Skenario Dinamis Menggunakan DCA (Dynamic Challenging Level Adapter) pada Permainan 2D Bergenre Real Time Strategy Tower Defense

Permainan Real-time Strategy (RTS) merupakan permainan yang bersifat kompetitif, antara pemain melawan pemain ataupun mela- wan non-player character (NPC). Banyak pengembang permainan menggunakan kemampuan NPC lawan yang terstruktur secara sta- tis. Kemampuan tersebut tidak selalu dapat memberikan tingkat tantangan (challenging rate (CR)) yang sesuai ke berbagai tipe ka- rakter pemain. Oleh karena itu dibutuhkan suatu kecerdasan bu- atan (AI) yang dapat mengatur kemampuannya terhadap berbagai tipe karakter pemain, sehingga dihasilkan pola permainan yang di- namis serta tingkat tantangan yang sesuai. Pada tugas akhir ini akan diimplementasikan dynamic challenging level adapter (DCA) sebagai mekanisme untuk beradaptasi terhadap unsur permainan dan menentukan pemilihan keputusan. Pemilihan keputusan ak- an menggunakan bantuan decision tree dan penyelesaian knapsack problem digunakan untuk memilih kombinasi pasukan yang tepat dalam mengatasi keadaan. Untuk mengukur performa sistem AI de- ngan DCA, dibuat AI yang didasari tingkat kesulitan yang umum digunakan yaitu mudah, sedang, dan sukar untuk menjadi lawan tanding. Hasil pengujian AI dengan DCA didapatkan rata-rata da- ri rata-rata selisih nilai CR tiap waktu untuk hasil pertarungan dengan berbagai tipe tingkat kesulitan adalah 34,02, dibandingkan dengan rata-rata untuk hasil pertarungan dengan tingkat kesulit- an yang setara adalah 33,65 sehingga hanya terpaut sebesar 0,37. Sedangkan dengan tingkat kesulitan tidak setara didapatkan rata- rata terpaut sebesar 55,98 dari rata-rata tingkat kesulitan setara. Dengan hasil tersebut dapat disimpulkan penggunaan DCA dapat menjadi pengganti tingkat kesulitan yang statis namun tetap dengan tingkat tantangan yang sesuai bagi tiap tipe karakter pemain. ========================================================================================================== Real-time Strategy (RTS) game is a game with competitive envi- ronment, that can be played by player versus player or non-player character (NPC). Many game developer implement enemy NPC wi- th static pattern. Ability of static NPC not always give challenge to any kind of model player. Because of that, it needed an arti�cial intelligence (AI) that can maintain it's ability to any kind of mo- del player, so it's produce dynamic ow of gameplay and balance in challenging level. In this �nal project will be implemented dynamic challenging level adapter (DCA) as mechanism for adapting of ele- ment in game environment and choose decision. Decision selection will be handled with decision tree to decide outcome for all condition, and solution of knapsack problem will be used for choosing unit com- bination to handle the condition. To see what AI NPC with DCA is capable of, another AI is created that implement simple mechanism with default di�culty level like easy, medium, and hard. This new AI will be enemy for AI with DCA. The result of implementation NPC with DCA give the average of average di�erence in CR each time was 34,02 as the result of battle NPC with DCA and without DCA with di�erent di�culty level. Comparing the result of NPC without DCA battle with the same di�culty level, the average of average di�erence in CR each time was 33,65, adrift at 0.37. While with di�erent di�culty level got the average adrift at 55,98 from the same di�culty level. With these result we can conclude the use of DCA can be a substitute for static di�culty level but still with the same challenging level to every type of model player

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

A METHODOLOGY FOR TECHNOLOGY-TUNED DECISION BEHAVIOR ALGORITHMS FOR TACTICS EXPLORATION

In 2016, the USAF found that current development and acquisition methods may be inadequate to achieve air superiority in 2030. The airspace is expected to be highly contested by 2030 due to the Anti-Access/Area Denial strategies being employed by adversaries. Capability gaps must be addressed in order to maintain air superiority. The USAF identified new development and acquisition paradigms as the number one non-material capability development area. The idea of a new development and acquisition paradigm is not new. Such a paradigm shift occurred during the transition from threat-based acquisition during the cold war to capability-based acquisition during the war on terror. Investigation into current US development and acquisition methods found several notional methodologies. Effectiveness-Based Design and Technology Identification, Evaluation, and Selection for Systems-of-Systems have been proposed as notional solutions. Both methodologies seek to evaluate the means – the technologies used to perform a mission – and the ways – the tactics used to complete a mission – of the technology design space. Proper evaluation of the ways would provide critical information to the decision-maker during technology selection. These findings suggest that a new paradigm focused on effectiveness-based acquisition is needed to improve current development and acquisition methods. To evaluate the ways design space, current methods must move away from a fixed or constrained mission model to one that is minimally defined and capable of exploring tactics for each unique technology. The proposed Technology-tuned Decision Behavior Algorithms for Tactics Exploration (Tech-DEBATE) methodology enables the exploration of the ways, or more formally, the mission action design space. The methodology enables further exploration of the technology design space by improving the quantification of mission effectiveness through deep reinforcement learning in a minimally defined mission environment. The data's foundation is based on traceable tactical alternatives that increase the confidence in the measures of effectiveness for each technology-tactic alternative. The methodology enables more informed decisions for technology investment, thereby reducing risks in the development and acquisition of new technologies. The reduction in risk inherently reduces the costs and development time associated with investment in new technologies. The Tech-DEBATE methodology provides a new methodology for technology evaluation through its emphasis on quantifying mission effectiveness in a minimally defined mission to inform technology investment decisions.Ph.D

Using adaptation and goal context to automatically generate individual personalities for virtual characters

Personality is a key component of characters that inhabit immersive virtual environments, such as games and virtual agent applications. In order to be distinguishable from other characters in the environment, each character should have its own personality in the form of different observable behaviour, not solely in its physical appearance or animation. Previous work in this field has mostly relied on time-consuming, handcrafted characters and static, trait-based approaches to personality. Our goal is a method to develop complex, individual personalities without handcrafting every behaviour. Unlike most implemented versions of personality theories, cognitive-social theories of personality address how personality is developed and adapts throughout childhood and over our lifetimes. Cognitive-social theories also emphasise the importance of situations in determining how we behave. From this basis, we believe that personality should be individual, adaptive, and based on context. Characters in current state-of-the-art games and virtual environments do not demonstrate all of these features without extensive handcrafting. We propose a model where personality influences both decision-making and evaluation of reward. Characters use their past experiences in the form of simple somatic markers, or gut-instinct, to make decisions; and determine rewards based on their own personal goals, rather than via external feedback. We evaluated the model by implementation of a simple game and tested it using quantitative criteria, including a purpose-designed individuality measure. Results indicate that, although characters are given the same initial personality template, it is possible to develop different personalities (in the form of behaviour) based on their unique experiences in the environment and relationships with other characters. This work shows a way forward for more automated development of personalities that are individual, context-aware and adapt to users and the environment