Puzzle Level Generation with Answer Set Programming

Swappy is a puzzle game that requires different character tokens to cooperatively navigate a maze to reach their goals. Swappy characters are special in that whenever they are collinear with another character, they may swap places. In practice, generating levels manually may take upwards of 20 hours, and is error prone. By employing Answer Set Programming (ASP), it is possible to generate and constrain level creation such that levels are solvable, meet an aesthetic standard, and follow the rules of the game. Using the grounder/solver tool, Clingo, level creation can be done in a matter of seconds or minutes. The expressive power of rules and constraints allows the developer to more clearly see their game for the abstract ruleset that it is. In this project we explore the use of ASP Prolog to generate artifacts useful for level generation for the puzzle game Swappy - finding succinct and expressive ways to do so compared to traditional programming languages

Deriving Quests from Open World Mechanics

Open world games present players with more freedom than games with linear progression structures. However, without clearly-defined objectives, they often leave players without a sense of purpose. Most of the time, quests and objectives are hand-authored and overlaid atop an open world's mechanics. But what if they could be generated organically from the gameplay itself? The goal of our project was to develop a model of the mechanics in Minecraft that could be used to determine the ideal placement of objectives in an open world setting. We formalized the game logic of Minecraft in terms of logical rules that can be manipulated in two ways: they may be executed to generate graphs representative of the player experience when playing an open world game with little developer direction; and they may be statically analyzed to determine dependency orderings, feedback loops, and bottlenecks. These analyses may then be used to place achievements on gameplay actions algorithmically.Comment: To appear at Foundations of Digital Games (FDG) 201

Generating Levels That Teach Mechanics

The automatic generation of game tutorials is a challenging AI problem. While it is possible to generate annotations and instructions that explain to the player how the game is played, this paper focuses on generating a gameplay experience that introduces the player to a game mechanic. It evolves small levels for the Mario AI Framework that can only be beaten by an agent that knows how to perform specific actions in the game. It uses variations of a perfect A* agent that are limited in various ways, such as not being able to jump high or see enemies, to test how failing to do certain actions can stop the player from beating the level.Comment: 8 pages, 7 figures, PCG Workshop at FDG 2018, 9th International Workshop on Procedural Content Generation (PCG2018

Automated Game Design Learning

While general game playing is an active field of research, the learning of game design has tended to be either a secondary goal of such research or it has been solely the domain of humans. We propose a field of research, Automated Game Design Learning (AGDL), with the direct purpose of learning game designs directly through interaction with games in the mode that most people experience games: via play. We detail existing work that touches the edges of this field, describe current successful projects in AGDL and the theoretical foundations that enable them, point to promising applications enabled by AGDL, and discuss next steps for this exciting area of study. The key moves of AGDL are to use game programs as the ultimate source of truth about their own design, and to make these design properties available to other systems and avenues of inquiry.Comment: 8 pages, 2 figures. Accepted for CIG 201

Refining the paradigm of sketching in AI-based level design

This paper describes computational processes which can simulate how human designers sketch and then iteratively refine their work. The paper uses the concept of a map sketch as an initial, low-resolution and low-fidelity prototype of a game level, and suggests how such map sketches can be refined computationally. Different case studies with map sketches of different genres showcase how refinement can be achieved via increasing the resolution of the game level, increasing the fidelity of the function which evaluates it, or a combination of the two. While these case studies use genetic algorithms to automatically generate levels at different degrees of refinement, the general method described in this paper can be used with most procedural generation methods, as well as for AI-assisted design alongside a human creator.The research was supported, in part, by the FP7 ICT projects C2Learn (project no: 318480) and ILearnRW (project no: 318803), and by the FP7 Marie Curie CIG project Auto- GameDesign (project no: 630665).peer-reviewe

Automatic Generation of Alternative Starting Positions for Simple Traditional Board Games

Simple board games, like Tic-Tac-Toe and CONNECT-4, play an important role not only in the development of mathematical and logical skills, but also in the emotional and social development. In this paper, we address the problem of generating targeted starting positions for such games. This can facilitate new approaches for bringing novice players to mastery, and also leads to discovery of interesting game variants. We present an approach that generates starting states of varying hardness levels for player~$1$ in a two-player board game, given rules of the board game, the desired number of steps required for player~$1$ to win, and the expertise levels of the two players. Our approach leverages symbolic methods and iterative simulation to efficiently search the extremely large state space. We present experimental results that include discovery of states of varying hardness levels for several simple grid-based board games. The presence of such states for standard game variants like $4 \times 4$ Tic-Tac-Toe opens up new games to be played that have never been played as the default start state is heavily biased.Comment: A conference version of the paper will appear in AAAI 201

Mech-Elites : illuminating the mechanic space of GVG-AI

This paper introduces a fully automatic method of mechanic illumination for general video game level generation. Using the Constrained MAP-Elites algorithm and the GVG-AI framework, this system generates the simplest tile based levels that contain specific sets of game mechanics and also satisfy playability constraints. We apply this method to illuminate the mechanic space for four different games in GVG-AI: Zelda, Solarfox, Plants, and RealPortals. With this system, we can generate playable levels that contain different combinations of most of the possible mechanics. These levels can later be used to populate game tutorials that teach players how to use the mechanics of the game.peer-reviewe