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Practical Video Game Bots: Automating Game Proc...

Develop and use bots in video gaming to automate game processes and see possible ways to avoid this kind of automation. This book explains how bots can be very helpful in games such as multiplayer online games, both for training your character and for automating repetitious game processes in order to start a competition with human opponents much faster.

Practical Video Game Bots: Automating Game Proc...

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Some players might use bots for cheating or avoiding game rules to gain an advantage over opponents - a sophisticated form of hacking that includes some elements of artificial intelligence (AI). However, while Practical Video Game Bots considers these topics, it is not a cheater's guide. Rather, this book is an attempt to overcome the information vacuum regarding bot development in video game applications. Through the use of three case study game examples, it covers most methods and technologies that are used by bot developers, and the details of anti-cheating systems.

Algorithms are instructions for solving a problem or completing a task. Recipes are algorithms, as are math equations. Computer code is algorithmic. The internet runs on algorithms and all online searching is accomplished through them. Email knows where to go thanks to algorithms. Smartphone apps are nothing but algorithms. Computer and video games are algorithmic storytelling. Online dating and book-recommendation and travel websites would not function without algorithms. GPS mapping systems get people from point A to point B via algorithms. Artificial intelligence (AI) is naught but algorithms. The material people see on social media is brought to them by algorithms. In fact, everything people see and do on the web is a product of algorithms. Every time someone sorts a column in a spreadsheet, algorithms are at play, and most financial transactions today are accomplished by algorithms. Algorithms help gadgets respond to voice commands, recognize faces, sort photos and build and drive cars. Hacking, cyberattacks and cryptographic code-breaking exploit algorithms. Self-learning and self-programming algorithms are now emerging, so it is possible that in the future algorithms will write many if not most algorithms.

In a broad sense, most video games incorporate some form of AI. Nevertheless, modern AI methods can be applied in distinct areas that help game companies realize business benefits as well. For example, through enhancing photorealistic effects, generating content, balancing in-game complexities, and providing intelligence to non-playing characters (NPCs), AI improves the overall user experience while saving game companies budget and time.

With modern video games frequently featuring sophisticatedand realistic environments, the needfor smart and comprehensive agents that understandthe various aspects of complex environmentsis pressing. Since video game AI is oftenspecifically designed for each game, video game AItools currently focus on allowing video game developersto quickly and efficiently create specificAI. One issue with this approach is that it does notefficiently exploit the numerous similarities thatexist between video games not only of the samegenre, but of different genres too, resulting in adifficulty to handle the many aspects of a complexenvironment independently for each video game. Inspired by the human ability to detect analogiesbetween games and apply similar behavior on aconceptual level, this paper suggests an approachbased on the use of a unified conceptual frameworkto enable the development of conceptual AIwhich relies on conceptual views and actions todefine basic yet reasonable and robust behavior. The approach is illustrated using two video games,Raven and StarCraft: Brood War.

Because artificial intelligence (AI) is a broad notion in video games, it is important to start by defining the scope of this work. A video game can be considered to have two main aspects, the context and the game. The game includes the elements that define the actual challenges players face and the problems they have to solve, such as rules and objectives. On the other hand, the context encompasses all the elements that make up the setting in which these problems appear, such as characters and plot. This work focuses on game AI, that is, AI which is concerned with solving the problems in the game such as defeating an opponent in combat or navigating in a maze. Conversely, context AI would deal with context-specific tasks such as making a character perform a series of actions to advance the plot or reacting to player choices. Thus, the scope of discussion is limited to the game aspect in this work.

Since video games are designed for human beings, it is only natural that they focus on their cognitive skills and physical abilities. The richer and more complex a game is, the more skills and abilities it requires. Thus, creating a truly smart and fully autonomous agent for a complex video game can be as challenging as replicating a large part of the complete human intelligence. On the other hand, AI is usually independently designed for each game. This makes it difficult to create thoroughly robust AI because its development is constrained to the scope of an individual game project. Although each video game is unique, they can share a number of concepts depending on their genre. Genres are used to categorize video games according to the way players interact with them as well as their rules. On a conceptual level, video games of the same genre typically feature similar challenges based on the same concepts. These similar challenges then involve common problems for which basic behavior can be defined and applied regardless of the problem instance. For example, in a first-person shooter one-on-one match, players face problems such as weapon selection, opponent position prediction and navigation. Each moment, a player needs to evaluate the situation and switch to the most appropriate weapon, predict where the opponent likely is or is heading and find the best route to get there. All of these problems can be reasoned about on a conceptual level using data such as the rate of fire of a weapon, the current health of the opponent and the location of health packs. These concepts are common to many first-person shooter games and are enough to define effective behavior regardless of the details of their interpretation. Such solutions already exist for certain navigation problems for instance and are used across many video games. Moreover, human players can often effortlessly use the experience acquired from one video game in another of the same genre. A player with experience in first-person shooter games will in most cases perform better in a new first-person shooter game than one without any experience and can even perform better than a player with some experience in the new game, indicating that it is possible to apply the behavior learned for one game in another game featuring similar concepts to perform well without knowing the details of the latter. Obviously, when the details are discovered, they can be used to further improve the basic conceptual behavior or even override it. It may therefore be possible to create cross-game AI by identifying and targeting conceptual problems rather than their game-specific instances. Detaching AI or a part of it from the development of video games would remove the project constraints that push developers to limit it and allow it to have a continuous and more thorough design process.

Conceptualizing video games is a process which involves abstraction and is similar to many other approaches that share the same goal, namely, that of factoring AI in video games. More generally, abstraction makes it possible to create solutions for entire families of problems that are essentially the same when a certain level of detail is omitted. For example, the problem of sorting an array can take different forms depending on the type of elements in the array, but considering an abstract data type and comparison function allows a programmer to write a solution that can sort any type of array. This prevents unnecessary code duplication and helps programmers make use of existing solutions as much as possible so as to minimize development efforts. Another example of widely used abstraction application is hardware abstraction. Physical components in a computer can be seen as abstract devices in order to simplify software development. Different physical components that serve the same purpose, storage for example, can be abstracted into a single abstract storage device type, allowing software developers to write storage applications that work with any kind of storage component. Such a mechanism is used in operating systems such as NetBSD [1] and the Windows NT operating system family [2].

The idea of creating an AI middleware for video games is also discussed in Karlsson [7], where technical issues and approaches for creating such middleware are explored. Among other things, it is argued that when state systems are considered, video game developers require a solution in between simple finite state machines and complex cognitive models. Another interesting argument is that functionality libraries would be more appropriate than comprehensive agent solutions because they provide more flexibility while still allowing agent-based solutions to be created. Here too, the possibility of creating specialized AI hardware was mentioned and a parallel with the impact mainstream graphics acceleration cards had on the evolution of computer graphics was drawn.

An Open AI Standard Interface Specification (OASIS) is proposed in Berndt et al. [8], aiming at making it easier to integrate AI in video games. The OASIS framework is designed to support knowledge representation as well as reasoning and learning and comprises five layers each dealing with different levels of abstraction, such as the object level or the domain level, or providing different services such as access, translation or goal arbitration services. The lower layers are concerned with interacting with the game while the upper layers deal with representing knowledge and reasoning. 041b061a72

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