GAME TURN-BASED ROLE PLAYING GAME (TURN-BASED RPG) “GRAND LINE” DENGAN UNITY GAME ENGINE BERBASIS ANDROID MENGGUNAKAN METODE HIERARCHIAL DYNAMIC SCRIPTING

Penelitian ini bertujuan untuk membuat sebuah game ber-genre turn-based rpg menggunakan unity game engine dan dapat dimainkan di smartphone dengan system operasi android. Pada game ini juga bertujuan untuk mengenalakan legenda yang ada di Indonesia. Metode Hierarchial Dynamic Scripting (HDS) merupakan metode pengembangan dari Dyanamic Scripting dengan mengadaptasi arsitektur Hierarchial Task Network untuk membentuk sebuah tree. Oleh karena itu, tahapan pada modul HDS memiliki kesamaan dengan tahapan dalam metode Dyanamic Scripting. Berdasarkan pengujian fungsional, kontrol pemain AI pada NPC dapat bekerja dengan baik dalam memproses kondisi yang diberikan oleh pemain didalam game. Tombol-tombol pada menu utama dapat berfungsi.

DynaBot: Dynamic Dota 2 Bot

This research was conducted to find out whether the Dynamic Scripting method that has been used before only on zeus characters can be generalized to be used on other characters on the Dota 2 game. Dynamic scripting works by using the rulebase where the rulebase contains actions that determine the actions performed by Artificial Intelligence (AI). In addition, some adjustments have been made to existing methods. To find out whether the performance of a generalized and adjusted model is better than the previous model, a test has been conducted where AI is made with dynamic scripting against AI provided by the valve in the Dota 2 game. In addition, AI has also been tested against humans. Then the performance of AI will be analyzed by comparing the winning ratio and several other supporting variables. The results of this study are that AI got a low winning percentage against standard AI and cannot win at all and give poor performance against humans. It can be concluded that the Dynamic Scripting method cannot be generalized to other characters in the Dota 2 game.

Improving Dynamic Scripting for Adaptive Game AI with a Tactic Replacement Algorithm

Artificial Intelligence (AI) plays an important role in digital games nowadays. With players becoming increasingly demanding, it is vital to provide an AI that challenges and entertains them. The use of Adaptive Artificial Intelligence (AAI) has shown potential to adapt to each player by learning their techniques and offering a consistent challenge. This research consists on the analysis of an AAI technique known as Dynamic Scripting (DS) and in the development of a new algorithm (called Tactic Replacement) to improve it. Results show that, in comparison with the default DS algorithm, the proposed algorithm achieved a time reduction of ≈ 50% to achieve convergence. Also, it was able to reduce by 40% the average number of rounds to reach the convergence.

Error location in Python: where the mutants hide

Dynamic scripting programming languages present a unique challenge to software engineering tools that depend on static analysis. Dynamic languages do not benefit from the full lexical and syntax analysis provided by compilers and static analysis tools. Prior work exploited a statically typed language (Java) and a simple \(n\)-gram language model to find syntax-error locations in programs. This work investigates whether \(n\)-gram-based error location on source code written in a dynamic language is effective without static analysis or compilation. UnnaturalCode.py is a syntax-error locator developed for the Python programming language. The UnnaturalCode.py approach is effective on Python code, but faces significantly more challenges than its Java counterpart did. UnnaturalCode.py generalizes the success of previous statically-typed approaches to a dynamically-typed language.

Error location in Python: where the mutants hide

Dynamic scripting programming languages present a unique challenge to software engineering tools that depend on static analysis. Dynamic languages do not benefit from the full lexical and syntax analysis provided by compilers and static analysis tools. Prior work exploited a statically typed language (Java) and a simple \(n\)-gram language model to find syntax-error locations in programs. This work investigates whether \(n\)-gram-based error location on source code written in a dynamic language is effective without static analysis or compilation. UnnaturalCode.py is a syntax-error locator developed for the Python programming language. The UnnaturalCode.py approach is effective on Python code, but faces significantly more challenges than its Java counterpart did. UnnaturalCode.py generalizes the success of previous statically-typed approaches to a dynamically-typed language.