Search-Based Procedural Content Generation: A Taxonomy and Survey

Togelius, Julian; Yannakakis, Georgios N.; Stanley, Kenneth O.; Browne, Cameron

doi:10.1109/tciaig.2011.2148116

Cited by 519 publications

(426 citation statements)

References 63 publications

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“…The existing system SMB used to generate the whole level segments by segments either based on the rhythm of the player interaction [10], with a partial human interaction [7], [8], the other technique is by using evolutionary algorithm where online(during execution or active play sessions) monitoring is done to check the player interaction and the algorithm adaptive to the player's difficulty handling [12], [13] or used the Long Short-Term Memory recurrent neural networks (LSTMs) [11] for the purpose of generating levels trained from Super Mario Bros. The quality assurance is the main focus in the content generation, this can be generally categorized into two main techniques in developing such mechanism [1], [6], [15]: deductive and inductive. The problem with using the deductive technique is that the developers will need all perquisites of the whole content space before generation of the segments with proper rule and this becomes especially complicated when the content space is too large such implementation may not be impossible but will not lead to proper rule/training sets and may often create problems in Procedural Content Generation (PCG).…”

Section: A Overviewmentioning

confidence: 99%

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Review on Dynamic Difficulty Adjustment (Dad) In Plat former Games

Desai¹

2017

IJRASET

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There are many challenges in procedural content generation (PCG), maintaining the quality of content with all the diversity, without any flaws and maintaining the balance in the level of difficulty by dynamic difficulty adjustment (DDA) of the game content in real time based on active inputs are some of the primary issues when concerned with machine generated adaptive content. This subject investigates the efficiency in the use of Procedural content generation by combining the learning based and rule based approach. The research is motivated by the PCG framework based on the learning approach, with the novel approach to address the two primary issues in Super Mario Bros (SMB). To deal with the content quality issue the combination of rule based and learning based techniques is used, which will help to produce the game segments with good quality, also called constructive primitives (CPs) which are all the quality segments in a game level. A DDA algorithm is proposed in the research that will control a CP-based level generator and will adjust the difficulty of levels rapidly based on players' real-time game play experience. The use of model based approach over model free approach will be the main focus of this research where the sampling will be done to minimize content into their respective datasets using the simple random sampling approach without replacement. In a model-free approach, player model is mainly determined by the players' gameplay data, e.g., controllable parameters and playlog in the game, and the feedback from this control parameters, e.g., state changes the affect the "fun" content of the game, while model based approach have the player model which relies on derived values of psychological emotion theories to mainly balance the game difficulty [14].As far as it is known most of the current implementation on SMB game falls under the model free approach and the model based approach is yet to be explored and not many have efficiently implemented the model based approach. The real-time model based approach for Dynamic Difficulty Adjustment (DDA) within the generation of a level is only implemented in the recent work of P. Shi and K. Chen [5], [6] for SMB level adaptation research to date. Learning-Based Procedural Content Generation (LBPCG) framework [15]is the main motivation in our research. The quality evaluation function of the content of the game will be determined from the levels mentioned by the developers. Generating short game segments of good quality will be determined as constructive primitives (CP) [5], [6] and modified Infinite Mario Bros will be used as a test game [3].

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Section: A Overviewmentioning

confidence: 99%

“…Procedural content generation is used to generate game content dynamically [1] either fully or partially, could be model based or model free and can be based on online content generation i.e. real-time time content generation or offline content generation i.e.…”

Section: Introductionmentioning

confidence: 99%

Review on Dynamic Difficulty Adjustment (Dad) In Plat former Games

Desai¹

2017

IJRASET

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“…[2, [4][5][6][7]). These, however, aim for an optimum appearance or a slightly varied play experience, rather than the highly diverse challenges that concern us here.…”

Section: Related Workmentioning

confidence: 99%

“…Martin and Hughes [2] and in the video games field, where it is called Procedural Content Generation (PCG) e.g. Togelius et al [4,5]. PCG is often used in situations where the manual creation of content would be prohibitively time-consuming or expensive [6].…”

Section: Introductionmentioning

confidence: 99%

Testing Autonomous Robot Control Software Using Procedural Content Generation

Arnold

Alexander

2013

Lecture Notes in Computer Science

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Abstract. We present a novel approach for reducing manual effort when testing autonomous robot control algorithms. We use procedural content generation, as developed for the film and video game industries, to create a diverse range of test situations. We execute these in the Player/Stage robot simulator and automatically rate them for their safety significance using an event-based scoring system. Situations exhibiting dangerous behaviour will score highly, and are thus flagged for the attention of a safety engineer. This process removes the time-consuming tasks of hand-crafting and monitoring situations while testing an autonomous robot control algorithm. We present a case study of the proposed approach -we generated 500 randomised situations, and our prototype tool simulated and rated them. We have analysed the three highest rated situations in depth, and this analysis revealed weaknesses in the smoothed nearnessdiagram control algorithm.

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“…Generating dungeons has also been a fertile research topic as summarized by [1]; algorithmic approaches using constraints [2], grammars [3] and genetic algorithms [4] have been successfully applied to this task. This paper introduces a method where procedural personas act as critics in a searchbased procedural content generation (SBPCG) framework [5]. Procedural personas are artificial agents which represent archetypical player behaviors (e.g.…”

Section: Introductionmentioning

confidence: 99%

Procedural Personas as Critics for Dungeon Generation

Liapis

Holmgård

Yannakakis

et al. 2015

Applications of Evolutionary Computation

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Abstract. This paper introduces a constrained optimization method which uses procedural personas to evaluate the playability and quality of evolved dungeon levels. Procedural personas represent archetypical player behaviors, and their controllers have been evolved to maximize a specific utility which drives their decisions. A "baseline" persona evaluates whether a level is playable by testing if it can survive in a worst-case scenario of the playthrough. On the other hand, a Monster Killer persona or a Treasure Collector persona evaluates playable levels based on how many monsters it can kill or how many treasures it can collect, respectively. Results show that the implemented two-population genetic algorithm discovers playable levels quickly and reliably, while the different personas affect the layout, difficulty level and tactical depth of the generated dungeons.

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Search-Based Procedural Content Generation: A Taxonomy and Survey

Cited by 519 publications

References 63 publications

Review on Dynamic Difficulty Adjustment (Dad) In Plat former Games

Review on Dynamic Difficulty Adjustment (Dad) In Plat former Games

Testing Autonomous Robot Control Software Using Procedural Content Generation

Procedural Personas as Critics for Dungeon Generation

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