A Framework for Game Content Generation

By setting the basis of user generated content in games, Yannakakis and Togelius(2015) proposed an approach called Experience-Driven Procedural Content Generation (EDPCG). It consists of specific components, forming a closed loop framework, that satisfy the efficient generation of game content through models of player experience:

• Player Experience Modelling (PEM) – behaviour, skills and challenges of user through the game.

• Content Quality – assessment of the generated content in relation to the user’s experience.

• Content Representation – structures associated with the efficient repres-entation of limitless content.

• Content Generator – the algorithm that is responsible for generating user optimised content according to the player model.

Each of these primary modalities are further split into several general methods and procedures in order for the researchers to be able to specify and compare different frameworks. These are summarised in Tables2.3and2.4.

Pedersen et al. (2010, 2009) modified the classic game Super Mario Bros to allow the personalised level creation. Following the components of the

EDPCGframework, they represented each level using a parameter vector used to represent features of the game (e.g. gaps) [indirect Content Representation].

The model of player experience was based on collecting information from the level content and player’s playing style (i.e. frequency of running, shooting) and associating them – using neural networks – to affective states of the user (fun, challenge, frustration, predictability, anxiety, boredom) [combination of gameplay-based (model-free) and subjective (pairwise preference)PEM]. Through

2.3 user generated adaptive content and training 47

Table 2.3:PEMof theEDPCGframework

free- response ranking Players ask to

put items in order

preference Compare between sessions

Model- basedModel- freeModel- basedModel- freehybridS&OS&GG&O

Hybrid

Combinations of all above methods

Psychophysiology in games Self-report driven cognitive

modelling

Correlation analysis of physiology to game play preferences Hard to analyse. Need to make

strong assumptions but richer information.

Emotional models derived from theoretical frameworks.

Unknown mapping between player's input and emotional state representation via user annotated

data.

Theoretical framework inspired of behavioural analysis

Identify patterns or predict user actions and intentions

forced

Asking players about their playing experience. This can be intrusive to the game or sensitive to players memory limitations. Good guide for

capturing player experience.

Linked to emotions and events that occur during gameplay through

physiological signals.

Objective

Statistical spatio-temporal features of game interaction (player actions and real-time preferences). Least

intrusive and computational efficient method. However there are

strong assumptions made between player experience and gameplay

actions.

Gameplay-based

Contains elements from both approaches

Table 2.4:EDPCGframework components

theory- drivendata- drivenstaticdynamicimplicitexplicitdirectindirect

exhaustive

Need to search over the search space for content that maximises particular aspects of player experience.

Able to identify the amount and frequency of content generation for a particular player. Emphasize on

randomness and lack of controllability.

Not Search Based Fitness of content quality is

evaluated during gameplay via interaction with a player in the

game.

Relation of genotypes (data structures of content generator) to phenotypes (data/processes assessed by

the evaluation function). The representation should have the right dimensionality to avoid "curse of

dimensionality"

Genotype is linearly proportional to the size of phenotype.

Genotype maps nonlinearly to the phenotype.

Collect data on the effect of various examples of content and map via

player experience to evaluation functions

Agent does not change through the game

Agent changes through the game (e.g. learning behaviour)

Observing events that occur during the game either game related (e.g.

content interaction) or user related (e.g. expressions). Data might be

noisy and inaccurate.

Data are collected directly from the player through questionnaires or

verbally. Usually coupled with subjective PEM components. Can

interrupt gameplay.

simulation-basedinteractive

Content Representation

Content Generator direct Extracted features from the

generated content are mapped to a quality value.

An artificial agent evaluates the content via play-through where relevant PEM features are extracted.

Can be executed faster than real-time but more computationally expensive. There is the assumption that the agent plays in a similar way

as a human player.

Designer is guided through a qualitative theory to derive the

mapping Content Quality (Evaluation functions)

2.3 user generated adaptive content and training 49

automatic feature selection certain subset of player data attributes where linked to the affective states.

In an extension of their work (Shaker et al.,2010), the models created were then used to generate and optimise the level for particular known player styles (maximise the predicted fun) [exhaustive search Content Generation].

However, the limitation of their approach was that emotions were inferred through the association of user self-reports combined with game context variables [direct data-driven evaluation function of Content Quality].

2.3.2.1 Procedural Content Generation in Car Racing Games

In racing games the generation of content mainly targets the track’s path.

Pushing the car to the limits and handling tight turns at high speeds engages the users in this “racing” game category. Loiacono et al. (2011) derived an algorithm for generating new tracks in a car simulator (TORCS⁸) using single and multi-objective genetic algorithms. By maximising the entropy of certain criteria (e.g. path curvature distributions along the track, achievable speeds distributions) and under the condition that the track has to be closed, their algorithm fills the path through particular “control” points that the road needs to pass through. Their initial aim was to provide tracks with an adequate amount of challenge and a large degree of diversity across their path.

A further improvement, for embedding a human oriented decision to the algorithm, was proposed byCardamone et al.(2011) where the framework for advancing the algorithm to a next generation of tracks was also influ-enced by human assistance. Subjects voted for each generated track using scoring interfaces (5 Likert-scale or boolean type) that were influencing the algorithm over the next generations of tracks. They showed that there was an improvement of user satisfaction in early generations. However, when the evolved tracks were tested by human subjects, they concluded that the tracks were only appealing to the players with some experience in racing games.

The user-oriented track generation concept, that this thesis is focused on, has been approached before byTogelius et al.(2006,2007);Togelius and Lucas (2006). Their evolutionary algorithm (Cascading Elitism) generated a number of different tracks either by changing the control points of a basic track segment or by constraining their angular position. Then a neural-network based controller (Togelius and Lucas, 2006), that was trained by human driver behaviour, was testing if a generated track is challenging enough

8 The Open Racing Car Simulator Website, [Online]. Available:http://goo.gl/2ExsfF(visited on 16/08/2016)

for a particular driver. Fitness metrics (e.g. varying challenge, fast driving regions) were used to evaluate the suitability of a new track for the controller.

However, the research was focused on the methodology and creativity of the generated tracks instead of their evaluation with human drivers.

2.3.2.2 Adaptive Artificial Intelligence Opponents

Adaptability in games can also be achieved by changing the behaviour of the AI computer-controlled opponents (game AI), according to the skills of the user. An attempt is described by Spronck et al. (2006) where they implemented and evaluated a technique for an adaptive gameAIin a third-person game (Neverwinter Nights) using adaptive dynamic scripting⁹. Their technique was trying to find the best rules so it can become stronger towards the opponent but also provide “difficulty scaling”¹⁰ so it can adapt to the skills of the human user.

An important finding from their research is that game developers are reluctant do adopt adaptive techniques if they don’t meet the computational and functional requirements listed in Table2.5. Also, the inclusion of domain-specific knowledge in the adaptation mechanism (i.e. initial weights) increases significantly the algorithm’s performance.

Table 2.5: Computational and functional requirements of an adaptive gameAI

Computational Functional

1. Speed 1. Clarity

(computationally fast) (interpretable results)

2. Effectiveness 2. Variety

(continuous successful behaviour) (non-predictable behaviours)

3. Robustness 3. Consistency

(to game’s randomness) (in successful learning) 4. Efficiency 4. Scalability

(to the learning technique) (adapt to different skill levels)

9 Dynamic scripting is an online machine-learning technique for game AI. The rules in the script that define the agent’s behaviour are selected from a rule-base with a probability according to an associated weight.

10 Difficulty Scaling is the automatic adaptation of a game to alter the challenge that it poses to the player.

2.4 prediction of user behaviour and system dynamics 51