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Player Profiling and Quality Assessment of Dynamic Car Racing Tracks using Entertainment Quantifier
Department of Computer Science, Foundation of Advancement of Science and Technology-National University, Lahore Campus, Lahore, Pakistan.ORCID iD: 0000-0002-2123-8187
Department of Computer Science, Foundation of Advancement of Science and Technology-National University, Lahore Campus, Lahore-54000, Pakistan.ORCID iD: 0000-0001-8139-2269
2018 (English)In: Computational Intelligence, ISSN 1467-8640, Vol. 34, no 4, p. 1046-1071, article id 9Article in journal (Refereed) Published
Abstract [en]

Interactive games have been an interesting area of research and have many challenges. With the advancement in technology, games have been revolutionizing at each step as per the emerging and variant interests of players. Recently, machine learning techniques are used for the generation of game content based on players experience. The Dynamic Content Generation (DCG) in computer games based on players experience and feedback is still a challenging task. This requires measurement of entertainment factor achieved by a player during a game. In order to measure entertainment factor, we need to incorporate Human Computer Interaction (HCI) by evolution of game content with respect to players response. Optimization techniques can be used for the measurement of entertainment factor as well as for the generation of dynamic game content. The use of computational intelligence techniques in game development can lead to a new domain called Computational Intelligence in Games (CIG). This research is focused on car racing game genre and the paradigm selected for dynamicity is track generation of car racing game. It requires player profiling and classification of players. The optimization of track generation has been performed by using single and multi-objective Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). Initially, classification of players rank based on data and theory driven approaches has been performed. Moreover, three different techniques of defining ranges or boundaries of race parameters for players rank classification are studied. The techniques are based on crisp values, neural network and fuzzy inference process. Then an Entertainment Quantifier (EQ) technique is proposed for a player after playing a certain number of games based on dynamic content generation using multi-objective genetic algorithm (MOGA) using standard Pareto optimal front as well as an Epsilon ("€") front. In conclusion, the method proposed for quantifying entertainment can be used to analyze and classify the trend in interests of a player according to which the game itself can dynamically generate. This will keep the interest of player intact and provides maximum entertainment experience as per the interest of an individual. The proposed solution can easily be used in generation of any game content and can effectively be used in accurate measurement of entertaining factor of any game.

Place, publisher, year, edition, pages
United States, 2018. Vol. 34, no 4, p. 1046-1071, article id 9
Keywords [en]
Computational intelligence, dynamic content generation, data driven approach, genetic algorithm, measuring entertainment, particle swarm optimization, pareto optimal front, epsilon front
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:ltu:diva-83820DOI: 10.1111/coin.12161ISI: 000449887900004Scopus ID: 2-s2.0-85043282457OAI: oai:DiVA.org:ltu-83820DiVA, id: diva2:1545590
Available from: 2021-04-19 Created: 2021-04-19 Last updated: 2023-09-12Bibliographically approved

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Javed, Saleha

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