Journal of Applied Computer Science
Stały URI zbioruhttp://hdl.handle.net/11652/3824
Journal of Applied Computer Science publishes original papers concerned with theory and practice of computer science and innovative computer technology as well as their application in engineering, biomedicine, ecology, socioeconomics and education.
Przeglądaj
4 wyniki
Wyniki wyszukiwania
Pozycja A Memory Model for Emotional Decision-Making Agent in a Game(Wydawnictwo Politechniki Łódzkiej, 2018) Rogalski, Jakub; Szajerman, DominikVirtual characters are an important part of many modern computer games. This paper describes a graph-based memory system designed for artificial agents that also simulate simple emotions. The system was tested using virtual simulation environment and it showed many new and desirable AI behaviours. These behaviours include simple preferences, reactions based on bot’s opinion of a stimuli or improvement of bot’s ability to find objects to interact with.Pozycja The Use of Heuristic Algorithms: A Case Study of a Card Game(Wydawnictwo Politechniki Łódzkiej, 2018) Lichy, Krzysztof; Mazur, Marcin; Stolarek, Jan; Lipiński, PiotrIn this paper we introduce the results of an experiment consisting in the creation of artificial intelligence using the heuristic algorithm Monte Carlo Tree Search and evaluation of its effectiveness in the card game Thousand.Pozycja Affective Pathfinding in Video Games(Wydawnictwo Politechniki Łódzkiej, 2018) Daszuta, Marcin; Wróbel, Filip; Rynkiewicz, Filip; Szajerman, Dominik; Napieralski, PiotrTo allow player submerge in created environment of a video game, agents called Non-Player Characters (NPCs) should act believably. One of the most vital aspect, in case of NPCs is pathfinding. There are a few methods that allow change path finding algorithms to become more human-like. Yet, those are not considering many vital aspects of human decisions regarding path choosing. The main purpose of this paper is to present known approaches and show example of a new approach that wider considers psychological aspects of decision making in case of choosing a path.Pozycja Geometric Transformations Embedded into Convolutional Neural Networks(Wydawnictwo Politechniki Łódzkiej, 2016) Tarasiuk, Paweł; Pryczek, MichałThis paper presents a novel extension to convolutional neural networks. While CNNs are known for invariance to object translation, changes to the other parameters could make the image recognition tasks diffcult – that includes rotations and scaling. Some improvement in this area could be achieved with embedded geometric transformations used inside the CNNs. In order to provide a practical solution, which allows fast propagation and learning of the modified networks, “fast geometric transformations” are introduced.