Session:「Explaining Players」

Dynamic Demographics: Lessons from a Large-Scale Census of Performative Possibilities in Games

論文URL: http://dl.acm.org/citation.cfm?doid=3173574.3173667

論文アブストラクト: While much popular discussion of representation in games exists, there is very little rigorously collected data available from which to draw direct conclusions. In this study, we set out to address this gap by performing a census of playable characters across a large sample of contemporary games. We gathered data from 200 games including independently published ("indie") games and so-called "AAA" titles from large publishers. While our initial analysis yielded some insight into the landscape of playable characters, it also highlighted the contingent, negotiated, and interpretive nature of representation in games. This led to additional analysis that emphasized the ways in which this negotiation manifests in research in the methods and metrics used to quantify representation. We argue that researchers studying representation in games need to treat it as a possibility space for a multitude of potential interpretations rather than a singular, measurable, phenomenon.

日本語のまとめ:

この論文ではあらゆるゲームの中のプレイアブルキャラクターのデザインについて調査をし、これがゲームの表現や解釈、背景などとどう関係性があるのかを考察しています。

Let Me Be Implicit: Using Motive Disposition Theory to Predict and Explain Behaviour in Digital Games

論文URL: http://dl.acm.org/citation.cfm?doid=3173574.3173764

論文アブストラクト: We introduce explicit and implicit motives (i.e., achievement, affiliation, power, autonomy) into player experience research and situate them in existing theories of player motivation, personality, playstyle, and experience. Additionally, we conducted an experiment with 109 players in a social play situation and show that: 1. As expected, there are several correlations of playstyle, personality, and motivation with explicit motives, but few with implicit motives; 2. The implicit affiliation motive predicts in-game social behaviour; and 3. The implicit affiliation motive adds significant variance to explain regression models of in-game social behaviours even when we control for social aspects of personality, the explicit affiliation motive, self-esteem, and social player traits. Our results support that implicit motives explain additional variance because they access needs that are experienced affectively and pre-consciously, and not through cognitive interpretation necessary for explicit expression and communication, as is the case in any approaches that use self-report.

日本語のまとめ:

オンラインゲームの中でMotive Disposition Theoryがプレイヤーのプレイスタイルやパーソナリティ、モチベーションとどう関係があるのかを調査、検討しています。

What Moves Players?: Visual Data Exploration of Twitter and Gameplay Data

論文URL: http://dl.acm.org/citation.cfm?doid=3173574.3174134

論文アブストラクト: In recent years, microblogging platforms have not only become an important communication channel for the game industry to generate and uphold audience interest but also a rich resource for gauging player opinion. In this paper we use data gathered from Twitter to examine which topics matter to players and to identify influential members of a game's community. By triangulating in-game data with Twitter activity we explore how tweets can provide contextual information for understanding fluctuations in in-game activity. To facilitate analysis of the data we introduce a visual data exploration tool and use it to analyze tweets related to the game Destiny. In total, we collected over one million tweets from about 250,000 users over a 14-month period and gameplay data from roughly 3,500 players over a six-month period.

日本語のまとめ:

この論文では、ツイッターの呟きからゲームをプレイしている人の意見や感情、動機などを分析するためのヴィジュアライズツールを導入し、あるゲームに関連するツイートを分析しました。

How the Experts Do It: Assessing and Explaining Agent Behaviors in Real-Time Strategy Games

論文URL: http://dl.acm.org/citation.cfm?doid=3173574.3174136

論文アブストラクト: How should an AI-based explanation system explain an agent's complex behavior to ordinary end users who have no background in AI? Answering this question is an active research area, for if an AI-based explanation system could effectively explain intelligent agents' behavior, it could enable the end users to understand, assess, and appropriately trust (or distrust) the agents attempting to help them. To provide insights into this question, we turned to human expert explainers in the real-time strategy domain --"shoutcasters"-- to understand (1) how they foraged in an evolving strategy game in real time, (2) how they assessed the players' behaviors, and (3) how they constructed pertinent and timely explanations out of their insights and delivered them to their audience. The results provided insights into shoutcasters' foraging strategies for gleaning information necessary to assess and explain the players; a characterization of the types of implicit questions shoutcasters answered; and implications for creating explanations by using the patterns and abstraction levels these human experts revealed.

日本語のまとめ:

本論文では昨今話題となっているe-Sportsの大会に置いて実況を行なっているキャスターがどのように実況しているのかを分析し、それをモデル化、AIベースの解説者の開発を目的としています。