ANALISIS PERBANDINGAN KECERDASAN BUATAN PADA COMPUTER PLAYER DALAM MENGAMBIL KEPUTUSAN PADA GAME BATTLE RPG

Pemanfaatan kecerdasan buatan telah diimplementasikan kedalam banyak hal, salah satunya adalah game. Secara umum tujuan dibuatnya game adalah untuk membuat pengguna menjadi terhibur dan merasakan kesenangan ketika sedang atau telah bermain. Kecerdasan buatan di dalam game dibutuhkan untuk meningkatkan tantangan di dalam game dan membuat game menjadi lebih dinamis dan terarah. Sehingga akan menciptakan kesenangan bagi pengguna pada saat dan setelah memainkan game. Beberapa penerapan kecerdasan buatan di dalam game diantaranya adalah dengan menggunakan metode Support Vector Machine (SVM). Dalam beberapa kasus game ada juga yang menggunakan metode Decision tree yang akan mengatur perilaku computer player di dalam permainan. Metode yang lebih sederhana untuk mengatur perilaku computer player yaitu Rulebase. Pada penelitian ini akan dilakukan perbandingan kecerdasan buatan untuk mengatur perilaku computer player di dalam game Role-Playing Game (RPG). Yang dimaksud computer player pada penelitian ini adalah pemain atau karakter yang dijalankan oleh sistem di dalam game.Tujuan dilakukannya perbandingan tersebut adalah untuk mengetahui metode kecerdasan buatan manakah yang paling baik diterapkan pada game berjenis battle RPG. Metode yang digunakan untuk menguji kecerdasan buatan yang diterapkan pada game battle RPG ini adalah dengan menggunakan skenario pertandingan.Berdasarkan analisis yang telah dilakukan didapatkan hasil bahwa kecerdasan buatan dengan menggunakan metode SVM memiliki keunggulan dalam faktor jumlah kemenangan. Hal ini dibuktikan dengan persentase kemenangan metode SVM sebesar 72.5%, Decision tree sebesar 50% dan Rulebase sebesar 22.5%. Berdasarkan data tersebut dapat disimpulkan bahwa pada penelitian ini metode SVM adalah metode pengambilan keputusan yang paling baik dibandingkan dengan metode decision tree dan Rulebase

ASPIRE Adaptive strategy prediction in a RTS environment

When playing a Real Time Strategy(RTS) game against the non-human player(bot) it is important that the bot can do different strategies to create a challenging experience over time. In this thesis we aim to improve the way the bot can predict what strategies the player is doing by analyzing the replays of the given players games. This way the bot can change its strategy based upon the known knowledge of the game state and what strategies the player have used before. We constructed a Bayesian Network to handle the predictions of the opponent's strategy and inserted that into a preexisting bot. Based on the results from our experiments we can state that the Bayesian Network adapted to the strategies our bot was exposed to. In addition we can see that the Bayesian Network only predicted the possible strategies given the obtained information about the game state.INFO390MASV-INF

Analisis Perbandingan Kecerdasan Buatan pada Computer Player dalam Mengambil Keputusan pada Game Battle RPG

Pemanfaatan kecerdasan buatan telah diimplementasikan kedalam banyak hal, salah satunya adalah game. Secara umum tujuan dibuatnya game adalah untuk membuat pengguna menjadi terhibur dan merasakan kesenangan ketika sedang atau telah bermain. Kecerdasan buatan di dalam game dibutuhkan untuk meningkatkan tantangan di dalam game dan membuat game menjadi lebih dinamis dan terarah. Sehingga akan menciptakan kesenangan bagi pengguna pada saat dan setelah memainkan game. Beberapa penerapan kecerdasan buatan di dalam game diantaranya adalah dengan menggunakan metode Support Vector Machine (SVM). Dalam beberapa kasus game ada juga yang menggunakan metode Decision Tree yang akan mengatur perilaku computer player di dalam permainan. Metode yang lebih sederhana untuk mengatur perilaku computer player yaitu Rulebase. Pada penelitian ini akan dilakukan perbandingan kecerdasan buatan untuk mengatur perilaku computer player di dalam game Role-Playing Game (RPG). Yang dimaksud computer player pada penelitian ini adalah pemain atau karakter yang dijalankan oleh sistem di dalam game. Tujuan dilakukannya perbandingan tersebut adalah untuk mengetahui metode kecerdasan buatan manakah yang paling baik diterapkan pada game berjenis battle RPG. Metode yang digunakan untuk menguji kecerdasan buatan yang diterapkan pada game battle RPG ini adalah dengan menggunakan skenario pertandingan. Berdasarkan analisis yang telah dilakukan didapatkan hasil bahwa kecerdasan buatan dengan menggunakan metode SVM memiliki keunggulan dalam faktor jumlah kemenangan. Hal ini dibuktikan dengan persentase kemenangan metode SVM sebesar 72.5%, Decision Tree sebesar 50% dan Rulebase sebesar 25%. Berdasarkan data tersebut dapat disimpulkan bahwa pada penelitian ini metode SVM adalah metode pengambilan keputusan yang paling baik dibandingkan dengan metode decision tree dan rulebase. ========================================================================== Artificial intelligence utilizing has been implemented into many things, one of them is a game. Generally, the purpose of games is to entertain users and make them feel excited while playing or even after it. Artificial intelligence in the game is needed to increase the challenge within the game and make it more dynamic and focused. Hence, it will create enjoyment for users at the time and after playing the game. SVM, decision tree and rulebase are some of the artificial intelligences method that have been commonly used. Comparison of artificial intelligence will be implemented in this study in order to regulate the behavior of computer player in the Role-Playing Game (RPG). The computer player in this study is a player or character that is run by the system in the game. The purpose of this study is to know which artificial intelligence technique is the best to be applied to battle RPG type games. Moreover, the method used to examine artificial intelligence which is applied to this RPG battle game is by utilizing a match scenario. Regarding the analysis that has been done, the result shows that artificial intelligence using SVM method has an advantage in the number of winning factor. This is evidenced by the percentage of winning methods SVM, Decision tree and Rulebase, at about 72.5%, 50% and 25% respectively. Therefore, based on these data it can be concluded that in this study SVM method is the best decision-making method compared with decision tree and Rulebase method

D-FRI-Honeypot:A Secure Sting Operation for Hacking the Hackers Using Dynamic Fuzzy Rule Interpolation

As active network defence systems, honeypots are commonly used as a decoy to inspect attackers and their attack tactics in order to improve the cybersecurity infrastructure of an organisation. A honeypot may be successful provided that it disguises its identity. However, cyberattackers continuously endeavour to discover honeypots for evading any deception and bolstering their attacks. Active fingerprinting attack is one such technique that may be used to discover honeypots by sending specially designed traffic. Preventing a fingerprinting attack is possible but doing that may hinder the process of dealing with the attackers, counteracting the purpose of a honeypot. Instead, detecting an attempted fingerprinting attack in real-time can enhance a honeypot’s capability, uninterruptedly managing any immediate consequences and preventing the honeypot being identified. Nevertheless, it is difficult to detect and predict an attempted fingerprinting attack due to the challenge of isolating it from other similar attacks, particularly when imprecise observations are involved in the monitoring of the traffic. Dynamic fuzzy rule interpolation (D-FRI) enables an adaptive approach for effective reasoning with such situations by exploiting the best of both inference and interpolation. The dynamic rules produced by D-FRI facilitate approximate reasoning with perpetual changes that often occur in this type of application, where dynamic rules are required to cover new network conditions. This paper proposes a D-FRI-Honeypot, an enhanced honeypot running D-FRI framework in conjunction with Principal Component Analysis, to detect and predict an attempted fingerprinting attack on honeypots. This D-FRI-Honeypot works with a sparse rule base but is able to detect active fingerprinting attacks when it does not find any matching rules. Also, it learns from current network conditions and offers a dynamically enriched rule base to support more precise detection. This D-FRI-Honeypot is tested against five popular fingerprinting tools (namely, Nmap, Xprobe2, NetScanTools Pro, SinFP3 and Nessus), to demonstrate its successful applications

Effective Team Strategies using Dynamic Scripting

Forming effective team strategies using heterogeneous agents to accomplish a task can be a challenging problem. The number of combinations of actions to look through can be enormous, and having an agent that is really good at a particular sub-task is no guarantee that agent will perform well on a team with members with different abilities. Dynamic Scripting has been shown to be an effective way of improving behaviours with adaptive game AI. We present an approach that modifies the scripting process to account for the other agents in a game. By analyzing an agent\u27s allies and opponents we can create better starting scripts for the agents to use. Creating better starting points for the Dynamic Scripting process and will minimize the number of iterations needed to learn effective strategies, creating a better overall gaming experience

Using machine learning techniques to create AI controlled players for video games

This study aims to achieve higher replay and entertainment value in a game through human-like AI behaviour in computer controlled characters called bats. In order to achieve that, an artificial intelligence system capable of learning from observation of human player play was developed. The artificial intelligence system makes use of machine learning capabilities to control the state change mechanism of the bot. The implemented system was tested by an audience of gamers and compared against bats controlled by static scripts. The data collected was focused on qualitative aspects of replay and entertainment value of the game and subjected to quantitative analysi

USING COEVOLUTION IN COMPLEX DOMAINS

Genetic Algorithms is a computational model inspired by Darwin's theory of evolution. It has a broad range of applications from function optimization to solving robotic control problems. Coevolution is an extension of Genetic Algorithms in which more than one population is evolved at the same time. Coevolution can be done in two ways: cooperatively, in which populations jointly try to solve an evolutionary problem, or competitively. Coevolution has been shown to be useful in solving many problems, yet its application in complex domains still needs to be demonstrated.Robotic soccer is a complex domain that has a dynamic and noisy environment. Many Reinforcement Learning techniques have been applied to the robotic soccer domain, since it is a great test bed for many machine learning methods. However, the success of Reinforcement Learning methods has been limited due to the huge state space of the domain. Evolutionary Algorithms have also been used to tackle this domain; nevertheless, their application has been limited to a small subset of the domain, and no attempt has been shown to be successful in acting on solving the whole problem.This thesis will try to answer the question of whether coevolution can be applied successfully to complex domains. Three techniques are introduced to tackle the robotic soccer problem. First, an incremental learning algorithm is used to achieve a desirable performance of some soccer tasks. Second, a hierarchical coevolution paradigm is introduced to allow coevolution to scale up in solving the problem. Third, an orchestration mechanism is utilized to manage the learning processes

Adaptation strategies for self-organising electronic institutions

For large-scale systems and networks embedded in highly dynamic, volatile, and unpredictable environments, self-adaptive and self-organising (SASO) algorithms have been proposed as solutions to the problems introduced by this dynamism, volatility, and unpredictability. In open systems it cannot be guaranteed that an adaptive mechanism that works well in isolation will work well — or at all — in combination with others. In complexity science the emergence of systemic, or macro-level, properties from individual, or micro-level, interactions is addressed through mathematical modelling and simulation. Intermediate meso-level structuration has been proposed as a method for controlling the macro-level system outcomes, through the study of how the application of certain policies, or norms, can affect adaptation and organisation at various levels of the system. In this context, this thesis describes the specification and implementation of an adaptive affective anticipatory agent model for the individual micro level, and a self-organising distributed institutional consensus algorithm for the group meso level. Situated in an intelligent transportation system, the agent model represents an adaptive decision-making system for safe driving, and the consensus algorithm allows the vehicles to self-organise agreement on values necessary for the maintenance of “platoons” of vehicles travelling down a motorway. Experiments were performed using each mechanism in isolation to demonstrate its effectiveness. A computational testbed has been built on a multi-agent simulator to examine the interaction between the two given adaptation mechanisms. Experiments involving various differing combinations of the mechanisms are performed, and the effect of these combinations on the macro-level system properties is measured. Both beneficial and pernicious interactions are observed; the experimental results are analysed in an attempt to understand these interactions. The analysis is performed through a formalism which enables the causes for the various interactions to be understood. The formalism takes into account the methods by which the SASO mechanisms are composed, at what level of the system they operate, on which parts of the system they operate, and how they interact with the population of the system. It is suggested that this formalism could serve as the starting point for an analytic method and experimental tools for a future systems theory of adaptation.Open Acces