Evolving missions to create game spaces

This paper describes a search-based generative method which creates game levels by evolving the intended sequence of player actions rather than their spatial layout. The proposed approach evolves graphs where nodes representing player actions are linked to form one or more ways in which a mission can be completed. Initially simple graphs containing the mission’s starting and ending nodes are evolved via mutation operators which expand and prune the graph topology. Evolution is guided by several objective functions which capture game design patterns such as exploration or balance; experiments in this paper explore how these objective functions and their combinations affect the quality and diversity of the evolved mission graphs.peer-reviewe

Progressive Content Generation Based on Cyclic Graph for Generate Dungeon

Dungeon is level in game consisting collection of rooms and doors with obstacles inside. To make good level, takes a lot of time. With Procedural Content Generation (PCG), dungeons can be created automatically. One of the approaches in PCG to create levels is progressive. Progressive approach produces timeline as representation of the interactions in the game. Timeline representation that is in the form of one straight line is good for endless runner, but for dungeon, the levels are linear. In this research, the timeline is changed to cyclic graph. Cyclic graph is formed using graph grammar algorithm. This research aims to build dungeon that has not linear and minimal dead ends. To eliminate linearity in dungeons, branching in dungeons needs to be formed. The steps carried out in this research are designing graph grammar rules, generating population of graphs, evaluating graphs with fitness values, and building dungeons. Four functions are used to determine the fitness value: shortest vertices, average duration, replayability, and variation. Dungeons produced with progressive approach manage to minimize linearity in dungeons. Dungeon formation is very dependent on the rule grammar that forms it. With the evaluation process, linear dungeons resulting from grammar rules can be minimized

Modifikasi Peta Permainan Bertema Dungeon Crawler Menggunakan Algoritma Genetika Dengan GMS2

Gim atau permainan digital ( Video Game ) memiliki berbagai macam aspek yang mempengaruhi didalamnya, aspek-aspek ini seperti gameplay, visual, story dan banyak aspek lainnya. Dalam tema permainan tertentu, terkadang sebuah aspek lebih menonjol daripada aspek lainnya. Dungeon Crawler adalah tema gim yang menonjolkan aspek gameplay nya berupa petualangan yang berbeda-beda selama permainan, sebuah kesulitan baru pada sisi pengembang gim ( Developer ) saat hendak mengembangkan sebuah gim Dungeon Crawler. Dikarenakan gim lebih mengutamakan aspek petualangan, developer diharuskan menciptakan banyak area permainan agar pemain tidak bosan, sedangkan bila diharuskan merubah satu-per-satu akan memakan waktu yang lama, untuk itu diperluhkan cara untuk membuat sebuah area berubah-ubah bentuknya. Pada akhir penelitian ini di ajukan sebuah cara melakukan modifikasi peta gim Dungeon Crawler menggunakan algoritma optimisasi, Genetika.Selain aplikasi, rumus yang digunakan juga akan dibuktikan dengan melakukan modifikasi terhadap rumu

Evaluation of Algorithms for Randomizing Key Item Locations in Game Worlds

In the past few years, game randomizers have become increasingly popular. In general, a game randomizer takes some aspect of a game that is usually static and shuffles it somehow. In particular, in this paper we will discuss the type of randomizer that shuffles the locations of items in a game where certain key items are needed to traverse the game world and access some of these locations. Examples of these types of games include series such as The Legend of Zelda and Metroid. In order to accomplish this shuffling in such a way that the player is able to reach the end of the game, some novel algorithms in graph theory must be utilized, where the game world and its item locations are represented as a graph and each edge on the graph has some rule for which items are required to traverse it. In this paper, we define these algorithms formally and evaluate them with different metrics that can guide a developer’s decision about which algorithm works best for their game

Evolving Missions to Create Game Spaces

conference pape