Semi-Automated Gameplay Analysis by Machine Learning

Semi-Automated Gameplay Analysis by Machine Learning Finnegan Southey, Gang Xiao, John Buchanan Robert C. Holte, Mark Trommelen Electronic Arts University of Alberta Abstract Gameplay is a popular term used throughout the industry. Lacking any precise definition, it typically refers to the While presentation aspects like graphics and sound are im- behaviour of a game (e.g., the rules, difficulty, consistency, portant to a successful commercial game, it is likewise im- fairness, goals, and constraints), rather than the presentation portant that the gameplay, the non-presentational behaviour of the game (e.g., graphics, artwork, and sound). Gameplay of the game, is engaging to the player. Considerable effort is invested in testing and refining gameplay throughout the de- contributes much to the enjoyability of a game and consid- velopment process. We present an overall view of the game- erable effort is invested in designing and refining gameplay. play management problem and, more concretely, our recent This problem is nothing new to game developers. Quality research on the gameplay analysis part of this task. This con- assurance and playtesting are fundamental parts of the de- sists of an active learning methodology, implemented in soft- velopment process. For the most part, these evaluations are ware tools, for largely automating the analysis of game be- made by continuously playing the game during development haviour in order to augment the abilities of game designers. and adjusting accordingly. Toward the end of the develop- The SAGA-ML (semi-automated gameplay analysis by ma- ment cycle, many human playtesters are employed to rigor- chine learning) system is demonstrated in a real commercial ously exercise the game, catching remaining bugs and fine- context, Electronic Arts' FIFA'99 Soccer title, where it has tuning its behaviour. identified exploitable weaknesses in the game that allow easy scoring by players. In the context of computer games, AI is usually associated with computer-controlled opponents and allies. While this is certainly a key application, it is not the only area in which AI Introduction can contribute to computer games. Our recent research has Modern computer games are very complex constructions, been directed toward what we call semi-automated game- sophisticated software systems involving many parameters play analysis, developing techniques to partially automate for the graphics, simulation, and artificial intelligence (AI). the process of evaluating gameplay in order to reduce the The virtual worlds portrayed in many games are three- burden on designers and improve the quality of the final dimensional, have rich physics models, and may feature product. To this end, we have developed sampling and ma- dozens or even hundreds of game agents, each of which may chine learning techniques that automatically explore the behave a wide range of properties. Computer games may have haviour of the game and provide intelligible summaries to many different levels or maps and there are often a variety the designer. We use the term semi-automatic to reinforce of objectives, constraints, rules and options in different sec- the notion that such tools cannot replace the human designer tions of the game. The number of players often varies too, but can only assist, helping them make complex decisions especially when playing on the Internet, and there may be a about what constitutes enjoyable gameplay. This analysis mixture of human and computer-controlled players. is a key component in the larger task of gameplay management, which deals not only with the analysis of gameplay This variability poses difficult problems for developers. but also with the visualization of analysis results and the ad- They must expend considerable effort to ensure that the justment of the game's parameters and design. game is "well-behaved" across the widest possible range of scenarios. Beyond the already difficult task of ensuring that In the following sections, we will briefly characterize the the game runs reliably (e.g., no crashes, graphical glitches, overall gameplay management task and its role in the de- or networking problems), they must ensure that the simu- velopment process, and then focus on gameplay analysis, lation is plausible and consistent, that artificial opponents presenting recent research efforts applied to Electronic Arts' and allies behave reasonably, and, most importantly, that the FIFA Soccer title. While discussed in this specific context, game is enjoyable for the player. We will refer to this range the approach is general and treats the game engine as a black of properties as the gameplay of a game. box. We describe the overall methodology and our current implementation, the reusable part of which we call SAGA- Copyright c 2005, American Association for Artificial Intelli- ML (semi-automated gameplay analysis by machine learn- gence (www.aaai.org). All rights reserved. ing). Our chief concern in this paper is to describe the overall approach rather than the details of the underlying ma- adjustment: Given the metrics, the ability to analyze, and chine learning. We address practical issues relevant to in- a set of goals, adjustments must be made to the game to dustry developers who may be interested in adopting the achieve the desired effect. Currently, most adjustment is methodology. We conclude by noting significant industry done by hand. It may be possible to automate parts of the interest in our work, as well as the possibilities for future process, with the designer setting and refining goals, and research and improvements to computer games. the software working to achieve them. This is a key part of the overall problem and is very difficult, but we will Gameplay Management not consider it deeply at present. Before one can adjust It is difficult to characterize the gameplay management task effectively, one must be able to analyze. precisely. This is largely due to the fact that it inevitably in- visualization: The results of analysis must be presented volves some human judgement. “Enjoyability” is essentially clearly to the designer. Methods for expressing goals and impossible to quantify. An integral but complex part of en- machine learning techniques for summarizing data are ar- joyability is the gameplay offered. One important aspect of eas where AI has much to offer, but a substantial part of good gameplay is appropriate difficulty. A game that is too visualization consists of user interface design. We have hard is frustrating, while too little challenge can be boring. worked extensively on a visualization tool for our current In multi-player games, it is important that the game be fair, case study and consider this a very important part of the offering no player an intrinsic advantage. Internal consis- problem, but one in which our expertise can have only tency in the game world is also important. Even in fictional limited impact. Ultimately, game developers will best un- worlds, players expect some sort of logic to apply. derstand how to interact with their game. The designer must shape and regulate the gameplay throughout the game (e.g., by controlling difficulty and vary- Gameplay Analysis ing the rules and objectives). It would be naive to think that this can be addressed without human intervention. How- As noted above, our current research efforts are focused on ever, computational tools can reduce the human burden, al- the task of gameplay analysis. The metrics to be evaluated low more extensive testing, and search in systematic ways will be game dependent, but the analysis methods are one that may be tedious or unintuitive to a human. Where part that may be transferred between projects. Our method- possible, these tools should be applicable to many games. ology embraces three basic tools used in many branches of This re-usability is important since industry development AI research: abstraction, sampling, and machine learning. cycles have little spare time to redevelop such tools for each While our framework is intended to be independent of project. The tools must also be easy to use. Many designers game, and even of genre, we illustrate the approach with our 1 are not programmers, so it is not reasonable to expect them implementation for Electronic Arts' FIFA'99 Soccer game. to write or understand complex scripts to guide the process. In particular, we have developed a general purpose analy- We will now outline the gameplay management task. By sis tool (SAGA-ML) and a FIFA-specific visualization tool decomposing this process more formally, we can address the (SoccerViz). The aim is to identify “sweet spots” (manoeu- components one by one in our research. vres that can be used to repeatedly score goals with unrea- sonable ease) and “hard spots” (situations where it is too The aspects of gameplay of interest to gameplay metrics: difficult to score) in various FIFA scenarios. In this case, the the designer. These are the game aspects we attempt to metric of interest is the probability of scoring. measure. Typical metrics include the time to complete part of the game, the “cost” (e.g., health, game money, re- sources) involved, and the probability of succeeding. The Electronic Arts' FIFA'99 Soccer amount of variance in any one of these is also of interest We have used SAGA-ML to analyze scenarios in the Elec- (highly variable outcomes may indicate an inconsistency, tronic Arts (EA) soccer game, FIFA'99. Games are gener- a bug, or a poorly chosen parameter). Typically, we want ally too complex to be analyzed in their entirety. It makes to evaluate a metric given some initial game state and a sense to analyze certain specific scenarios, especially when set of player actions taken from that initial state. the game itself distinguishes between scenarios (e.g., different levels, minigames, and control modes). We have ex- goals: The targets the designer wishes to achieve.

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