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S E C T I O N The Evolution of Game Al Paul Tozout-Ion Storm Austin gehn298yahoo.com Th e field of game artificial intelligence (AI) has existed since the dawn of video games in the 1970s. Its origins were humble, and the public perception of game A1 is deeply colored by the simplistic games of the 1970s and 1980s. Even today, game AI is haunted by the ghosts of Pac-Man2 Inky, Pinky, Blinky, and Clyde. Until very recently, the video game industry itself has done all too little to change this perception. However, a revolution has been brewing. The past few years have witnessed game AIs vastly richer and more entertaining than the simplistic AIs of the past. As 3D ren- dering hardware improves and the skyrocketing quality of game graphics rapidly approaches the point of diminishing returns, A1 has increasingly become one of the crit- ical factors in a game's success, deciding which games become bestsellers and determining the fate of more than a few game studios. In recent years, game AI has been quietly transformed from the redheaded stepchild of gaming to the shining star of the industry. The game A1 revolution is at hand. A Little Bit of History --miilh46844I "M1 "ass~.lrgysbpag)~-~~~~~w~~~>mP~~Bw~~~~~"*"B*B-"8 *"*"X*I b"s*s"s*e "9-*W" *"* At the dawn of gaming, AIs were designed primarily for coin-operated arcade games, and were carefully designed to ensure that the player kept feeding quarters into the machine. Seminal games such as Pong, Pac-Man, Space Invaders, Donkey Kong, and Joust used a handful of very simple rules and scripted sequences of actions combined with some random decision-making to make their behavior less predictable. Chess has long been a mainstay of academic AI research, so it's no surprise that chess games such as Chessmaster 2000 [Soffool86] featured very impressive A1 opponents. These approaches were invariably based on game tree search [SvarovskyOO]. Strategy games were among the earliest pioneers in game AI. This isn't surprising, as strategy games can't get very far on graphics alone and require good AI to even be playable. Strategy game AI is particularly challenging, as it requires sophisticated unit- level AI as well as extraordinarily complex tactical and strategic computer player AI. A number of turn-based strategy games, most notably MicroProse's Civilization [Micro- Prose911 and Civilization 2, come to mind as early standouts, despite their use of cheating to assist the computer player at higher di&culty settings. 4 Section 1 General Wisdom Even more impressive is the quality of many recent real-time strategy game AIs. Warcraft II [Blizzard951 featured one of the first highly competent and entertaining "RTS" AIs, and Age of Empires 2: The Age of Engs [Ensemble991 features the most challenging RTS AI opponents to date. Such excellent RTS AIs are particularly impressive in light of the difficult real-time performance requirements that an RTS AI faces, such as the need to perform pathfinding for potentially hundreds of units at a time. In the first-person shooter field, Valve Software's Half-Life [Valve98] has received high praise for its excellent tactical AI. The bots of Epic Games' Unreal: Tournament [Epic991 are well known for their scalability and tactical excellence. Looking Glass Studios' Thiej The Dark Project [LGS98], the seminal "first-person sneaker," stands out for its careful modeling of AIs' sensory capabilities and its use of graduated alert levels to give the player important feedback about the internal state of the AIs. Sierra Studios' SWAT 3: Close Quarters Battle [Sierra991 did a remarkable job of demon- strating humanlike animation and interaction, and took great advantage of randomized A1 behavior parameters to ensure that the game is different each time you play it. "Sim" games, such as the venerable SimCiy [Maxis89], were the first to prove the ~otentialo f artificial life ("A-Life") approaches. The Sims [MaxisOO] is particularly worth noting for the depth of ~ersonalityo f its A1 agents. This spectacularly popular game beautifully demonstrates the of fuzzy-state machines (FuSMs) and A- Life technologies. Another early contender in the A-Life category was the Creatures series, originat- ing with Creatures in 1996 [CyberLife96]. Creatures goes to great lengths to simulate the psychology and physiology of the "Norns" that populate the game, including, "Digital DNA" that is unique to each creature. "God games" such as the seminal hits Populous [Bullfrog89] and Dungeon Keeper [Bullfrog971 combined aspects of sim games and A-Life approaches with real-time strategy elements. Their evolution is apparent in the ultimate god game, Lionhead Studios' recent Black & White [Lionheado1 1. Black Q White features what is undoubtedly one of the most impressive game AIs to date-some of which is described in this book. Although it's certainly not the first game to use machine learning technologies, it's undoubtedly the most successful use of A1 learning approaches yet seen in a computer game. It's important to note that Black Q White was very carefully designed in a way that allows the A1 to shine-the game is entirely built around the concept of teaching and training your "creature." This core paradigm effectively focuses the player's atten- tion on the AI's development in a way that's impossible in most other games. Behind the Revolution A key factor in the success of recent game A1 has been the simple fact that developers are finally taking it seriously. Far too often, AI has been a last-minute rush job, imple- mented in the final two or three months of development by overcaffeinated program- 1.1 The Evolution of Game Al 5 *"M"~a- mers with dark circles under their eyes and thousands of other high-priority tasks to complete. Hardware constraints have also been a big roadblock to game AI. Graphics ren- dering has traditionally been a huge CPU hog, leaving little time or memory for the AI. Some AI problems, such as pathfinding can't be solved without significant processor resources. Console games in particular have had a difficult time with AI, given the painfully tight memory and performance requirements of console hardware until recent years. A number of the early failures and inadequacies of game AI also arose from an insufficient appreciation of the nature of game AI on the part of the development team itself-what is sometimes referred to as a "magic bullet" attitude. This usually manifests itself in the form of an underappreciation of the challenges of AI development-"we'll just use a scripting language9'--or an inadequate understanding of how to apply AI techniques to the task at hand-"we'll just use a big neural network." Recent years have witnessed the rise of the dedicated AI programmer, solely devoted to AI from Day One of the project. This has, by and large, been a smashing success. In many cases, even programmers with no previous AI experience have been able to produce high-quality game AI. AI development doesn't necessarily require a lot of arcane knowledge or blazing insights into the nature of human cognition. Quite often, all it takes is a down-to-earth attitude, a little creativity, and enough time to do the job right. Mainstream Al The field of academic artificial intelligence consists of an enormous variety of different fields and subdisciplines, many of them starkly ideologically opposed to one another. To avoid any of the potentially negative connotations that some readers might associate with the term "academic," we will refer to this field as mainstream AI. We cannot hope to understand game AI without also understanding something of the much broader field of artificial intelligence. A reasonable history of the evolution of the AI field is outside the scope of this article; nevertheless, this part of the book enumerates a handful of mainstream AI techniques-specifically, those that we AII consider most relevant to present and future game See [AI95] for an introduction to nearly all of these techniques. Expert systems attempt to capture and exploit the knowledge of a human expert within a given domain. An expert system represents the expert's expertise within a knowledge base, and performs automated reasoning on the knowledge base in response to a query. Such a system can produce similar answers to those that the human expert would have provided. Case-based reasoning techniques attempt to analyze a set of inputs by comparing them to a database of known, possibly historical, sets of inputs and the most advisable outputs in those situations. The approach was inspired by the human Section 1 General Wisdom tendency to apprehend novel situations by comparing them to the most similar situations one has experienced in the past. Finite-state machines are simple, rule-based systems in which a finite number of "states" are connected in a directed graph by "transitions" between states. The finite-state machine occupies exactly one state at any moment. Production systems are- comprised of a database of rules. Each rule consists of an arbitrarily complex conditional statement, plus some number of actions that should be performed if the conditional statement is satisfied. Production rule systems are essentially lists of "if-then" statements, with various conflict res- olution mechanisms available in the event that more than one rule is satisfied simultaneously. Decision trees are similar to complex conditionals in "if-then" statements. DTs make a decision based on a set of inputs by starting at the root of the tree and, at each node, selecting a child node based on the value of one input. Algorithms such as ID3 and C4.5 can automatically construct decision trees from sample data. Search methods are concerned with discovering a sequence of actions or states within a graph that satisfy some goal-either reaching a specified "goal state" or simply maximizing some value based on the reachable states. Planning systems and scheduling systems are an extension of search methods that emphasize the subproblem of finding the best (simplest) sequence of actions that one can perform to achieve a particular result over time, given an initial state of the world and a precise definition of the consequences of each possible action. First-order logic extends propositional logic with several additional features to allow it to reason about an AI agent within an environment. The world consists of "objects" with individual identities and "properties" that distinguish them from other objects, and various "relations" that hold between those objects and properties. The situation calculus employs first-order logic to calculate how an A1 agent should act in a given situation. The situation calculus uses automated reasoning to determine the course of action that will produce the most desirable changes to the world state. Multi-agent systems approaches focus on how intelligent behavior can naturally arise as an emergent property of the interaction between multiple competing and cooperating agents. Artificial life (or A-Life) refers to multi-agent systems that attempt to apply some of the universal properties of living systems to A1 agents in virtual worlds. Flocking is a subcategory of A-Life that focuses on techniques for coordinated movement such that AI agents maneuver in remarkably lifelike herds and flocks. Robotics deals with the problem of allowing machines to function interactively in the real world. Robotics is one of the oldest, best-known, and most successful fields of artificial intelligence, and has recently begun to undergo a renaissance because of the explosion of available computing power. Robotics is generally divided into sep- arate tasks of "control systems" (output) and "sensory systems" (input). Genetic algorithms and genetic programming are undoubtedly some of the most fascinating fields of AI (and it's great fun to bring them up whenever you find yourself in an argument with creationists). These techniques attempt to imi- - tate the process of evolution directly, performing selection and interbreeding with randomized crossover and mutation operations on populations of programs, algorithms, or sets of parameters. Genetic algorithms and genetic programming have achieved some truly remarkable results in recent years [Koza99], beautifully disproving the ubiquitous public misconception that a computer "can only do what we program it to do." Neural networks are a class of machine learning techniques based on the archi- tecture of neural interconnections in animal brains and nervous systems. Neural networks operate by repeatedly adjusting the internal numeric parameters (or weights) between interconnected components of the network, allowing them to learn an optimal or near-optimal response for a wide variety of different classes of learning tasks. Fuzzy logic uses real-valued numbers to represent degrees of membership in a number of sets-as opposed to the Boolean (true or false) values of traditional logic. Fuzzy logic techniques allow for more expressive reasoning and are capable of much more richness and subtlety than traditional logic. Belief networks, and the specific subfield of Bayesian inference, provide tools for modeling the underlying causal relationships between different phenomena, and use probability theory to deal with uncertainty and incomplete knowledge of the world. They also provide tools for making inferences about the state of the world and determining the likely effects of various possible actions. Game AIs have taken advantage of nearly all of these techniques at one point or another, with varying degrees of success. Ironically, it is the simplest techniques-finite-state machines, decision trees, and production rule systems-that have most often proven their worth. Faced with tight schedules and minimal resources, the game A1 community has eagerly embraced rules-based systems as the easiest type of A1 to create, understand, and debug. Expert systems share some common ground with game AI in the sense that many game AIs attempt to play the game as an expert human player would. Although a game AI's knowledge base is usually not represented as formally as that of an expert system, the end result is the same: an imitation of an expert player's style. Many board game AIs, such as chess and backgammon, have used game trees and game tree search with enormous success. Backgammon AIs now compete at the level of the best human players [SnowieOI]. Chess A1 famously proved its prowess with the bitter defeat of chess grandmaster Garry Kasparov by a massive supercomputer named "Deep Blue" [IBM97]. Other games, such as Go, have not yet reached the level of human masters, but are quickly narrowing the gap [GoOl]. Unfortunately, the complexities of modern video game environments and game mechanics make it impossible to use the brute-force game tree approach used by systems such as Deep Blue. Other search techniques are commonly used for game AI navigation and pathfinding, however. The A* search algorithm in particular deserves special men- tion as the reigning king of AI pathfinding in every game genre (see [StoutOO], [RabinOO] for an excellent introduction to A*, as well as [Matthews021 in this book). Game AI also shares an enormous amount in common with robotics. The significant challenges that robots face in attempting to perceive and comprehend the "real world" is dramatically different from the easily accessible virtual worlds that game AIs inhabit, so the sensory side of robotics is not terribly applicable to game AI. However, the control-side techniques are very useful for game AI agents that need to intelli- gently maneuver around the environment and interact with the player, the game world, and other AI agents. Game A1 agent pathfinding and navigation share an enormous amount in common with the navigation problems faced by mobile robots. Artificial life techniques, multi-agent systems approaches, and flocking have all found a welcome home in game AI. Games such as The Sims and SimCity have indis- putably proven the usefulness and entertainment value of A-Life techniques, and a number of successful titles use flocking techniques for some of their movement AI. Planning techniques have also met with some success. The planning systems developed in mainstream A1 are designed for far more complex planning problems than the situations that game A1 agents face, but this will undoubtedly change as modern game designs continue to evolve to ever higher levels of sophistication. Fuzzy logic has proven a popular technique in many game AI circles. However, formal first-order logic and the situation calculus have yet to find wide acceptance in games. This is most likely due to the difficulty of using the situation calculus in the performance-constrained environments of real-time games and the challenges of ade- quately representing a game world in the language of logical formalisms. Belief networks are not yet commonly used in games. However, they are particularly well suited to a surprising number of game AI subproblems [Tozour02]. The Problem of Machine Learning ~ ~ B B B ~ ~ ~ B _ B B _ B ~ a ~ e ~ m ~ ~ m m ~ ~ w w m s m M ~ a w ~ ~ B m ~ * ~ In light of this enormous body of academic research, it's understandable that the game A1 field sometimes seems to have a bit of an inferiority complex. Nowhere is this more true than with regard to machine learning techniques. It? beginning to sound like a worn recordfi.omy ear to year, but once again, game developers at GDC2001 described theirgame AIas not being in the same province as academic technologies such as neural networks and genetic algorithms. Game developers continue to use simple rules-basedj- nite- andfizzy-state machinesfor nearly all their AI need [WoodcockOl]. There are very good reasons for this apparent intransigence. Machine learning approaches have had a decidedly mixed history in game AI. Many of the early 1.1 The Evolution of Game Al 9 attempts at machine learning resulted in unplayable games or AIs that learned poorly, if at all. For all its potential benefits, learning, particularly when applied inappropri- ately, can be a disaster. There are several reasons for this: Machine learning (ML) systems can learn the wrong lessons. If the AI learns from the human player's play style, an incompetent player can easily miseducate the AI. ML techniques can be difficult to tune and tweak to achieve the desired results. Learning systems require a "fitness function" to judge their success and tell them how well they have learned. Creating a challenging and competent computer player is one thing, but how do you program a fitness function for "fun?" Some machine learning technologies-neural networks in particular-are heinously difficult to modify, test, or debug. Finally, there are many genres where in-game learning just doesn't make much sense. In most action games and hack-and-slash role-playing games, the A1 opponents seldom live long enough to look you in the eye, much less learn anything. Besides these issues, it's fair to say that a large part of the problem has sprung from the industry's own failure to apply learning approaches correctly. Developers have often attempted to use learning to develop an AI to a basic level of competence that it could have attained more quickly and easily with traditional rules-based A1 approaches. This is counterproductive. Machine learning approaches are most useful and appropriate in situations where the AI entities actually need to learn something. Recent games such as Black & White prove beyond any doubt that learning approaches are useful and entertaining and can add significant value to a game's AI. Learning approaches are powerful tools and can shine when used in the right context. The key, as Black ei. White aptly demonstrates, is to use learning as one carefully con- sidered component within a multilayered system that uses a number of other techniques for the many AI subtasks that don't benefit from machine learning. We Come in Pursuit of Fun Much of the disparity between game AI and mainstream AI stems from a difference in goals. Academic AI pursues extraordinarily difficult problems such as imitating human cognition and understanding natural language. But game AI is all about&n. At the end of the day, we are still a business. You have a customer who paid $40 for your game, and he or she expects to be entertained. In many game genres-action games in particular-it's surprisingly easy to develop an AI that will consistently trounce the player without breaking a sweat. This is "intelligentn from that AI's perspective, but it's not what our customers bought the game for. Deep Blue beat Kas- parov in chess, but it never tried to entertain its opponent-an assertion Kasparov - - himself can surely confirm. In a sense, it's a shame that game AI is called "AI" at all. The term does as much to obscure the nature of our endeavors as it does to illuminate it. If it weren't too late 10 Section 1 General Wisdom to change it, a better name might be "agent design" or "behavioral modeling." The word intelligence is so fraught with ambiguity that it might be better to avoid it completely. The misapprehension of intelligence has caused innumerable problems through- out the evolution of game AI. Our field requires us to design agents that produce appropriate behaviors in a given context, but the adaptability of humanlike "intelligence" is not always necessary to produce the appropriate behaviors, nor is it always desirable. Intelligence Is Context-Dependent The term "IQ" illustrates the problem beautifully. The human brain is an extraordi- nary, massively interconnected network of both specialized and general-purpose cog- nitive tools evolved over billions of years to advance the human species in a vast number of different and challenging environments. To slap a single number on this grand and sublime artifact of evolution can only blind us to its actual character. The notion of IQi s eerily similar to the peculiar Western notion of the "Great Chain of Being [Descartesl641]. Rather than viewing life as a complex and multi- faceted evolutionary phylogeny in which different organisms evolve within different environments featuring different selective pressures, the "Great Chain of Being col- lapses all this into a linear ranking. All living organisms are sorted into a universal pecking order according to their degree of "perfection," with God at the end of the chain. An article on IQi n a recent issue of Psychology Today [PTOl] caught my eye: In 1986, a colleague and Ipublished a study of men who fiquented the racetracks daily. Some were excellent handicappers, while others were not. What distinguished experts from nonexperts was the use of a complex mental algorithm that converted racing data taken from the racingpro- grams sold at the track. The use of the algorithm was unrelated to the men? IQscores, however. Some experts were dockworkers with IQs in the low 805, but they reasonedfar more complexly at the track than all nonexperts-even those with IQs in the upper 120s. In fact, experts were always better at reasoning complexly than nonexperts, regardless of their IQ scores. But the same experts who could reason so well at the track were often abysmal at reasoning outside the track- about, say, their retirement pensions or their social relationships. This quote gives us a good perspective on what game A1 is all about. What we need is not a generalized "intelligence," but context-dependent expertise. Game A1 is a vast panorama of specialized subproblems, including pathfinding, steering, flocking, unit deployment, tactical analysis, strategic planning, resource allo- cation, weapon handling target selection, group coordination, simulated perception, situation analysis, spatial reasoning, and context-dependent animation, to name a few.