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Bayesian models of eye movements
Lux, Erik ; Děchtěrenko, Filip (advisor) ; Toth, Peter Gabriel (referee)
Attention allows us to monitor objects or regions of visual space and extract information from them to use for report or storage. Classical theories of attention assumed a single focus of selection but many everyday activities, such as playing video games, suggest otherwise. Nonetheless, the underlying mechanism which can explain the ability to divide attention has not been well established. Numerous attempts have been made in order to clarify divided attention, including analytical strategies as well as methods working with visual phenomena, even more sophisticated predictors incorporating information about past selection decisions. Virtually all the attempts approach this problem by constructing a simplified model of attention. In this study, we develop a version of the existing Bayesian framework to propose such models, and evaluate their ability to generate eye movement trajectories. For the comparison of models, we use the eye movement trajectories generated by several analytical strategies. We measure the...
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Metrics for eye movements comparisons
Kocián, Matěj ; Děchtěrenko, Filip (advisor) ; Vodrážka, Jindřich (referee)
Measurement of eye movements is becoming a well established part of expe- rimental research in many areas (such as human-computer interaction, cognitive psychology and others). Then usually a need arises to mutually compare the eye movements. Many different metrics have been suggested for this purpose, but what is missing is a comparison of these metrics and consequently an agreement on the ones that should be used in specific cases. In this thesis we describe some commonly used metrics and then create a model of smooth pursuit eye move- ments. We subsequently use this model to compare the ability of Levenshtein metric, Normalized Scanpath Saliency for dynamic scenes and discrete Fréchet distance to recognise similarity between the original eye movement trajectory and its modified copy. 1
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Bayesian models of eye movements
Lux, Erik ; Děchtěrenko, Filip (advisor) ; Toth, Peter Gabriel (referee)
Attention allows us to monitor objects or regions of visual space and extract information from them to use for report or storage. Classical theories of attention assumed a single focus of selection but many everyday activities, such as playing video games, suggest otherwise. Nonetheless, the underlying mechanism which can explain the ability to divide attention has not been well established. Numerous attempts have been made in order to clarify divided attention, including analytical strategies as well as methods working with visual phenomena, even more sophisticated predictors incorporating information about past selection decisions. Virtually all the attempts approach this problem by constructing a simplified model of attention. In this study, we develop a version of the existing Bayesian framework to propose such models, and evaluate their ability to generate eye movement trajectories. For the comparison of models, we use the eye movement trajectories generated by several analytical strategies. We measure the similarity between...
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Artificial intelligence for the game Desktop dungeons
Černý, Vojtěch ; Děchtěrenko, Filip (advisor) ; Pilát, Martin (referee)
Rogue-like games are a subgenre of computer RPG games, featuring procedurally generated environment and permanent death. Winning them is a challenge for a human player, and more so for artificial intelligence (AI). In this work, we present a framework for implementing artificial players for a rogue-like game Desktop Dungeons. We then investigate options of suitable AI creation, and settle for using a genetic algorithm to fine-tune a greedy strategy. The resulting AI was as succesful as a mediocre human player, winning the game 72% of the time. This framework and results may be used to improve the quality of rogue-like games, procedural content generators, and artificial intelligence in similiar environments. Powered by TCPDF (www.tcpdf.org)
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Advanced use of ACT-R in Pogamut
Zemčák, Lukáš ; Kadlec, Rudolf (advisor) ; Děchtěrenko, Filip (referee)
The requirements for virtual agents are more and more demanding. In order to manage the complex behavior of the agent, it's possible to take advantage of cognitive architectures which arised on the field neuroscience and artificial intelligence. This work examines PoJACTR library which links Pogamut library for developing intelligent agents in Unreal Tournament 2004 and jACT-R library which is Java implementation of one of the leading cognitive architectures ACTR. This work also studies certain agent implementation problems in PoJACTR and proposes a solution for them in form of debugging tools, which were subsequently implemented on an Eclipse IDE platform. In addition, it expands PoJACTR navigation and communication library modules for the game - Capture The Flag. As a validation, two agents (bots) were developed to play game, one in standard Pogamut and one in PoJACTR. When matched against each other in battle, Po- JACTR bot had comparable performance to a Pogamut bot. The results showed that debugging tools facilitated development process of PoJACTR agents.
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Disruption of movement or cohesion of groups through individuals
Vejmola, Jiří ; Neruda, Roman (advisor) ; Děchtěrenko, Filip (referee)
Title: Disruption of movement or cohesion of groups through individuals Author: Jiří Vejmola Department: Department of Theoretical Computer Science and Mathematical Logic Supervisor of the master thesis: Mgr. Roman Neruda, CSc., Institute of Computer Science of the ASCR, v. v. i. Abstract: Just a few of informed and like-minded individuals, guides, are needed to lead otherwise naive group. We look at some of the possible changes that can be caused by the presence of another informed individual with different intentions, an intruder. It is implied that he cannot cause anything significant under normal circumstances. To counter that and to increase his chances of success we intruduce a new parameter - credibility. We explore how it changes the overall behaviour. We show that by applying it to the intruder his influence over others increases. This in turn makes naive individuals more willing to follow him. We show that if the right conditions are met he can eventually become the one who leads the group. Keywords: multi-agent system, swarm intelligence, emergence, credibility
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Content-based Image Search
Talaš, Josef ; Surynek, Pavel (advisor) ; Děchtěrenko, Filip (referee)
This work aims at content-based image search. Different approaches to this type of search are investigated. The main focus of the thesis is special category of content-based image search called sketch-based image search. The most important descriptor types used for image feature extraction in image search are analyzed. Main contribution of the thesis to this research area is a new feature extraction method based on sketch-based image search. This method is implemented together with search interface. The method was evaluated by three test persons. The testing results show promising properties of new method and suggest further possible improve-ments. Powered by TCPDF (www.tcpdf.org)
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Modelling eye movements during Multiple Object Tracking
Děchtěrenko, Filip ; Lukavský, Jiří (advisor) ; Toth, Peter Gabriel (referee)
In everyday situations people have to track several objects at once (e.g. driving or collective sports). Multiple object tracking paradigm (MOT) plausibly simulate tracking several targets in laboratory conditions. When we track targets in tasks with many other objects in scene, it becomes difficult to discriminate objects in periphery (crowding). Although tracking could be done only using attention, it is interesting question how humans plan their eye movements during tracking. In our study, we conducted a MOT experiment in which we presented participants repeatedly several trials with varied number of distractors, we recorded eye movements and we measured consistency of eye movements using Normalized scanpath saliency (NSS) metric. We created several analytical strategies employing crowding avoidance and compared them with eye data. Beside analytical models, we trained neural networks to predict eye movements in MOT trial. The performance of the proposed models and neuron networks was evaluated in a new MOT experiment. The analytical models explained variability of eye movements well (results comparable to intraindividual noise in the data); predictions based on neural networks were less successful.
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Artificial Intelligence for the Bang! Game
Daniláková, Monika ; Pilát, Martin (advisor) ; Děchtěrenko, Filip (referee)
This work explores artificial intelligence (AI) algorithms for the game Bang!, a Wild West-themed card game created by Italian game designer Emiliano Sciarra. The aim of this work was to design three different AIs for this game and to compare them theoretically and experimentally. First, we analyzed game Bang! with regards to game theory, and researched some of the AI algorithms used in similar games. We then designed three different AIs algorithms and compared their advantages and disadvantages. These three AIs included an AI based on the Monte Carlo Tree Search algorithm, a genetic AI and a hybrid AI using elements of both previous AIs. We also implemented the game itself and the AIs in C#. The implementation makes it easy to add more AIs or client applications in the future, and also to compare and train the AIs. Finally, we experimentally compared the implemented AIs. The genetic AI performed the best, while Monte Carlo Tree Search AI and the hybrid AI were less suitable for this game. Powered by TCPDF (www.tcpdf.org)
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