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Human-cobot Teams

Now that collaborative robots are becoming more widespread in industry, the question arises how we can make them better co-workers and team members. Team members cooperate and collaborate to attain common goals. Consequently they provide and receive information, often non-linguistic, necessary to accomplish the work at hand and coordinate their activities. The cooperative behaviour needed to function as a team also entails that team members have to develop a certain level of trust towards each other. In this paper we argue that for cobots to become trusted, successful co-workers in an industrial setting we need to develop design principles for cobot behaviour to provide legible, that is understandable, information and to generate trust. Furthermore, we are of the opinion that modelling such non-verbal cobot behaviour after animal co-workers may provide useful opportunities, even though additional communication may be needed for optimal collaboration. Marijke Bergman, Elsbeth de Joode, +1 author Janienke Sturm Published in CHIRA 2019 Computer Science

Effects of advertisements and questionnaire interruptions on the player experience

Framework for Modelling Multi-stakeholder City Logistics Domain Using the Agent based Modelling Approach

Efficiency of city logistics activities suffers due to conflicting personal preferences and distributed decision making by multiple city logistics stakeholders. This is exacerbated by interdependency of city logistics activities, decision making with limited information and stakeholders’ preference for personal objectives over system efficiency. Accordingly, the key to understanding the causes of inefficiency in the city logistics domain is understanding the interaction between heterogeneous stakeholders of the system. With the capabilities of representing a system in a natural and flexible way, agent based modelling (ABM) is a promising alternative for the city logistics domain. This research focuses on developing a framework for the successful implementation of the ABM approach for the city logistics domain. The framework includes various elements – a multi-perspective semantic data model (i.e. ontology) and its validation, the development of an agent base model using this ontology, and a validation approach for the agent-based model. Conclusively, the framework shows that a rigorous course can be taken to successfully implement agent based modelling approach for the city logistics domain.

Effects of advertisements and questionnaire interruptions on the player experience

New online stores and digital distribution methods have led to the development of alternative monetization models for video-games, such as free-to-play games with advertisements. Although there are many games using such models, until now the effect on the player experience from such interruptions has not been studied. In this controlled experiment, we requested that participants (N=236) play one of three different versions of a platformer game with: 1) no interruptions, 2) 30-second video advertisements, and 3) a multiple-choice questionnaire. We then evaluated the effects on the player experience. The study shows differences in their experiences, namely in: competence, immersion, annoyance, affects, and the reliability of the questionnaire answers. The contribution of this work is to identify which player experience variables are affected by interruptions, which can be valuable for selecting the business model and guiding the game design process.

Pressure at play

With the increased adoption of real-time objective measurements of player experience, advances have been made in characterising the dynamically changing aspects of the player experience during gameplay itself. A direct coupling to player action, however, is not without challenges. Many physiological responses, for instance, have an inherent delay, and often take some time to return to a baseline, providing challenges of interpretation when analysing rapidly changing gameplay on a micro level of interaction. The development of event-related, or phasic, measurements directly coupled to player actions provides additional insights, for instance through player modelling, but also through the use of behavioural characteristics of the human computer interaction itself. In this study, we focused on the latter, and measured keyboard pressure in a number of different, fast-paced action games. In this particular case, we related specific functional game actions (keyboard presses) to experiential player behaviour. We found keyboard pressure to be higher for avoidance as compared to approach-oriented actions. Additionally, the difference between avoidance and approach keyboard pressure related to levels of arousal. The findings illustrate the application potential of qualifying players’ functional actions at play (navigating in a game) and interpret player experience related to these actions through players’ real world behavioural characteristics like interface pressure.

Player behavioural modelling for video games

Player behavioural modelling has grown from a means to improve the playing strength of computer programs that play classic games (e.g., chess), to a means for impacting the player experience and satisfaction in video games, as well as in cross-domain applications such as interactive storytelling. In this context, player behavioural modelling is concerned with two goals, namely (1) providing an interesting or effective game AI on the basis of player models and (2) creating a basis for game developers to personalise gameplay as a whole, and creating new user-driven game mechanics. In this article, we provide an overview of player behavioural modelling for video games by detailing four distinct approaches, namely (1) modelling player actions, (2) modelling player tactics, (3) modelling player strategies, and (4) player profiling. We conclude the article with an analysis on the applicability of the approaches for the domain of video games.