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Outline

Title
Abstract
Figures
Introduction
Relation to Our Own Previous Work
Data Collection
Content Data
Gameplay Data
Reported Player Experience Data
Experimental Protocol
Analysis
Discussion
References

Crowd-Sourcing the Aesthetics of Platform Games

Profile image of Noor ShakerNoor Shaker

Abstract

What are the aesthetics of platform games and what makes a platform level engaging, challenging and/or frustrating? We attempt to answer such questions through mining a large-set of crowd-sourced gameplay data of a clone of the classic platform game Super Mario Bros. The data consists of 40 short game levels that differ along six key level design parameters. Collectively, these levels are played 1560 times over the Internet and the perceived experience is annotated by experiment participants via self-reported ranking (pairwise preferences). Given the wealth of this crowd-sourced data, as all details about players' in-game behaviour are logged, the problem becomes one of extracting meaningful numerical features at the appropriate level of abstraction for the construction of generic computational models of player experience and, thereby, game aesthetics. We explore dissimilar types of features, including direct measurements of event and item frequencies, and features constructed through frequent sequence mining and go through an in-depth analysis of the interrelationship between level content, player's behavioural patterns and reported experience. Furthermore, the fusion of the extracted features allows us to predict reported player experience with a high accuracy even from short game segments. In addition to advancing our insight on the factors that contribute to platform game aesthetics, the results are useful for the personalisation of game experience via automatic game adaptation.

Figures (6)
Fig. 1. Snapshot from Infinite Mario Bros, showing Mario standing on horizontally placed blocks surrounded by different types of enemies.
Fig. 1. Snapshot from Infinite Mario Bros, showing Mario standing on horizontally placed blocks surrounded by different types of enemies.
Fig. 2. Enemies placement using different probabilities: high probability is given to placement around horizontal boxes, Pp (a), around gaps, Pg (b), and to random placement, P;. (c).
Fig. 2. Enemies placement using different probabilities: high probability is given to placement around horizontal boxes, Pp (a), around gaps, Pg (b), and to random placement, P;. (c).
Fig. 4. Snapshot from a level and the corresponding platform structure sequence representation, P (a), and enemies and items sequence represen- tation, I (b).
Fig. 4. Snapshot from a level and the corresponding platform structure sequence representation, P (a), and enemies and items sequence represen- tation, I (b).
ml Sad ON, BEST MLP TOPOLOGIES AND CORRESPONDING PERFORMANCE ON DIRECT AND SEQUENTIAL FEATURES. THE PERFORMANCE OF MLP MODELS BUILT ON THE SUBSET OF SELECTED FEATURES, M LP; IS COMPARED AGAINST THE MODELS BUILT ON SELECTED AND FORCED CONTROLLABLE FEATURES, ML P.. THE TOPOLOGIES ARE PRESENTED IN THE FORM: NUMBER OF INPUTS-NUMBER OF NEURONS IN THE FIRST HIDDEN LAYER-NUMBER OF NEURONS IN THE SECOND HIDDEN LAYER. these two types together, simple jumping actions, {}, can be explained by changes in platform height and placement of items; moving right followed by jumping and standing still patterns ((m, ff, 5) and (»)({}",S)) mostly relates to ‘ . : oo the behaviour of overcoming enemies @ a) , © ); the more complex navigation patterns that has been selected, such as (R™,R™,R”*) that defines the behaviour of pressing a combination of buttons at the same time within a very small window time, suggest the existence of a gap that requires speeding up followed by jumping while the moving right and the speed button are still pressed ((™%,7, )). Note that the two patterns ((m, {}, S) and (»)({}",S)) can also be the result of overcoming a gap, which in that case reflect a beginner player playing style. On the contrary the pattern
ml Sad ON, BEST MLP TOPOLOGIES AND CORRESPONDING PERFORMANCE ON DIRECT AND SEQUENTIAL FEATURES. THE PERFORMANCE OF MLP MODELS BUILT ON THE SUBSET OF SELECTED FEATURES, M LP; IS COMPARED AGAINST THE MODELS BUILT ON SELECTED AND FORCED CONTROLLABLE FEATURES, ML P.. THE TOPOLOGIES ARE PRESENTED IN THE FORM: NUMBER OF INPUTS-NUMBER OF NEURONS IN THE FIRST HIDDEN LAYER-NUMBER OF NEURONS IN THE SECOND HIDDEN LAYER. these two types together, simple jumping actions, {}, can be explained by changes in platform height and placement of items; moving right followed by jumping and standing still patterns ((m, ff, 5) and (»)({}",S)) mostly relates to ‘ . : oo the behaviour of overcoming enemies @ a) , © ); the more complex navigation patterns that has been selected, such as (R™,R™,R”*) that defines the behaviour of pressing a combination of buttons at the same time within a very small window time, suggest the existence of a gap that requires speeding up followed by jumping while the moving right and the speed button are still pressed ((™%,7, )). Note that the two patterns ((m, {}, S) and (»)({}",S)) can also be the result of overcoming a gap, which in that case reflect a beginner player playing style. On the contrary the pattern
[HE SUBSET OF DIRECT FEATURES USED FOR PREDICTING PLAYERS’ REPORTED EXPERIENCE AND THE CORRESPONDING MODELS’ TOPOLOGIES AND PERFORMANCE AS PRESENTED IN [19]
[HE SUBSET OF DIRECT FEATURES USED FOR PREDICTING PLAYERS’ REPORTED EXPERIENCE AND THE CORRESPONDING MODELS’ TOPOLOGIES AND PERFORMANCE AS PRESENTED IN [19]
THE PERFORMANCE OF THE MODELS OF [19] ON THE NEW DATASET (Pota/new) COMPARED TO THE PERFORMANCE OF THE NEW MODELS ON THE DATASET OF [19] (Prew/ola)
THE PERFORMANCE OF THE MODELS OF [19] ON THE NEW DATASET (Pota/new) COMPARED TO THE PERFORMANCE OF THE NEW MODELS ON THE DATASET OF [19] (Prew/ola)

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A recent trend within computational intelligence and games research is to investigate how to affect video game players' in-game experience by designing and/or modifying aspects of game content. Analysing the relationship between game content, player behaviour and self-reported affective states constitutes an important step towards understanding game experience and constructing effective game adaptation mechanisms. This papers reports on further refinement of a method to understand this relationship by analysing data collected from players, building models that predict player experience and analysing what features of game and player data predict player affect best. We analyse data from players playing 780 pairs of short game sessions of the platform game Super Mario Bros, investigate the impact of the session size and what part of the level that has the major affect on player experience. Several types of features are explored, including item frequencies and patterns extracted through frequent sequence mining.

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One promising avenue towards increasing player entertainment for individual game players is to tailor player experience in real-time via automatic game content generation. Modeling the relationship between game content and player preferences or affective states is an important step towards this type of game personalization. In this paper we analyse the relationship between level design parameters of platform games and player experience. We introduce a method to extract the most useful information about game content from short game sessions by investigating the size of game session that yields the highest accuracy in predicting players' preferences, and by defining the smallest game session size for which the model can still predict reported emotion with acceptable accuracy. Neuroevolutionary preference learning is used to approximate the function from game content to reported emotional preferences. The experiments are based on a modified version of the classic Super Mario Bros game. We investigate two types of features extracted from game levels; statistical level design parameters and extracted frequent sequences of level elements. Results indicate that decreasing the size of the feature window lowers prediction accuracy, and that the models built on selected features derived from the whole set of extracted features (combining the two types of features) outperforms other models constructed on partial information about game content.

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Estimating affective and cognitive states in conditions of rich human-computer interaction, such as in games, is a field of growing academic and commercial interest. Entertainment and serious games can benefit from recent advances in the field as, having access to predictors of the current state of the player (or learner) can provide useful information for feeding adaptation mechanisms that aim to maximize engagement or learning effects. In this paper, we introduce a large data corpus derived from 58 participants that play the popular Super Mario Bros platform game and attempt to create accurate models of player experience for this game genre. Within the view of the current research, features extracted both from player gameplay behavior and game levels, and player visual characteristics have been used as potential indicators of reported affect expressed as pairwise preferences between different game sessions. Using neuroevolutionary preference learning and automatic feature selection, highly accurate models of reported engagement, frustration, and challenge are constructed (model accuracies reach 91%, 92%, and 88% for engagement, frustration, and challenge, respectively). As a step further, the derived player experience models can be used to personalize the game level to desired levels of engagement, frustration, and challenge as game content is mapped to player experience through the behavioral and expressivity patterns of each player.

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