Esports Analytics Through Encounter Detection

Esports is computer games played in a competitive environment, and analytics in this domain is focused on player and team behavior. Multiplayer Online Battle Arena (MOBA) games are among the most played digital games in the world. In these es, teams of players fight against each other in enclosed arena environs, with a complex gameplay focused on tactical combat. Here we present a technique for segmenting matches into spatio‐temporally defined components referred to as encounters, enabling performance analysis. We apply encounter‐based analysis to match data from the popular esport game DOTA, and present win probability predictions based on encounters. Finally,metrics for evaluating team performance during match runtime are proposed

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

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

We address the problem of playing turn-based multi-action 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 multi-action 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

Procedural Generation of 3D Caves for Games on the GPU

Procedural Content Generation in Games (PCG) is a thriv- ing field of research and application. Recent presented ex- amples range from levels, stories and race tracks to complete rulesets for games. However, there is not much research to date on procedural 3D modeling of caves, and similar en- closed natural spaces. In this paper, we present a modular pipeline to procedurally generate underground caves in real- time, to be used as part of larger landscapes in game worlds. We propose a three step approach, which can be fully im- plemented using General-Purpose Computing on Graphics Processing (GPGPU) technology: 1) an L-System to em- ulate the expanded cracks and passages which form cave structures in nature, 2) a noise-perturbed metaball approach for virtual 3D carving, and 3) a rendering component for isosurface extraction of the modeled voxel data, and fur- ther mesh enhancement through shader programming. We demonstrate how the interaction between these components produce results comparable to real world caves, and show that the solution is viable for video game environments. For this, we present the findings of a user study we conducted among indie-game developers and players, using our results

Towards the automatic generation of card games through Grammar-Guided Genetic Programming

We demonstrate generating complete and playable card games using evolutionary algorithms. Card games are represented in a previously devised card game description language, a context-free grammar. The syntax of this language allows us to use grammar-guided genetic programming. Candidate card games are evaluated through a cascading evaluation function, a multi-step process where games with undesired properties are progressively weeded out. Three representa- tive examples of generated games are analysed. We observed that these games are reasonably balanced and have skill ele- ments, they are not yet entertaining for human players. The particular shortcomings of the examples are discussed in re- gard to the generative process to be able to generate quality game

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.peer-reviewe

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.peer-reviewe