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Luiz ChaimowiczGeber Lisboa RamalhoRenato Antonio Celso FerreiraMirna Paula Silva2019-08-12T12:13:19Z2019-08-12T12:13:19Z2015-12-03http://hdl.handle.net/1843/ESBF-AARQWMO Ajuste Dinâmico de Dificuldade (DDA) consiste em uma alternativa para o balanceamento estático de dificuldade realizado nos jogos digitais. Esta abordagem tem se mostrado apropriada para proporcionar desafios equilibrados, evitando tédio e/ou frustração durante o jogo. Assim, o objetivo deste trabalho é apresentar um mecanismo que realize o DDA durante uma partida de um jogo MOBA. A ideia principal do mecanismo é alterar dinamicamente a Inteligência Artificial (IA) do jogo, adaptando-a as habilidades do jogador. Para isso, foram implementadas três IAs diferentes para simular o comportamento estratégico de jogadores: iniciante, intermediário e experiente no jogo Defense of the Ancient (DotA). Foram realizados experimentos comparando dois jogadores controlados por diferentes IAs e em seguida, testes com usuários foram realizados com o objetivo de avaliar qualitativamente a abordagem. O mecanismo se mostrou capaz de oferecer um adversário consistente e desafiador em maioria dos casos.Dynamic Difficulty Adjustment (DDA) consists in an alternative to the static game balancing performed in game design. DDA is done during execution, tracking the player's performance and adjusting the game to present proper challenges to the player. This approach seems appropriate to increase the player entertainment, since it provides balanced challenges, avoiding boredom and/or frustration during gameplay. Therefore, this paper presents a mechanism to perform the dynamic difficulty adjustment during a match of a MOBA game. The idea is to dynamically change the game Artificial Intelligence (AI), adapting it to the player skills in order to make the player's experience less frustrating. We implemented three different AIs to match player behaviors: beginner, regular and expert in the game Defense of the Ancient (DotA), a modification (MOD) of the game Warcraft III. We performed a series of experiments with AI versus AI and, after comparing all results, the presented mechanism was able to keep up with the simulated player's abilities on 85% of all experiments. The remaining 15% failed to suit the simulated player's need because the adjustment did not occur on the right moment. Thereafter, user tests were performed aiming to qualitatively evaluate the mechanism. We can conclude that it behaved as expected, being able to offer a consistent opponent for beginner and intermediate players. For expert players the adaptive AI presented itself not very challenging due to the lack of a more complex strategical behavior. But in contrast to this incompatibility, all users have informed to feel challenged and motivated to fulfill the goal of the game, without feeling anxious or bored during the match.Universidade Federal de Minas GeraisUFMGInteligencia artificialJogos eletrônicosJogos eletronicosInteligência artificialComputaçãoJogos DigitaisBalanceamento de DificuldadeJogos MOBAInteligência ArtificialAjuste Dinâmico de DificuldadeInteligência artificial adaptativa para ajuste dinâmico de dificuldade em jogos digitaisinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/openAccessporreponame:Repositório Institucional da UFMGinstname:Universidade Federal de Minas Gerais (UFMG)instacron:UFMGORIGINALmirnapaula.pdfapplication/pdf7903696https://repositorio.ufmg.br/bitstream/1843/ESBF-AARQWM/1/mirnapaula.pdfcf390c5617ec5a3501f7555388c0ca4dMD51TEXTmirnapaula.pdf.txtmirnapaula.pdf.txtExtracted texttext/plain142753https://repositorio.ufmg.br/bitstream/1843/ESBF-AARQWM/2/mirnapaula.pdf.txt7114836518c8a2ac2a75eee064ea5c09MD521843/ESBF-AARQWM2019-11-14 17:47:08.811oai:repositorio.ufmg.br:1843/ESBF-AARQWMRepositório InstitucionalPUBhttps://repositorio.ufmg.br/oaiopendoar:2019-11-14T20:47:08Repositório Institucional da UFMG - Universidade Federal de Minas Gerais (UFMG)false
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