Academic literature on the topic 'Sokoban game'

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Journal articles on the topic "Sokoban game"

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Setiani, Rosa Tri, and Bonifacius Vicky Indriyono. "Implementation of A* Algorithm for Solving Sokoban Logic Games." Journal of Applied Intelligent System 4, no. 2 (March 13, 2020): 104–11. http://dx.doi.org/10.33633/jais.v4i2.3409.

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Sokoban is one of the puzzle category games that requires players to push boxes towards the top, bottom, left and right until the specified destination. Sometimes it takes a long time to complete a very difficult level so that a solution is needed. So that these difficulties can be overcome, we need an algorithm that can help find the right path.A * algorithm is an algorithm that minimizes the total cost of the track under the right conditions that will provide the best solution in optimal time. A * algorithm will find the distance of the fastest route to be taken by a starting point to the destination object by removing unnecessary steps.From the test results, this A * algorithm can help players in finding a solution to the road in the form of completion steps in the Sokoban game by determining the smallest F value. Keywords - Artificial Intelligence, Games, Sokoban, A * algorithm
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Shi, Yi Ping. "The Development of Sokoban Game Based on Android System." Applied Mechanics and Materials 610 (August 2014): 703–6. http://dx.doi.org/10.4028/www.scientific.net/amm.610.703.

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Along with the popularity of Google Android operating system, the Android game designed for recreation, exercise thinking will become mainstream. This paper mainly introduces the development process of a 3D Sokoban game based on Android system.
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Cropper, Andrew, Richard Evans, and Mark Law. "Inductive general game playing." Machine Learning 109, no. 7 (November 18, 2019): 1393–434. http://dx.doi.org/10.1007/s10994-019-05843-w.

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AbstractGeneral game playing (GGP) is a framework for evaluating an agent’s general intelligence across a wide range of tasks. In the GGP competition, an agent is given the rules of a game (described as a logic program) that it has never seen before. The task is for the agent to play the game, thus generating game traces. The winner of the GGP competition is the agent that gets the best total score over all the games. In this paper, we invert this task: a learner is given game traces and the task is to learn the rules that could produce the traces. This problem is central to inductive general game playing (IGGP). We introduce a technique that automatically generates IGGP tasks from GGP games. We introduce an IGGP dataset which contains traces from 50 diverse games, such as Sudoku, Sokoban, and Checkers. We claim that IGGP is difficult for existing inductive logic programming (ILP) approaches. To support this claim, we evaluate existing ILP systems on our dataset. Our empirical results show that most of the games cannot be correctly learned by existing systems. The best performing system solves only 40% of the tasks perfectly. Our results suggest that IGGP poses many challenges to existing approaches. Furthermore, because we can automatically generate IGGP tasks from GGP games, our dataset will continue to grow with the GGP competition, as new games are added every year. We therefore think that the IGGP problem and dataset will be valuable for motivating and evaluating future research.
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Dziechcińska-Halamoda, Zyta, Zofia Majcher, Jerzy Michael, and Zdzisław Skupień. "A Sokoban-type game and arc deletion within irregular digraphs of all sizes." Discussiones Mathematicae Graph Theory 27, no. 3 (2007): 611. http://dx.doi.org/10.7151/dmgt.1387.

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Pujiyono, Wahyu, Mursid Wahyu Hananto, and Suheri Suheri. "MEMBANGUN APLIKASI GAME SOKOBAN BERORIENTASI OBJEK PADA PONSEL DENGAN TEKNOLOGI JAVA 2 MICRO EDITION (J2ME)." TELKOMNIKA (Telecommunication Computing Electronics and Control) 4, no. 2 (August 1, 2006): 127. http://dx.doi.org/10.12928/telkomnika.v4i2.1256.

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Chandra Prakash, V., J. K. R. Sastry, V. Kantha Rao, T. Vineetha, V. Harika, and A. Abhishek Reddy. "A critical study on applicability of sokoban game for building cognitive model of a student for career assessment." International Journal of Engineering & Technology 7, no. 1.1 (December 21, 2017): 260. http://dx.doi.org/10.14419/ijet.v7i1.1.9482.

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Career assessment is extremely important for a student to choose the best career in his/her life. A career counsellor may assess a student by considering his/her academic record and the scores obtained in IQ tests, reasoning, quantitative-aptitude, etc. and suggest a suitable career. After studies, when the student gets employment in his/her selected career, at times, the student may find it very difficult to continue in his/her career. A major reason may be that the psychology of the student may not match with the psychology that is expected for the chosen career. Hence, it is highly essential for the counsellors to assess the psychological factors of a student before suggesting a suitable career. Game playing is one method through which psychological factors of a student can be assessed. This paper presents a critical study on applicability of Sokoban game for assessing student’s psychological factors so that an Expert system can build a cognitive model of a student for career assessment.
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Pardo, Fabio, Vitaly Levdik, and Petar Kormushev. "Scaling All-Goals Updates in Reinforcement Learning Using Convolutional Neural Networks." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 5355–62. http://dx.doi.org/10.1609/aaai.v34i04.5983.

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Being able to reach any desired location in the environment can be a valuable asset for an agent. Learning a policy to navigate between all pairs of states individually is often not feasible. An all-goals updating algorithm uses each transition to learn Q-values towards all goals simultaneously and off-policy. However the expensive numerous updates in parallel limited the approach to small tabular cases so far. To tackle this problem we propose to use convolutional network architectures to generate Q-values and updates for a large number of goals at once. We demonstrate the accuracy and generalization qualities of the proposed method on randomly generated mazes and Sokoban puzzles. In the case of on-screen goal coordinates the resulting mapping from frames to distance-maps directly informs the agent about which places are reachable and in how many steps. As an example of application we show that replacing the random actions in ε-greedy exploration by several actions towards feasible goals generates better exploratory trajectories on Montezuma's Revenge and Super Mario All-Stars games.
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Dissertations / Theses on the topic "Sokoban game"

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Žlebek, Petr. "Hra Sokoban a umělá inteligence." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2021. http://www.nusl.cz/ntk/nusl-442845.

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The thesis is focused on solving the Sokoban game using artificial intelligence algorithms. The first part of the thesis describes the Sokoban game, state space and selected state space search methods. In the second part selected methods were implemented and graphic user interface was created in the Python environment. Comparative experiments were executed in the final part.
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Taylor, Joshua. "The Procedural Generation of Interesting Sokoban Levels." Thesis, University of North Texas, 2015. https://digital.library.unt.edu/ark:/67531/metadc801887/.

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As video games continue to become larger, more complex, and more costly to produce, research into methods to make game creation easier and faster becomes more valuable. One such research topic is procedural generation, which allows the computer to assist in the creation of content. This dissertation presents a new algorithm for the generation of Sokoban levels. Sokoban is a grid-based transport puzzle which is computational interesting due to being PSPACE-complete. Beyond just generating levels, the question of whether or not the levels created by this algorithm are interesting to human players is explored. A study was carried out comparing player attention while playing hand made levels versus their attention during procedurally generated levels. An auditory Stroop test was used to measure attention without disrupting play.
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Ming-Yuan, Tsai, and 蔡明原. "The Design and Analysis of Minimum Moving Algorithm for Sokoban Game." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/65246077778697709529.

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碩士
國立臺灣師範大學
資訊教育研究所
90
Sokoban game is a motion planning problem. The main challenge of this problem is to detect deadlock states and to reduce the search space with good stratagems. Previous researchers designed single-agent search methods to solve Sokoban game. Single-agnet search methods dynamically build patterns to detect deadlock, but it can not ensure that the result of moving steps is minimal. In this thesis, we use backward search method to decrease deadlock states and to make deadlock detecting easily. In our experiments, we also find out that Skiplist has better performance than AVL-tree in inserting and searching data. Our algorithm can solve 63 out of 90 XSokoban games and find out those games’ minimal moving steps successfully, This result is better than previous researches.
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Book chapters on the topic "Sokoban game"

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Tyers, Ben. "Sokoban Game Remake." In GameMaker: Studio 100 Programming Challenges, 161–62. Berkeley, CA: Apress, 2017. http://dx.doi.org/10.1007/978-1-4842-2644-5_80.

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Botea, Adi, Martin Müller, and Jonathan Schaeffer. "Using Abstraction for Planning in Sokoban." In Computers and Games, 360–75. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-40031-8_24.

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Conference papers on the topic "Sokoban game"

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Li, Zheng, Liam O'Brien, Shayne Flint, and Ramesh Sankaranarayana. "Object-oriented Sokoban solver: A serious game project for OOAD and AI education." In 2014 IEEE Frontiers in Education Conference (FIE). IEEE, 2014. http://dx.doi.org/10.1109/fie.2014.7044115.

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Luzhnica, Granit, Christoffer Ojeling, Eduardo Veas, and Viktoria Pammer. "Technical Concept and Technology Choices for Implementing a Tangible Version of the Sokoban Game." In 2016 IEEE International Symposium on Mixed and Augmented Reality (ISMAR-Adjunct). IEEE, 2016. http://dx.doi.org/10.1109/ismar-adjunct.2016.0053.

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