper thumbnail of Travel time prediction for trams in Warsaw

The paper presents a comparison between different prediction methods for trams time travels in Wa... more The paper presents a comparison between different prediction methods for trams time travels in Warsaw. Predictions are constructed based on historical trams GPS positions. Three different prediction approaches were implemented and compared with the official timetables and real time travels. Obtained results show that the official timetables provides only approximated time travel especially in rush hours. Proposed prediction methods outperform the official schedule in the term of time travel precision and may be used as a more accurate source of travel time for passengers.

Research paper thumbnail of DeepIQ A Human-Inspired AI System for Solving IQ Test Problems

This paper presents a neural network approach to solving the most common type of human IQ test pr... more This paper presents a neural network approach to solving the most common type of human IQ test problems-Raven's Progressive Matrices (RMs). The proposed DeepIQ system is composed of three modules: a deep autoencoder which is trained to learn a feature-based representation of various figure images used in IQ tests, an ensemble of shallow multilayer perceptrons applied to detection of feature differences, and a scoring module use for assessment of candidate answers. DeepIQ is able to learn the underlying principles of solving RMs (the importance of similarity of figures in shape, rotation, size or shading) in a domain-independent way, that allows its subsequent application to test instances constructed based on a different set of figures, never seen before, or another type of IQ problem, with no requirement for additional training. This transfer learning property is of paramount importance due to scarce availability of the real data, and is demonstrated in the paper on two different RM data sets, as well as two distinct types of IQ tasks (solving RMs and odd-one-out problems). Experimental results are promising, excelling human average scores by a large margin on the most challenging subset of RM instances and exceeding 90% accuracy in odd-one-out tests.

Research paper thumbnail of Dimensionality Reduction in Multilabel Classification with Neural Networks

A new neural network method for Dimensionality Reduction (DR) of the input feature space in Multi... more A new neural network method for Dimensionality Reduction (DR) of the input feature space in Multilabel Classification (MC) problems is proposed and experimentally evaluated in this paper. The method (abbreviated as TCART-MR) can be used in two possible scenarios: either as a stand-alone DR pre-processing phase, preceding subsequent application of any particular MC algorithm, or as a compact MC approach in which TCART-MR is applied twice-first to DR task and then to MC problem with reduced input space. Extensive experimental results proved statistically relevant advantage of TCART-MR over three state-of-the-art approaches in DR domain (in the context of MC), as well as its superiority over 10 state-of-the-art MC algorithms listed in a recent MC survey paper. The MC tests were performed on a set of 9 benchmark problems and 16 evaluation measures (leading to 144 experimental cases in total).