Proceedings, The Eighth AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment Mining Rules from Player Experience and Activity Data Jeremy Gow and Simon Colton Paul Cairns Paul Miller Department of Computing Department of Computer Science Formerly of Rebellion Developments Ltd Imperial College London University of York Oxford OX2 0ES, UK London SW7 2RH, UK York YO10 5GH, UK [email protected] {j.gow, s.colton}@imperial.ac.uk [email protected] Abstract Background Feedback on player experience and behaviour can be Player and player experience modelling from log data has invaluable to game designers, but there is need for spe- been an increasingly well-researched topic over the last few cialised knowledge discovery tools to deal with high years. Approaches have used a wide range of AI techniques volume playtest data. We describe a study with a com- to analyse log and/or experience data. This typically in- mercial third-person shooter, in which integrated player volves classifying players according to an existing model of activity and experience data was captured and mined personality or affect. For example, online summary statistics for design-relevant knowledge. We demonstrate that as- on player behaviour in World of Warcraft have been used to sociation rule learning and rule templates can be used find relationships with, and then predict, player personality to extract meaningful rules relating player activity and profiles (Yee et al. 2011). In contrast, novel player models experience during combat. We found that the number, type and quality of rules varies between experiences, have been generated from game data using unsupervised ap- and is affected by feature distributions. Further work is proaches, including applying self-organising maps to high required on rule selection and evaluation. volume summary statistics from Tomb Raider: Underworld (Drachen, Canossa, and Yannakakis 2009), and identifying player style traits in Rogue Trooper with linear discriminant Introduction analysis (Gow et al. 2012). One direction particularly relevant to our work is using Data analytics has become increasingly popular in the games game data to model and predict player experience. For in- industry in recent years, with high volume log data collec- stance, using a clone of Super Mario Bros, Pedersen et al. tion supporting a range of data-centred design approaches. (2009) trained a neural network to predict the player expe- This presents opportunities to combine game data with mea- riences of fun, challenge and frustration based on level con- surements of player personality and experience, to produce tent. In the educational domain, dynamic Bayesian networks deeper insights into game design (Yee et al. 2011) and allow have been used estimate student affect in a simple factori- greater personalisation of game content (Yannakakis and To- sation game (Conati and Maclaren 2005). Once experience gelius 2011). However, the development of specialised tools can be predicted reasonably accurately from known data, it and techniques to support data-centred designers lags behind becomes possible to generate or adapt content to induce cer- our ability to collect mountains of data. tain experiences, e.g. Shaker et al. (2010) used experience In this paper, we describe a novel approach to generat- prediction for Super Mario to automatically generate level ing design-relevant knowledge from integrated game log and designs. For a more detailed overview of these areas, see player feedback data. Experience rules describe the condi- Yannakakis & Togelius (2011). tions under which specific experiences were reported. Us- Association rule learning was originally developed to ing a simple categorisation of features, we define experience analyse associations between items in supermarket transac- rule templates corresponding to various aspects of game tions (Agrawal, Imielinski, and Swami 1993). Given a set design: the design of level content, the design of adaptive of items, a transaction database describes a list of observed mechanisms, and reflection on connections between player itemsets. An association rule A ⇒ B, for disjoint itemsets experiences. We present a study of 24 players of the com- A and B, is a statement about the transactions: whenever mercial third-person shooter Rogue Trooper, in which de- a transaction contains the items in A, it also contains the tailed experience and activity data was captured, and demon- items in B. Agrawal and colleagues originally introduced strate that our templates and association rule mining can be the support-confidence framework: the support for an item- used to generate meaningful and design-relevant experience set is the probability a transaction contains it, and the confi- rules. We discuss lessons learned and suggest some direc- dence of a rule is then p(B|A)=p(AB)/p(A). Rule mining tions for further work. algorithms such as FP-Growth (Han, Pei, and Yin 2000) can Copyright c 2012, Association for the Advancement of Artificial generate rules according to predefined minimum support and Intelligence (www.aaai.org). All rights reserved. confidence constraints. A range of alternative rule metrics 148 Rule type Template Contextual rules These rules describe the context of an ex- + + General All ⇒ All perience: those experiences observed at the same time as + Class All ⇒ All the consequent experience. The premise contains at least Experience All+ ⇒ PX one experience (PX), along with any other features. These Contextual {IN,CN,OB}∗,PX+ ⇒ PX rules might help a designer understand connections be- Observable {IN,CN,OB}+ ⇒ PX tween distinct experiences in various gaming contexts. Adaptative {IN,CN}∗,OB+ ⇒ PX + Adaptive rules These capture the directly observable sit- Content {IN,CN} ⇒ PX uation associated with an experience. The premise must Dynamic content IN∗,CN+ ⇒ PX + contain an observable feature (OB), along with other ob- Static content IN ⇒ PX servable and controllable features. These rules could be used in the design of adaptive mechanisms that monitor Table 1: Experience rule templates. The set of all features player experience for an episode and adjust the content All = IN ∪ CN ∪ OB ∪ PX. of upcoming episodes accordingly. Indeed, a rule-based adaptive system could use the rules directly — a scenario we hope to explore in future work. have been researched (Geng and Hamilton 2006), e.g. lift p(B|A)/p(A), conviction p(A)p(¬B)/p(A¬B) and lever- Dynamic content rules These associate controllable fea- age p(B|A) − p(A)p(B). Association rules are a conceptu- tures of the episode activity with a specific experience. ally simple and well-researched area of data mining, with The premise contains at least one controllable feature several open source implementations available, e.g. Weka (CN) and other controllable features or initial conditions. (Hall et al. 2009), presenting a very low barrier to entry for They could be used to design dynamic game content game designers. aimed at inducing specific player experiences, e.g. the control of NPCs. Experience rule templates Static content rules These describe how the initial condi- Our approach assumes the activity and experience data is tions of an episode can impact on player experience. The structured as a set of episodes, each of which corresponds premise contain features describing the player’s back- to a period of gameplay. Each episode has an arbitrary num- ground (PP) and the initial episode configuration (IN). ber of defined features which we discretise into nominal at- They could be used to reflect on how different types of tributes, giving us a list of episodes (transactions), each de- game content affect different types of player. fined by a set of attribute/value pairs (itemsets) suitable for Collectively, we refer to these as CADS rules. association rule learning. In this paper, the episodes corre- spond to individual combat between the player and a group Data capture of NPCs (non-player characters), but they could represent To explore the generation and use of CADS experience any arbitrary period of gaming activity, e.g. a puzzle, a level, rules, we conducted a study to capture activity and expe- or a month of play. rience data for combat episodes in the commercial third- In order to distinguish rules that might be of interest to person shooter Rogue Trooper (Eidos/Rebellion). An instru- designers, we first categorise the episode features: mented version of the game was developed which, every 0.2 Player Profile (PP) Any information known about the seconds, logged position, orientation and state data for the player, e.g. genre preferences. PC (Player Character) and all NPCs within a given radius, CD along with in-game events such as damage or item use. Combat Design ( ) Features which are predetermined An initial study, in which 10 Rogue Trooper players by the episode content, as determined by the game de- recorded post-game commentaries (Gow et al. 2010), identi- signer, e.g. the initial NPC health and relative positions. fied 9 dimensions of experience commonly associated with Initial (IN = PP ∪ CD) Initial conditions of an episode, player activity. A 9-item questionnaire was then devised to i.e. predetermined features of the player and content. elicit ratings for these experiences, with each item present- Controllable (CN) Features of the game play during the ing a pair of opposing statements. The respondent could episode that can be manipulated by the designers, e.g. how slightly agree, agree or strongly agree with one or neither much NPCs fire. statement, providing a rating for that experience on a 7 point scale (+3to−3). Observable (OB) Features which describe the interaction The rated experiences were: Aware (I was fully aware of between player and content, which cannot be controlled, the situation / I didn’t know what was happening); Care (I but which can be computed directly from the game log. was careful / I jumped straight in); Challenge (The enemy Player Experience (PX) Measurements of player experi- were a challenge / The enemy were easily defeated); Danger ence for the episode (see ”Data Capture” below).