PENINGKATAN KECERDASAN COMPUTER PLAYER PADA GAME PERTARUNGAN BERBASIS K-NEAREST NEIGHBOR BERBOBOT

Salah satu teknologi komputer yang berkembang dan perubahannya cukup pesat adalah game. Tujuan dibuatnya game adalah sebagai sarana hiburan dan memberikan kesenangan bagi penggunanya. Contoh elemen dalam pembuatan game yang penting adalah adanya tantangan yang seimbang sesuai level. Dalam hal ini, adanya kecerdasan buatan atau AI merupakan salah satu unsur yang diperlukan dalam pembentukan game. Penggunaan AI yang tidak beradaptasi ke strategi lawan akan  mudah diprediksi dan repetitif. Jika AI terlalu pintar maka player akan kesulitan dalam memainkan game tersebut. Dengan keadaan seperti itu akan menurunkan tingkat enjoyment dari pemain. Oleh karena itu, dibutuhkan suatu metode AI yang dapat beradaptasi dengan kemampuan dari player yang bermain. Sehingga tingkat kesulitan yang dihadapi dapat mengikuti kemampuan pemainnya dan pengalaman enjoyment ketika bermain game terus terjaga. Pada penelitian sebelumnya, metode AI yang sering digunakan pada game berjenis pertarungan adalah K-NN. Namun metode tersebut menganggap semua atribut dalam game adalah sama sehingga hal ini mempengaruhi hasil learning AI menjadi kurang optimal.Penelitian ini mengusulkan metode untuk AI dengan menggunakan metode K-NN berbobot pada game berjenis pertarungan. Dimana, pembobotan tersebut dilakukan untuk memberikan pengaruh setiap atribut dengan bobot disesuaikan dengan aksi player. Dari hasil evaluasi yang dilakukan terhadap 50 kali pertandingan pada 3 skenario uji coba, metode yang diusulkan yaitu K-NN berbobot mampu menghasilkan tingkat kecerdasan AI dengan akurasi sebesar 51%. Sedangkan, metode sebelumnya yaitu K-NN tanpa bobot hanya menghasilkan tingkat kecerdasan AI sebesar 38% dan metode random menghasilkan tingkat kecerdasan AI sebesar 25%

Comparing dynamitic difficulty adjustment and improvement in action game

A thesis submitted to the University of Bedfordshire in partial fulfilment of the requirements for the degree of Master ResearchDesigning a game difficulty is one of the key things as a game designer. Player will be feeling boring when the game designer makes the game too easy or too hard. In the past decades, most of single player games can allow players to choose the game difficulty either easy, normal or hard which define the overall game difficulty. In action game, these options are lack of flexibility and they are unsuitable to the player skill to meet the game difficulty. By using Dynamic Difficulty Adjustment (DDA), it can change the game difficulty in real time and it can match different player skills. In this paper, the final goal is the comparison of the three DDA systems in action game and apply an improved DDA. In order to apply a new improved DDA, this thesis will evaluate three chosen DDA systems with chosen action decision based AI for action game. A new DDA measurement formula is applied to the comparing section

Deduction of Fighting-Game Countermeasures Using the k-Nearest Neighbor Algorithm and a Game Simulator

This paper proposes an artificial intelligence algorithm that uses the k-nearest neighbor algorithm to predict its opponent's attack action and a game simulator to deduce a countermeasure action for controlling an in-game character in a fighting game. This AI algorithm (AI) aims at achieving good results in the fighting-game AI competition having been organized by our laboratory since 2013. It is also a sample AI, called MizunoAI, publicly available for the 2014 competition at CIG 2014. In fighting games, every action is either advantageous or disadvantageous against another. By predicting its opponent's next action, our AI can devise a countermeasure which is advantageous against that action, leading to higher scores in the game. The effectiveness of the proposed AI is confirmed by the results of matches against the top-three AI entries of the 2013 competition

Meningkatkan Kecerdasan Adaptif Computer Player Pada Game Pertarungan Berbasis K-Nearest Neighbor Berbobot

Salah satu produk dari teknologi komputer yang berkembang dan perubahannya cukup pesat adalah game. Tujuan dibuatnya game adalah sebagai sarana hiburan dan untuk memberikan kesenangan bagi penggunanya. Salah satu contoh elemen dalam pembuatan game yang penting adalah adanya tantangan yang terukur dan seimbang sesuai level. Dalam hal ini, adanya kecerdasan buatan atau AI merupakan salah satu unsur yang diperlukan dalam pembentukan game. Penggunaan AI yang statis dan tidak beradaptasi ke strategi lawan akan mudah diprediksi dan repetitif. Sebaliknya, jika AI terlalu pintar maka player akan kesulitan dalam memainkan game tersebut. Dengan keadaan seperti itu akan menurunkan tingkat enjoyment dari pemain. Oleh karena itu, dibutuhkan suatu metode adaptif AI yang dapat beradaptasi dengan kemampuan dari player yang bermain. Sehingga tingkat kesulitan yang dihadapi dapat diatur secara otomatis mengikuti kemampuan pemainnya dan pengalaman enjoyment ketika bermain game terus terjaga. Terkait dengan metode K-NN yang digunakan, metode ini sudah sering digunakan pada penelitian sebelumnya khususnya pada game berjenis pertarungan. Namun metode tersebut menganggap semua atribut dalam game adalah sama sehingga hal ini mempengaruhi learning AI menjadi kurang akurat. Penelitian ini mengusulkan metode untuk adaptif AI dengan menggunakan metode K-NN berbobot pada game berjenis pertarungan. Dimana, pembobotan tersebut dilakukan untuk memberikan pengaruh setiap atribut dengan perubahan bobot sesuai dengan aksi player. Dari hasil evaluasi yang dilakukan terhadap 50 kali pertandingan pada 3 skenario uji coba, metode yang diusulkan yaitu K-NN berbobot mampu menghasilkan tingkat adaptif AI dengan akurasi sebesar 72%. Sedangkan, metode sebelumnya yaitu K-NN hanya menghasilkan tingkat adaptif AI sebesar 38% dan metode random menghasilkan tingkat adaptif AI sebesar 25%. =================================================================================================== One of the computer technology products that develops and changes quite rapidly is game. The purpose of game creation is as an entertainment facility which gives pleasure to its users. One example of the important element in game creation is a measurable and balance challenge by level. In this case, the existence of artificial intelligence or AI is one of the elements which is needed in game formation. The static and unadaptive AI use will be easily predicted by the opponent. Moreover, the game will be repetitive. Conversely, if AI is too smart, the player will have difficulty in playing the game. Consequently, it will reduce the level of the players' enjoyment. Therefore, it needs an adaptive AI method that can adapt the capabilities of the players. So that the difficulty level can be arranged automatically by following the player's ability and enjoyment experience during the continous play. Related to the previous studies about K-NN method use, this method had been frequently used in many studies, particularly in game battle type. However, the method considers that all the attributes in the game are similar so it affects the learning of AI which can be less accurate. This study proposed a method for adaptive AI using the weighted K-NN method on game battle type. In this study, the weighting was done by giving an effect to each attribute with weight changes based on the player action. Based on the evaluation results of 50 times competition on 3 trial scenario, the proposed method, weighted K-NN was capable to result AI Adaptive level with the accuracy level about 72%. Meanwhile, the previous method of K-NN only resulted adaptive AI level about 38%, while the random method resulted an adaptive level of AI about 25%

Mimicking human player strategies in fighting games using game artificial intelligence techniques

Fighting videogames (also known as fighting games) are ever growing in popularity and accessibility. The isolated console experiences of 20th century gaming has been replaced by online gaming services that allow gamers to play from almost anywhere in the world with one another. This gives rise to competitive gaming on a global scale enabling them to experience fresh play styles and challenges by playing someone new. Fighting games can typically be played either as a single player experience, or against another human player, whether it is via a network or a traditional multiplayer experience. However, there are two issues with these approaches. First, the single player offering in many fighting games is regarded as being simplistic in design, making the moves by the computer predictable. Secondly, while playing against other human players can be more varied and challenging, this may not always be achievable due to the logistics involved in setting up such a bout. Game Artificial Intelligence could provide a solution to both of these issues, allowing a human player s strategy to be learned and then mimicked by the AI fighter. In this thesis, game AI techniques have been researched to provide a means of mimicking human player strategies in strategic fighting games with multiple parameters. Various techniques and their current usages are surveyed, informing the design of two separate solutions to this problem. The first solution relies solely on leveraging k nearest neighbour classification to identify which move should be executed based on the in-game parameters, resulting in decisions being made at the operational level and being fed from the bottom-up to the strategic level. The second solution utilises a number of existing Artificial Intelligence techniques, including data driven finite state machines, hierarchical clustering and k nearest neighbour classification, in an architecture that makes decisions at the strategic level and feeds them from the top-down to the operational level, resulting in the execution of moves. This design is underpinned by a novel algorithm to aid the mimicking process, which is used to identify patterns and strategies within data collated during bouts between two human players. Both solutions are evaluated quantitatively and qualitatively. A conclusion summarising the findings, as well as future work, is provided. The conclusions highlight the fact that both solutions are proficient in mimicking human strategies, but each has its own strengths depending on the type of strategy played out by the human. More structured, methodical strategies are better mimicked by the data driven finite state machine hybrid architecture, whereas the k nearest neighbour approach is better suited to tactical approaches, or even random button bashing that does not always conform to a pre-defined strategy