Artificial Intelligence Video Games: Fundamentals and Applications

By Fouad Sabry

What Is Artificial Intelligence Video Games

Artificial intelligence (AI) is used in video games to develop responsive, adaptive, or intelligent behaviors, primarily in non-player characters (NPCs), that are akin to the intellect of humans. Since the beginning of the video game industry in the 1950s, artificial intelligence has been an essential component of the medium. Artificial intelligence (AI) in video games is a discrete topic that is distinct from AI in academic settings. Rather than serving the purposes of machine learning or decision making, it is designed to enhance the experience of game players. The concept of artificial intelligence (AI) opponents became very popular during the golden age of arcade video games. This concept manifested itself in the form of graduated difficulty levels, distinct movement patterns, and in-game events that were reliant on the player's input. The behavior of non-player characters (NPCs) in modern games is frequently governed by tried-and-true methods such as pathfinding and decision trees. Data mining and procedural content production are two examples of AI applications that are frequently utilized in methods that are not immediately obvious to the user.

How You Will Benefit

(I) Insights, and validations about the following topics:

Chapter 1: Artificial intelligence in video games

Chapter 2: Artificial intelligence

Chapter 3: List of artificial intelligence projects

Chapter 4: Video game programmer

Chapter 5: Interactive storytelling

Chapter 6: Outline of video games

Chapter 7: Outline of artificial intelligence

Chapter 8: General game playing

Chapter 9: Dynamic game difficulty balancing

Chapter 10: Machine learning in video games

(II) Answering the public top questions about artificial intelligence video games.

(III) Real world examples for the usage of artificial intelligence video games in many fields.

(IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of artificial intelligence video games' technologies.

Who This Book Is For

Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of artificial intelligence video games.

• Language: English
• Publisher: One Billion Knowledgeable
• Release date: Jul 4, 2023

Book preview

Chapter 1: Artificial intelligence in video games

Artificial intelligence (AI) is used in video games to create non-player characters (NPCs) with humanlike intelligence through the generation of responsive, adaptive, or intelligent behaviors. Since their inception in the 1950s, video games have relied heavily on artificial intelligence.

The term artificial intelligence (AI) in the context of video games does not refer to the creation of a computer program that can pass the Turing test or replicate human intelligence.

Since the term game AI is used to refer to a wide variety of algorithms, including those from control theory, robotics, computer graphics, and computer science in general, it's possible that the AI used in video games isn't true AI in the sense that it enables computer learning or meets other standard criteria; rather, it may just be automated computation or a limited set of responses to a limited set of inputs. Game AI for NPCs takes a very different tack than traditional AI does because it is focused on the appearance of intelligence and good gameplay within the constraints of the environment.

Artificial intelligence research has always included studying how to play games. Nim, a computer game created in 1951 and released in 1952, is often cited as one of the earliest examples of artificial intelligence. Although it was made with cutting-edge technology 20 years before Pong, the game was contained within a compact box and routinely triumphed over even the most accomplished players. The earliest video games, such as Spacewar!, Pong, and Gotcha (1973), were developed in the 1960s and early 1970s. These games relied on discrete logic and were based solely on two-player competition.

It wasn't until the 1970s that games with a single player mode and enemies appeared. Atari's Qwak (a duck hunting light gun shooter) and Taito's Pursuit (a racing video game) were the first prominent arcade releases in 1974. (fighter aircraft dogfighting simulator). Star Trek (1971) and Hunt the Wumpus (1973), two text-based computer games, also featured antagonists. Patterns of enemy behavior were recorded and used later. Movement patterns could benefit from additional computation and random elements if microprocessors were incorporated.

The success of Space Invaders (1978), which featured a gradually increasing difficulty, distinct movement patterns, and in-game events dependent on hash functions based on the player's input, popularized the concept of artificial intelligence opponents during the golden age of video arcade games. More nuanced and varied enemy movement was introduced in Galaxian (1979), such as rogue enemy individuals breaking formation. The 1980 maze game Pac-Man was the first to use AI patterns, and the enemies all had distinct personalities to boot. Karate Champ (1984) was the first fighting game to use AI patterns. The characters in First Queen (1988), a tactical action role-playing game, can be led by the computer. The Tactics system, in which the player can modify the AI routines of non-player characters during battle, was first seen in Dragon Quest IV (1990), and was later brought to the action role-playing game genre by Secret of Mana (1993).

Madden Football, Earl Weaver Baseball, and Tony La Russa Baseball all base their artificial intelligence on an attempt to simulate the coaching or managerial style of the corresponding celebrity manager or coach. Madden, Weaver, and La Russa all put in a lot of time with the developers of these games to ensure that they were as accurate as possible. In later sports games, players could tune AI parameters to implement a tactic chosen by themselves as a manager or coach.

In the 1990s, the proliferation of new game genres necessitated the implementation of formal AI tools such as finite state machines. The complexities of real-time strategy games, such as large numbers of objects, missing data, difficult pathfinding, split-second decisions, and strategic economic planning, put a strain on the AI. The genre's later games featured increasingly complex artificial intelligence.

Newer games employ bottom-up AI techniques, Games like Creatures and Black & White are great examples due to the emergent behavior and evaluation of player actions.

Façade (interactive story) was released in 2005 and used interactive multiple way dialogs and AI as the main aspect of game.

Video games have provided a testing ground for AI advancements that could have real-world implications. Watson, a computer programmed to play Jeopardy!, and the RoboCup tournament, in which soccer-playing robots have been trained to compete are two such examples.

Since game AI is not about intelligence and shares few goals with the academic field of AI, many specialists argue that the AI in the term game AI overstates its worth. Game AI typically consists of a handful of rules of thumb, or heuristics, that are just enough to give a good gameplay experience, while real AI addresses fields like machine learning, decision making based on arbitrary data input, and even the ultimate goal of strong AI that can reason. Because academic approaches to game AI have traditionally been simplistic and non-scalable, they have existed largely independently of commercial products. The industry has created its own AI tools, which have proven effective in many games.

The definition of what constitutes AI in a game is becoming less idiosyncratic as a result of game developers' growing familiarity with academic AI and the growing interest in computer games among the academic community. Game AI can nonetheless be seen as a separate subfield of AI due to the substantial differences between different application domains of AI. One key distinction is that some AI problems in games can be legitimately solved by cheating. When applied to robotics, determining the location of an unseen object based on previous observations can be challenging, but in a video game, a non-player character need only consult the game's scene graph. Such dishonesty is not always desirable because it can spark unsustainable patterns of behavior. However, the very fact that it is possible serves to differentiate game AI and introduces new challenges, such as when and how to cheat.

Strong AI is severely hampered by the extreme complexity of the decision-making process and the level of thought that it requires. This means that while developing smart AI is theoretically possible, doing so would require a lot of computational resources.

Computer chess

Computer shogi

Computer Go

Computer checkers

Computer Othello

Computer poker players

Akinator

Computer Arimaa

In the game of Reversi, Logistello is used.

Rog-O-Matic, a robot that performs Rogue

Humans versus Scrabble computers

The Computer Olympiad features a number of different board games.

General game playing

Games that have been solved by computers are guaranteed to have an optimal computer strategy, which may impose a win or draw on the game.

There are many different applications for artificial intelligence and heuristic algorithms in games. The most glaring example is undoubtedly manipulating the game's non-player characters, something that is traditionally done through scripting (a decision tree).

Action, first-person shooter, and adventure games are common in today's gaming market. There is typically some form of conflict resolution in these games. In these types of games, the AI's combat effectiveness is crucial. Attempts are currently being made to give artificial intelligence a more humanlike appearance.

The ability to hunt is one of the more positive and efficient features of modern AI in video games. At first, AI responded in a very binary way. The AI would take a wholly offensive or defensive stance depending on the player's location. Recently, the concept of hunting has been introduced; in this hunting state, the AI actively seeks out realistic markers, such as the character's own voice or the character's own footprints. Ultimately, these advancements make possible a higher level of complexity in play. The player now has a choice between directly engaging the enemy or running away from them. This trait is especially common in covert action movies.

The concept of survival instinct has also been developed in recent game AI updates. Computers within the game are able to identify various elements of a setting and weigh their importance to the character's continued survival. In a firefight, the AI can use cover like a human would before doing something risky like reloading a weapon or throwing a grenade. Indicators can be programmed to tell it when to take action. If the AI is instructed to monitor its health at regular intervals during play, for instance, it can be programmed to take