scholarly journals Patrolling AI Systems in Video Games

2019 ◽  
Vol 8 (4) ◽  
pp. 2479-2485
Keyword(s):  
Artificial Intelligence ◽  
Video Games ◽  
Real Time ◽  
Dynamic Game ◽  
Common Element ◽  
Path Finding ◽  
Game Engine ◽  
Adventure Games ◽  
Game Environment ◽  
Moving Obstacle

Artificial Intelligence is a common element in digital video games and it is one of the most essential component in modern games. Modern games are populated with Non-Player Characters (AI Characters) that performs different activities. Realism is dominating in games and AI behavio rs are expected to be more realistic in games. Games that has poor unrealistic AI elements are facing heavy criticism from the player bases. Further, modern games are highly dynamic. Classical games had static environment with less or no changes in it. Such environments made implementation of AI easy and simple. In modern games, Progressive terrain generation and other such content generation methods increases the complexities of building an efficient AI for games that has many changes in real time. One of the most common AI in games is Patrolling AI especially in Shooter and Adventure Games. Patroling AI involves path finding and obstacle attack or defense. RRT algorithm and its variants are highly successful Probabilistic Determination AI that produced effective results in real time robotic movement. In order to build efficient Patrolling AI for games, a real time RRT* variant called RT-RRT* algorithm was employed. The algorithm is flexible enough to add various behaviors in addition to path finding which makes it more suitable for games. The algorithm takes samples from the environment and construct the efficient path. Also the algorithm inspect the environment in run time to ensure that no moving obstacle blocks the path. In such case, it rewires and create a new path. In order to manage the dynamic obstacles, a Real Time Obstacle Handling Algorithm was designed and employed in a dynamic game environment. The algorithms inputs the obstacles types and parameters. On identifying the obstacle approaching the AI in terrain, it tells the agent to perform the needed actions. The simulations was carried out using Unity Game Engine. The model proposed helped to create efficient patrolling AI that handle two major aspects of patrolling which is Path finding and Obstacle handling. The model will be highly suitable for dynamic game environments with lots of uncertainty and emergence.

scholarly journals Npc Behavior Plugin Development for Game Engine Unity

2020 ◽  
Vol 23 (5) ◽  
pp. 1044-1057
Author(s):  
Leonid Nikolaevich Parenyuk ◽  
Vlada Vladimirovna Kugurakova
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Real Time ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Game Engine ◽  
Game Development ◽  
Advantages And Disadvantages

There are various approaches for creating artificial intelligence in games, and each has both advantages and disadvantages. This study describes an authoring implementation of the NPC behavior task using machine learning algorithms that will be associated with the Unity environment in real time. This approach can be used in game development.

scholarly journals Unifying Rigid and Soft Bodies Representation: The Sulfur Physics Engine

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Dario Maggiorini ◽  
Laura Anna Ripamonti ◽  
Federico Sauro
Keyword(s):  
Video Games ◽  
Video Game ◽  
General Purpose ◽  
Game Engine ◽  
Unified Approach ◽  
Game Mechanics ◽  
Physics Simulation ◽  
Game Engines ◽  
Player Experience ◽  
Game Environment

Video games are (also) real-time interactive graphic simulations: hence, providing a convincing physics simulation for each specific game environment is of paramount importance in the process of achieving a satisfying player experience. While the existing game engines appropriately address many aspects of physics simulation, some others are still in need of improvements. In particular, several specific physics properties of bodies not usually involved in the main game mechanics (e.g., properties useful to represent systems composed by soft bodies), are often poorly rendered by general-purpose engines. This issue may limit game designers when imagining innovative and compelling video games and game mechanics. For this reason, we dug into the problem of appropriately representing soft bodies. Subsequently, we have extended the approach developed for soft bodies to rigid ones, proposing and developing a unified approach in a game engine: Sulfur. To test the engine, we have also designed and developed “Escape from Quaoar,” a prototypal video game whose main game mechanic exploits an elastic rope, and a level editor for the game.

scholarly journals Pergerakan Pasukan Untuk Mengejar Musuh Bergerak Menggunakan D* Lite Berbasis Algoritma Pathfinding

2017 ◽  
Vol 2 (1) ◽  
pp. 45
Author(s):  
Mochamad Kholil
Keyword(s):  
Artificial Intelligence ◽  
Real Time ◽  
Video Game ◽  
Moving Target ◽  
Environment Change ◽  
Solid Foundation ◽  
Artificial Intelligence Algorithm ◽  
Strategy Game ◽  
Game Environment ◽  
Real Time Strategy Game

The movement of agents in the Real Time Strategy game influenced by several factors, one of which is the technique of agent movement within the game environment. Pathfinding in a video game is an artificial intelligence algorithm how to find an agent moves the optimal way in which there are obstacles in the environment. This can be achieved by implementing a pathfinding algorithm to the game. This study of the D* Lite algorithm is able to plan a search path in an game environment, change the environment and moving target to be efficient, optimal and complete for agents and will describe some way of planning applications and provides a solid foundation for further research on methods of search re in artificial intelligence.Keywords: Agent Movement, Real Time Strategy, Pathfinding, D* Lite AbstrakPergerakan agen pada permainan Real Time Strategy dipengaruhi oleh beberapa faktor salah satunya adalah teknik pergerakan agen didalam lingkungan permainan. Pathfinding dalam video game merupakan algoritma kecerdasan buatan bagaimana cara sebuah agen bergerak menemukan jalan optimal dengan usaha minimal sampai pada tujuan. Hal ini bisa dicapai dengan mengimplementasikan suatu algoritma pathfinding pada game. Penelitian ini mengenai algoritma D* Lite yang mampu merencanakan pencarian jalur di lingkungan game dengan environment yang berubah sekaligus objek yang sebagai target bergerak dan menjadikan proses pengejaran target menjadi efisien bagi agen serta memberikan dasar yang kuat untuk penelitian lebih lanjut tentang metode pencarian ulang dalam kecerdasan buatanKata kunci: Agent Movement, Real Time Strategy, Pathfinding, D* Lite

scholarly journals Perceptual, Attentional, and Executive Functioning After Real-Time Strategy Video Game Training: Efficacy and Relation to In-Game Behavior

2021 ◽  
Author(s):  
Paweł Dobrowolski ◽  
Maciek Skorko ◽  
Monika Myśliwiec ◽  
Natalia Kowalczyk-Grębska ◽  
Jakub Michalak ◽  
...  
Keyword(s):  
Video Games ◽  
Executive Functioning ◽  
Cognitive Functioning ◽  
Real Time ◽  
Video Game ◽  
Cognitive Abilities ◽  
Training Group ◽  
Cognitive Improvement ◽  
Game Environment ◽  
Meta Analyses

AbstractRecent meta-analyses and meta-analytic reviews of most common approaches to cognitive training broadly converge on describing a lack of transfer effects past the trained task. This also extends to the more recent attempts at using video games to improve cognitive abilities, bringing into question if they have any true effects on cognitive functioning at all. Despite this, video game training studies are slowly beginning to accumulate and provide evidence of replicable improvements. Our study aimed to train non-video game playing individuals in the real-time strategy video game StarCraft II in order to observe any subsequent changes to perceptual, attentional, and executive functioning. Thirty hours of StarCraft II training resulted in improvements to perceptual and attentional abilities, but not executive functioning. This pattern of results is in line with previous research on the more frequently investigated “action” video games. By splitting the StarCraft II training group into two conditions of “fixed” and “variable” training, we were also able to demonstrate that manipulating the video game environment produces measurable differences in the amount of cognitive improvement. Lastly, by extracting in-game behavior features from recordings of each participant’s gameplay, we were able to show a direct correlation between in-game behavior change and cognitive performance change after training. These findings highlight and support the growing trend of more finely detailed and methodologically rigorous approaches to studying the relationship between video games and cognitive functioning.

scholarly journals Pembangunan Game 3D Real Time Strategy “Bug Invasion” Berbasis Desktop

2021 ◽  
Vol 3 (2) ◽  
pp. 28-42
Author(s):  
Rudi Kurniawan ◽  
Rizal Yudha Pradatama
Keyword(s):  
Artificial Intelligence ◽  
Life Cycle ◽  
Real Time ◽  
Game Engine ◽  
Town Hall ◽  
Development Life Cycle ◽  
Multimedia Development

Banyaknya judul-judul baru yang ditawarkan oleh permainan dengan genre Real Time Strategy, dari hasil pengamatan pada kenyataannya belum banyak yang menawarkan mekanisme baru dalam permainan tersebut. Penelitian ini bertujuan untuk menghasilkan sebuah mekanisme baru dengan menawarkan sebuah metode mikro ekonomi. Dengan metode mikro ekonomi permainan dapat memberikan tantangan baru bagi para Pemain berupa pembatasan (boundary) dalam penempatan posisi suatu objek, misalnya banyaknya jumlah bangunan yang dibangun dalam satu area tidak dapat dilakukan seara sembarangan. Pada permainan ini dikembangkan juga sebuah Artificial Intelligence (AI) yang digunakan sebagai kubu lawan dalam sebuah simulasi pertempuran melawan pemain nyata (manusia). Pada proses pembangunan game 3D digunakan metode Multimedia Development Life Cycle (MDLC) yaitu (1) Tahap konsep; (2) Tahap desain; (3) Tahap pengumpulan material (gambar, model character, animasi, video, audio dan lain-lain); (4) Tahap pembuatan (penggabungan semua objek berdasarkan tahap konsep dan tahap desain); (5) Tahap pengujian, alfa serta beta; (6) Tahap pendistribusian (rilis game kepada target pengguna). Adapun tools yang digunakan yaitu Game Engine Unity dengan bahasa pemrograman C#. Dari hasil ujicoba dengan mekanisme ini, permainan baru yang menggunakan sebuah metode mikro ekonomi telah mampu memberikan batasan terhadap penempatan posisi serta banyaknya bangunan yang ditempatkan yaitu dengan cara membuat sebuah border berbentuk lingkaran yang berpusat dimasing-masing town hall Pemain maupun AI. Pembuatan sebuah border yang berbentuk lingkaran digunakan sebagai penanda batas wilayah antara Pemain dan AI. Dengan demikian Pemain maupun AI tidak akan dapat membangun bangunan yang mereka miliki serta mengambil sumber daya yang berada di luar batas dari border/ wilayahnya

scholarly journals BattleNet: Capturing Advantageous Battlefield in RTS Games (Student Abstract)

2020 ◽  
Vol 34 (10) ◽  
pp. 13849-13850
Author(s):  
Donghyeon Lee ◽  
Man-Je Kim ◽  
Chang Wook Ahn
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Real Time ◽  
Large Scale ◽  
Outcome Predictor ◽  
Short Term ◽  
Rts Game

In a real-time strategy (RTS) game, StarCraft II, players need to know the consequences before making a decision in combat. We propose a combat outcome predictor which utilizes terrain information as well as squad information. For training the model, we generated a StarCraft II combat dataset by simulating diverse and large-scale combat situations. The overall accuracy of our model was 89.7%. Our predictor can be integrated into the artificial intelligence agent for RTS games as a short-term decision-making module.

scholarly journals Design of a dynamic and self-adapting system, supported with artificial intelligence, machine learning and real-time intelligence for predictive cyber risk analytics in extreme environments – cyber risk in the colonisation of Mars

2021 ◽  
Author(s):  
Petar Radanliev ◽  
David De Roure ◽  
Kevin Page ◽  
Max Van Kleek ◽  
Omar Santos ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Real Time ◽  
Cyber Security ◽  
Extreme Environments ◽  
Learning Technologies ◽  
Cyber Attacks ◽  
Edge Computing ◽  
Mathematical Formulas ◽  
Cyber Risk

AbstractMultiple governmental agencies and private organisations have made commitments for the colonisation of Mars. Such colonisation requires complex systems and infrastructure that could be very costly to repair or replace in cases of cyber-attacks. This paper surveys deep learning algorithms, IoT cyber security and risk models, and established mathematical formulas to identify the best approach for developing a dynamic and self-adapting system for predictive cyber risk analytics supported with Artificial Intelligence and Machine Learning and real-time intelligence in edge computing. The paper presents a new mathematical approach for integrating concepts for cognition engine design, edge computing and Artificial Intelligence and Machine Learning to automate anomaly detection. This engine instigates a step change by applying Artificial Intelligence and Machine Learning embedded at the edge of IoT networks, to deliver safe and functional real-time intelligence for predictive cyber risk analytics. This will enhance capacities for risk analytics and assists in the creation of a comprehensive and systematic understanding of the opportunities and threats that arise when edge computing nodes are deployed, and when Artificial Intelligence and Machine Learning technologies are migrated to the periphery of the internet and into local IoT networks.

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