Simulating human-like behaviour in games using intelligent agents

The field of artificial intelligence has come a long way in the past 50 years, and studies of its methods soon expanded to a field in which they are of great practical value -- computer games. The concept of intelligent agents provides a much needed theoretical background for the comparison of various different approaches to intelligent, rational behaviour of computer-controlled characters in games. By combining rationality with certain limitations of the capabilities of our agents, we can achieve very human-like behaviour. In this diploma thesis we introduced and compared various types of agents that are used in games (but not only in games) and showed how to implement meaningful, reasonable limitations to agent capabilities into the game world. The aim of this thesis is to show the strengths and weaknesses of each type of agent and decide what types of tasks it is suitable for. We showed that even the simplest agents can succeed in their tasks in certain task environments, while more difficult task environments often require a more advanced agent architecture. The addition of goals into the agent architecture had the biggest impact on the agent's behaviour, while the finite-state machine approach kept our implementation simple and compact

FighterZero: a self-playing deep reinforcement learning agent for fighting game AI

Področje globokega učenja je v zadnjem desetletju doživelo precejšen razcvet. Uporablja se za reševanje premnogih problemov, v zadnjih petih letih pa precej tudi za igranje iger. Dva pomembna dosežka sta bila globoke Q-mreže (DQN) in AlphaZero. DQN se je naučila igrati klasične igre za Atari 2600 (Pong, Space Invaders, itd.), AlphaZero pa se je s samo-igranjem naučil igrati šah, šogi in Go. Mi smo na temelju AlphaZero poskusili zgraditi agenta FighterZero, ki bi se prav tako s samo-igranjem naučil igrati pretepaške računalniške igre. Rezultati so bili manj uspešni, kot smo pričakovali, saj se je časovna zahtevnost izkazala za nepremagljivo oviro.Deep learning has been a field of great academic interest and substantial breakthroughs over the last decade. Its applications are many and over the last five years it has spread also to the field of game playing, owing largely to two chief accomplishments of Google\u27s DeepMind team: Deep Q-Networks (DQN), which learned to play classic Atari 2600 games, and AlphaZero, which learned, strictly through self-play, to play the board games chess, shogi and Go. In this thesis we attempted to build on the success of AlphaZero by adapting its self-playing architecture to fighting games, a popular genre of video games. The results were, however, less successful than we had expected and hoped, as the time constraints proved to be an insurmountable obstacle

Simulating human-like behaviour in games using intelligent agents

The field of artificial intelligence has come a long way in the past 50 years, and studies of its methods soon expanded to a field in which they are of great practical value -- computer games. The concept of intelligent agents provides a much needed theoretical background for the comparison of various different approaches to intelligent, rational behaviour of computer-controlled characters in games. By combining rationality with certain limitations of the capabilities of our agents, we can achieve very human-like behaviour. In this diploma thesis we introduced and compared various types of agents that are used in games (but not only in games) and showed how to implement meaningful, reasonable limitations to agent capabilities into the game world. The aim of this thesis is to show the strengths and weaknesses of each type of agent and decide what types of tasks it is suitable for. We showed that even the simplest agents can succeed in their tasks in certain task environments, while more difficult task environments often require a more advanced agent architecture. The addition of goals into the agent architecture had the biggest impact on the agent's behaviour, while the finite-state machine approach kept our implementation simple and compact

Lahki globoki modeli za prepoznavo beločnice

Sclera recognition is a subfield within biometric recognition technology that focuses on identifying individuals based on the vascular structures in the sclera, i.e. the white part of the eye. Most existing solutions for sclera recognition are based either on hand-crafted methods from the field of computer vision, which perform suboptimally, or on deep convolutional networks, which require powerful hardware to run efficiently. However, biometric systems are increasingly being deployed on smartphones, head-mounted displays, and edge devices, which require light-weight models, i.e. simple computational models capable of running well on weaker hardware. As such, in our thesis (i) we propose the novel method IPAD, which decreases the number of parameters and operations in a deep network, and using IPAD we develop a light-weight model for sclera segmentation, and (ii) we develop the light-weight GazeNet network, based on the SqueezeNet architecture and trained via multi-task learning, which we use as our sclera vessel feature extractor. The results of our extensive experimental analysis affirm the superiority of deep convolutional networks over classical hand-crafted methods. On the other hand, our analysis of the models developed with the IPAD method demonstrates that the networks commonly relied on in the literature can be significantly reduced in terms of their spatial and computational requirements, without a significant decrease in accuracy -- in fact, in certain cases, simplifying the models even enhances their accuracy. Even light-weight deep networks require a significant amount of training data to achieve high-quality performance. We note that, while iris datasets are plentiful, there is a considerable lack of sclera-focused datasets. Thus, as part of the aforementioned contributions, we introduce MOBIUS, the first publicly available mobile-camera-acquired dataset intended primarily for sclera segmentation, although it can be used for iris and periocular biometrics as well. Finally, since biometric systems have been shown to exhibit bias in various biometric fields, we (iii) propose a novel methodology for bias evaluation based on two novel metrics: FSD and CGD. Using the proposed methodology, we study the bias of contemporary sclera segmentation solutions and show that even in sclera biometrics a certain amount of demographic bias is present in existing solutions.Razpoznavanje beločnice je področje biometrije, ki se ukvarja s prepoznavo identitete na podlagi žilnih struktur v beločnici, torej v belem delu očesa. Večina pristopov za razpoznavanje beločnice se zanaša ali na klasične ročno zasnovane metode računalniškega vida, ki dosegajo slabše rezultate, ali pa na globoke konvolucijske mreže, ki za dobro delovanje potrebujejo močno strojno opremo. Biometrični sistemi pa se vse bolj nameščajo na pametne telefone, naglavne zaslone in podobne naprave, za katere potrebujemo lahke modele, torej preproste računske modele, ki jih je moč poganjati na manj zmogljivi strojni opremi. V doktorskem delu zato: (i) razvijemo metodo IPAD, s katero zmanjšamo število parametrov in operacij v globokih modelih in z njo razvijemo lahki model za segmentacijo beločnice in (ii) razvijemo lahki model GazeNet, ki temelji na arhitekturi SqueezeNet in je učen s hkratnim učenjem, uporabimo pa ga za luščenje značilk iz žilnih struktur beločnice. Na podlagi obsežnega eksperimentalnega ovrednotenja v delu najprej potrdimo, da globoke nevronske mreže dosegajo občutno boljše rezultate na vseh relevantnih nalogah. Po drugi strani pa z analizo metode IPAD pokažemo, da lahko mreže iz literature znatno zmanjšamo ter kljub temu ohranimo visok nivo natančnosti -- delovanje modela se v nekaterih primerih celo izboljša, ko ga tako poenostavimo. Lahki globoki modeli pa za dobro delovanje še vedno potrebujejo veliko količino učnih podatkov, podatkovnih množic namenjenih biometriji beločnice pa primanjkuje. Zato kot del teh doprinosov sestavimo tudi MOBIUS, ki je prva javno dostopna podatkovna množica primarno namenjena biometriji beločnice s slikami zajetimi s fotoaparatom pametnih telefonov, hkrati pa jo je mogoče uporabiti tudi za raziskave na področju šarenice in periokularne regije. Ker pa so se biometrični sistemi na različnih področjih izkazali za pristranske, v doktorskem delu (iii) predlagamo novo evalvacijsko metodologijo za ocenjevanje pristranskosti biometričnih sistemov na podlagi dveh novih mer: FSD in CGD. V skladu s predlagano metodologijo izvedemo študijo pristranskosti sodobnih pristopov za segmentacijo beločnice in pokažemo, da se tudi na področju beločnice pojavi pristransko delovanje glede na določene demografske faktorje

Exploring bias in sclera segmentation models

Bias and fairness of biometric algorithms have been key topics of research in recent years, mainly due to the societal, legal and ethical implications of potentially unfair decisions made by automated decision-making models. A considerable amount of work has been done on this topic across different biometric modalities, aiming at better understanding the main sources of algorithmic bias or devising mitigation measures. In this work, we contribute to these efforts and present the first study investigating bias and fairness of sclera segmentation models. Although sclera segmentation techniques represent a key component of sclera-based biometric systems with a considerable impact on the overall recognition performance, the presence of different types of biases in sclera segmentation methods is still underexplored. To address this limitation, we describe the results of a group evaluation effort (involving seven research groups), organized to explore the performance of recent sclera segmentation models within a common experimental framework and study performance differences (and bias), originating from various demographic as well as environmental factors. Using five diverse datasets, we analyze seven independently developed sclera segmentation models in different experimental configurations. The results of our experiments suggest that there are significant differences in the overall segmentation performance across the seven models and that among the considered factors, ethnicity appears to be the biggest cause of bias. Additionally, we observe that training with representative and balanced data does not necessarily lead to less biased results. Finally, we find that in general there appears to be a negative correlation between the amount of bias observed (due to eye color, ethnicity and acquisition device) and the overall segmentation performance, suggesting that advances in the field of semantic segmentation may also help with mitigating bias

IPAD

The sclera has recently been gaining attention as a biometric modality due to its various desirable characteristics. A key step in any type of ocular biometric recognition, including sclera recognition, is the segmentation of the relevant part(s) of the eye. However, the high computational complexity of the (deep) segmentation models used in this task can limit their applicability on resource-constrained devices such as smartphones or head-mounted displays. As these devices are a common desired target for such biometric systems, lightweight solutions for ocular segmentation are critically needed. To address this issue, this paper introduces IPAD (Iterative Pruning with Activation Deviation), a novel method for developing lightweight convolutional networks, that is based on model pruning. IPAD uses a novel filter-activation-based criterion (ADC) to determine low-importance filters and employs an iterative model pruning procedure to derive the final lightweight model. To evaluate the proposed pruning procedure, we conduct extensive experiments with two diverse segmentation models, over four publicly available datasets (SBVPI, SLD, SMD and MOBIUS), in four distinct problem configurations and in comparison to state-of-the-art methods from the literature. The results of the experiments show that the proposed filter-importance criterion outperforms the standard L$^1$ and L$^2$ approaches from the literature. Furthermore, the results also suggest that: (i) the pruned models are able to retain (or even improve on) the performance of the unpruned originals, as long as they are not over-pruned, with RITnet and U-Net at 50% of their original FLOPs reaching up to 4% and 7% higher IoU values than their unpruned versions, respectively, (ii) smaller models require more careful pruning, as the pruning process can hurt the model’s generalization capabilities, and (iii) the novel criterion most convincingly outperforms the classic approaches when sufficient training data is available, implying that the abundance of data leads to more robust activation-based importance computation

NIR Iris Challenge Evaluation in Non-cooperative Environments: Segmentation and Localization

For iris recognition in non-cooperative environments, iris segmentation has been regarded as the first most important challenge still open to the biometric community, affecting all downstream tasks from normalization to recognition. In recent years, deep learning technologies have gained significant popularity among various computer vision tasks and also been introduced in iris biometrics, especially iris segmentation. To investigate recent developments and attract more interest of researchers in the iris segmentation method, we organized the 2021 NIR Iris Challenge Evaluation in Non-cooperative Environments: Segmentation and Localization (NIR-ISL 2021) at the 2021 International Joint Conference on Biometrics (IJCB 2021). The challenge was used as a public platform to assess the performance of iris segmentation and localization methods on Asian and African NIR iris images captured in non-cooperative environments. The three best-performing entries achieved solid and satisfactory iris segmentation and localization results in most cases, and their code and models have been made publicly available for reproducibility research