Tobias Mahlmann

Predicting player behavior in Tomb Raider: Underworld

This paper presents the results of an explorative study on predicting aspects of playing behavior for the major commercial title Tomb Raider: Underworld (TRU). Various supervised learning algorithms are trained on a large-scale set of in-game player behavior data, to predict when a player will stop playing the TRU game and, if the player completes the game, how long will it take to do so. Results reveal that linear regression models and other non-linear classification techniques perform well on the tasks and that decision tree learning induces small yet well-performing and informative trees. Moderate performance is achieved from the prediction models, which indicates the complexity of predicting player behavior based on a constrained set of gameplay metrics and the noise existent in the dataset examined, a generic problem in large-scale data collection from millions of remote clients.

Evolving card sets towards balancing dominion

In this paper we use the popular card game Dominion as a complex test-bed for the generation of interesting and balanced game rules. Dominion is a trading-card-like game where each card type represents a different game mechanic. Each playthrough only features ten different cards, the selection of which can form a new game each time. We compare and analyse three different agents that are capable of playing Dominion on different skill levels and use three different fitness functions to generate balanced card sets. Results reveal that there are particular cards of the game that lead to balanced games independently of player skill and behaviour. The approach taken could be used to balance other games with decomposable game mechanics.

Skill-based differences in spatio-temporal team behaviour in defence of the Ancients 2 (DotA 2)

Multiplayer Online Battle Arena (MOBA) games are among the most played digital games in the world. In these games, teams of players fight against each other in arena environments, and the gameplay is focussed on tactical combat. In this paper, we present three data-driven measures of spatio-temporal behaviour in Defence of the Ancients 2 (DotA 2): 1) Zone changes; 2) Distribution of team members and: 3) Time series clustering via a fuzzy approach. We present a method for obtaining accurate positional data from DotA 2. We investigate how behaviour varies across these measures as a function of the skill level of teams, using four tiers from novice to professional players. Results from three analyses indicate that spatio-temporal behaviour of MOBA teams is highly related to team skill.

Spicing up map generation

We describe a search-based map generator for the classic real-time strategy game Dune 2. The generator is capable of creating playable maps in seconds, which can be used with a partial recreation of Dune 2 that has been implemented using the Strategy Game Description Language. Map genotypes are represented as low-resolution matrices, which are then converted to higher-resolution maps through a stochastic process involving cellular automata. Map phenotypes are evaluated using a set of heuristics based on the gameplay requirements of Dune 2.

Towards procedural strategy game generation: evolving complementary unit types

The Strategy Game Description Game Language (SGDL) is intended to become a complete description of all aspects of strategy games, including rules, parameters, scenarios, maps, and unit types. One of the main envisioned uses of SGDL, in combination with an evolutionary algorithm and appropriate fitness functions, is to allow the generation of complete new strategy games or variations of old ones. This paper presents a first version of SGDL, capable of describing unit types and their properties, together with plans for how it will be extended to other sub-domains of strategy games. As a proof of the viability of the idea and implementation, an experiment is presented where unit types are evolved so as to generate complementary properties. A fitness function based on Monte Carlo simulation of gameplay is devised to test complementarity.

Modelling and evaluation of complex scenarios with the Strategy Game Description Language

The Strategy Game Description Game Language (SGDL) is intended to become a complete description of all aspects of strategy games, including rules, parameters, scenarios, maps, and unit types. Our aim is to be able to model a wide variety of strategy games, simple ones as well as complex commercially available titles. In our previous work [1] we introduced the basic concepts of modelling game rules in a tree structure and evaluating them through simulated playthrough. In this paper we present some additions to the language and discuss and compare three methods to evaluate the quality of a set of game rules in two different scenarios. We find that the proposed evaluation measures are complementary, and depend on the artificial agent used.

Using genetically optimized artificial intelligence to improve gameplaying fun for strategical games

Fun in computer games depends on many factors. While some factors like uniqueness and humor can only be measured by human subjects, in a strategical game, the rule system is an important and measurable factor. Classics like chess and GO have a millennia-old story of success, based on clever rule design. They only have a few rules, are relatively easy to understand, but still they have myriads of possibilities. Testing the deepness of a rule-set is very hard, especially for a rule system as complex as in a classic strategic computer game. It is necessary, though, to ensure prolonged gaming fun. In our approach, we use artificial intelligence (AI) to simulate hours of beta-testing the given rules, tweaking the rules to provide more game-playing fun and deepness. To avoid making the AI a mirror of its programmers gaming preferences, we not only evolved the AI with a genetic algorithm, but also used three fundamentally different AI paradigms to find boring loopholes, inefficient game mechanisms and, last but not least, complex erroneous behavior.

Online Evolution for Multi-action Adversarial Games

We present Online Evolution, a novel method for playing turn-based multi-action adversarial games. Such games, which include most strategy games, have extremely high branching factors due to each turn having multiple actions. In Online Evolution, an evolutionary algorithm is used to evolve the combination of atomic actions that make up a single move, with a state evaluation function used for fitness. We implement Online Evolution for the turn-based multi-action game Hero Academy and compare it with a standard Monte Carlo Tree Search implementation as well as two types of greedy algorithms. Online Evolution is shown to outperform these methods by a large margin. This shows that evolutionary planning on the level of a single move can be very effective for this sort of problems.

Relevant Information as a formalised approach to evaluate game mechanics

We present a new approach to use adaptive AI and Information Theory to aid the evaluation of game mechanics. Being able to evaluate the core game mechanics early during production is useful to improve the quality of a game, and ultimately, player satisfaction. A current problem with automated game evaluation via AI is to define measurable parameters that correlate to the quality of the game mechanics. We apply the Information Theory based concept of “Relevant Information” to this problem and argue that there is a relation between enjoyment related game-play properties and Relevant Information for an AI playing the game. We also demonstrate, with a simple game implementation, a.) how an adaptive AI can be used to approximate the Relevant Information, b.) how those measurable numerical values relate to certain game design flaws c.) how this knowledge can be used to improve the game.

Playing Multi-Action Adversarial Games : Online Evolutionary Planning versus Tree Search

We address the problem of playing turn-based multiaction adversarial games, which include many strategy games with extremely high branching factors as players take multiple actions each turn. This leads to the breakdown of standard tree search methods, including Monte Carlo tree search (MCTS), as they become unable to reach a sufficient depth in the game tree. In this paper, we introduce online evolutionary planning (OEP) to address this challenge, which searches for combinations of actions to perform during a single turn guided by a fitness function that evaluates the quality of a particular state. We compare OEP to different MCTS variations that constrain the exploration to deal with the high branching factor in the turn-based multiaction game Hero Academy. While the constrained MCTS variations outperform the vanilla MCTS implementation by a large margin, OEP is able to search the space of plans more efficiently than any of the tested tree search methods as it has a relative advantage when the number of actions per turn increases.