Evolving missions to create game spaces

Karavolos, Daniel; Liapis, Antonios; Yannakakis, Georgios N.

doi:10.1109/cig.2016.7860396

Cited by 13 publications

(8 citation statements)

References 11 publications

(17 reference statements)

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“…Karavolos et al [3] also abstract a game world as a graph, where nodes represent rooms, edges represent doorways between rooms, and a the value on a node represents what type of room it is in order to model dungeons in games. However, this work's focus is on generating graphs to make interesting dungeons to explore without consideration for locked edges and reachability.…”

Section: Related Workmentioning

confidence: 99%

Evaluation of Algorithms for Randomizing Key Item Locations in Game Worlds

et al. 2021

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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.INDEX TERMS Game analysis, game randomization, graph theory.

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Section: Related Workmentioning

confidence: 99%

Evaluation of Algorithms for Randomizing Key Item Locations in Game Worlds

et al. 2021

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“…Similar to the evaluation of dungeons based on so-called mission graphs that express the type and order of challenge in [69], all the evaluation methods for narrative in games we found [43,55,77] are based on a kindred representation of the temporal order of events. While a crowdsourced approach that includes implicit feedback is proposed in [77], [43,55] add information collected from gameplay data instead.…”

Section: A26 Strategy Action and Narrative-based Gamesmentioning

confidence: 99%

Uncertainty Handling in Surrogate Assisted Optimisation of Games

Volz

2019

Künstl Intell

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A detailed illustrative example of how modelling human players using AIs can bias the obtained outcomes based on published data for the game StarCraft II (Blizzard 2010). The example conclusively shows the need for uncertainty handling in simulation-based game-related optimisation.The example is based on an algorithm for StarCraft II winner prediction as published in [VPB18] with my co-authors Mike Preuss and Mathias K. Bonde. The analysis presented in this paper was however not published yet and is based on newly generated results from different datasets. The example also profited from discussion with Boris Naujoks. CHAPTER 1. INTRODUCTION Game-Benchmark for Evolutionary AlgorithmsAn extension of the existing COCO benchmarking framework for numerical black-box optimisation 3 that introduces two new functions suites based on game optimisation problems. The benchmark code, results and further information is freely available 4 . Two workshops around the benchmark were also organised for GECCO 2018 and 2019 in collaboration with Tea Tušar, Boris Naujoks and Pascal Kerschke. The interface for the new functions to the existing framework was implemented by Tea Tušar.The new function suites are based on two previous publications. The first is an optimisation problem for cards in TopTrumps that was introduced to demonstrate the feasibility of automatic game balancing with and without surrogate models in collaboration with Boris Naujoks and Günter Rudolph in [VRN16]. The second function suite is based on a significantly extended version of a procedural content generation technique for Super Mario Bros. levels proposed in [Vol+18] developed with SAPEO Algorithm SAPEO, an algorithm designed for the robust optimisation of games and similar complex simulation-based real-world problems. The algorithm is extensively evaluated theoretically and empirically. Benchmarks on artificial functions do attest SAPEO robust performance, especially for complex functions. However, we were not able to obtain a meaningful interpretation of the algorithm's performance on the game benchmark.Previous versions of SAPEO have been published for both single-and multi-objective optimisation problems in [VRN17a] and [VRN17b] in collaboration with Günter Rudolph and Boris Naujoks 6 . The version of SAPEO proposed in this thesis is however improved based on findings from previous publications and extended by model validation features as suggested by Alma Rahat. The results presented in this thesis are thus novel and not previously published. The theoretical performance assessment was supported by Michael Emmerich in context of a short term scientific mission 7 .

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“…The dungeon is a popular level design archetype found in several popular game genres [5,18]. Dungeons are also popular in PCG research, where different approaches have been presented for generating dungeon levels [8,11,13,19,22]. These works emphasize the importance of considering goals, missions, the narrative or themes, visual style, and gameplay rules when designing levels, therefore they should be taken into consideration when developing a mixed-initiative tool for content generation [12].…”

Section: Dungeon Design In Videogamesmentioning

confidence: 99%

Fostering creativity in the mixed-initiative evolutionary dungeon designer

Alvarez

Dahlskog

Font

et al. 2018

Proceedings of the 13th International Conference on the Foundations of Digital Games

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Mixed-initiative systems highlight the collaboration between humans and computers in fostering the generation of more interesting content in game design. In light of the ever-increasing cost of game development, providing mixed-initiative tools can not only significantly reduce the cost but also encourage more creativity amongst game designers. The Evolutionary Dungeon Designer (EDD) [3] is a mixed-initiative tool with a focus on using evolutionary computation to procedurally generate content that adhere to game design patterns. As part of an ongoing project, feedback from a user study on EDD's capabilities as a mixed-initiative design tool pointed out the need for improvement on the tool's functionalities [4]. In this paper we present a review of the principles of the mixedinitiative model, as well as the existing approaches that implement it. The outcome of this analysis allows us to address the appointed needs for improvement by shaping a new version of EDD that we describe here. Finally, we also present the results from a user study carried out with professional game developers, in order to assess EDD's new functionalities. Results show an overall positive evaluation of the tool's intuitiveness and capabilities for empowering game developers' creative skills during the design process of dungeons for adventure games. They also allow us to identify upcoming challenges pattern-based mixed-initiative tools could benefit from. CCS CONCEPTS • Theory of computation → Evolutionary algorithms; • Applied computing → Computer games; • Software and its engineering → Interactive games;

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Evolving missions to create game spaces

Cited by 13 publications

References 11 publications

Evaluation of Algorithms for Randomizing Key Item Locations in Game Worlds

Evaluation of Algorithms for Randomizing Key Item Locations in Game Worlds

Uncertainty Handling in Surrogate Assisted Optimisation of Games

Fostering creativity in the mixed-initiative evolutionary dungeon designer

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