Towards Emotionally Adapted Games based on User Controlled Emotion Knobs

In this paper, we present a framework for a gaming personalization system to systematically facilitate user-selected desired emotional states during gameplay with control knobs that regulate the emotional impact of the game. Underlying the framework is a Psychological Customization system. It entails personalization of the way of presenting information (user interface, visual layouts, modalities, narrative structures and other factors) per user or user group to create desired transient psychological effects and states, such as emotion, attention, involvement, presence, persuasion and learning (Saari and Turpeinen, 2004; Turpeinen and Saari, 2004). By varying the form of information presented in a game in an emotionally intelligent way it may be possible to achieve such effects. Theory, key concepts, available empiric evidence and an example of user controlled emotional gaming as well as a basic system design are presented. Psychological Customization includes modeling of individuals, groups, and communities to create psychological profiles and other profiles based on which customization may be conducted. In addition, a database of design rules is needed to define the desired cognitive and emotional effects for different types of profiles. Once these components are in place, content management technologies can be extended to cover variations of form and substance of information based on psychological profiles and design rules to create the desired psychological effects. (Turpeinen and Saari, 2004) Gaming research is often conducted on the basis of game content and genre analysis, typologies of gaming styles or consumption, narrative elements of the game and sales of games. Outside narrative elements of a game, also the factors related to the presentation of the substance of the game or the form of the game, such as visual representations of the gaming events, amount and pace of image motion, audio effects and background music, and the level of interactivity offered to the player, are important from the point of view of emotion. A basic approach to an element to be adapted inside a game is a psychologically validated template that is embedded inside the game to create a particular psychological effect. A broad view of templates may be that the whole game consists of a database of psychologically validated templates that are presented in sequences. A limited view entails that a smaller collection of templates is used. The element of psychological evaluation means that the selected psychological influence (such an emotional response) of the template on a particular type of user is sufficiently well predictable. These psychologically evaluated templates may consist of i) manipulating the substance of a game, such as story line (initiating events, new characters etc.) and manipulating the situations specifically related to the character of the player (such as putting the character into sudden and dangerous situations inside the game) and ii) manipulating the form or way of presentation of the game (such as visual elements, shapes, colours, types of objects, sound effects, background music, level of interactivity and feedback etc.). The difficulty level of the game may also be continuously automatically be adjusted, thereby keeping the skills and challenges in balance, which results in a maintenance of an optimal emotional experience and possibly also a flow-state. (Saari et al, in press) Introducing the element of user-controlled emotional regulation into such a gaming system happens by building a user experience control knob for the system. The user could select between emotions, such as wanting high arousal and positive emotion as much as possible or wanting to be calm and non-aroused while playing. One may also offer content-characteristic emotional regulation, such as less or more violence. This kind of a system could act as a parental control system for controlling the arousal states during childrens´ gameplay. One solution to verify the emotional reactios of the user during gaming is to have the user linked to a psychophysiological measurement system. An important advantage of psychophysiological measurements is that they can be performed continuously during game playing and have a high level of temporal precision. (Saari et al, in press) Several scenarios of using an emotional regulation system for gaming will be presented in the paper. It should be noted that from the point of view of ecological validity it may be stated that the key to a "good" fighting or war game is the optimal division of different types of emotional experiences while gaming, rather than just intensifying for instance excitement and arousal all the time. For instance, fear and hatred may be skillfully interlaced with joy and positive emotion. In other words, some parts of the game contain hatred and fear but there also have to be parts in which these are relieved and moments of victory and joy can be experienced (a terrible enemy has finally been devastated by the player). The value of the basic system design and approach presented in the article to HCI is obvious as a basis of new kind of paradigm for user controlled Human Computer Interaction based on emotional regulation. References Saari, T. and Turpeinen, M. (2004) Towards Psychological Customization of Information for Individuals and Social Groups. In Karat, M-C., Blom, J. and Karat. J. (eds.) Personalization of User Experiences for eCommerce, Kluwer, Germany. Saari, T., Ravaja, N., Laarni, J., Kallinen, K. and Turpeinen, M. (in press) Towards emotionally adapted Games. Accepted to Presence 2004 Turpeinen, M. and Saari, T. (2004) System Architechture for Psychological Customization of Information. Proceedings of HICSS-37- conference, 5.-8.1. 2004, Hawaii

TWIN: Personality-based Intelligent Recommender System

This paper presents the Tell me What I Need (TWIN) Personality-based Intelligent Recommender System, the goal of which is to recommend items chosen by like-minded (or twin ) people with similar personality types which we estimate from their writings. In order to produce recommendations it applies the results achieved in the personality from the text recognition research field to Personality-based Recommender System user profile modelling. In this way it creates a bridge between the efforts of automatic personality score estimation from plain text and the field of Intelligent Recommender Systems. The paper describes the TWIN system architecture, and results of the experimentation with the system in the online travelling domain in order to investigate the possibility of providing valuable recommendations of hotels of the TripAdvisor website for like-minded people . The results compare favourably with related experiments, although they demonstrate the complexity of this challenging task.The research work of the third author is partially funded by the WIQ-EI (IRSES grant n. 269180) and DIANA APPLICATIONS (TIN2012-38603-C02-01), and done in the framework of the VLC/Campus Microcluster on Multimodal Interaction in Intelligent Systems.Roshchina, A.; Cardiff, J.; Rosso, P. (2015). TWIN: Personality-based Intelligent Recommender System. Journal of Intelligent and Fuzzy Systems. 28(5):2059-2071. https://doi.org/10.3233/IFS-141484S20592071285Bodapati, A. V. (2008). Recommendation Systems with Purchase Data. Journal of Marketing Research, 45(1), 77-93. doi:10.1509/jmkr.45.1.77Dean, J., & Ghemawat, S. (2008). MapReduce. Communications of the ACM, 51(1), 107. doi:10.1145/1327452.1327492Nageswara Rao, K. (2008). Application Domain and Functional Classification of Recommender Systems—A Survey. DESIDOC Journal of Library & Information Technology, 28(3), 17-35. doi:10.14429/djlit.28.3.174Castro, J., Rodriguez, R. M., & Barranco, M. J. (2013). Weighting of Features in Content-Based Filtering with Entropy and Dependence Measures. International Journal of Computational Intelligence Systems, 7(1), 80-89. doi:10.1080/18756891.2013.859861Cantador, I., Bellogín, A., & Vallet, D. (2010). Content-based recommendation in social tagging systems. Proceedings of the fourth ACM conference on Recommender systems - RecSys ’10. doi:10.1145/1864708.1864756Huang, S. (2011). Designing utility-based recommender systems for e-commerce: Evaluation of preference-elicitation methods. Electronic Commerce Research and Applications, 10(4), 398-407. doi:10.1016/j.elerap.2010.11.003Tkalčič, M., Burnik, U., & Košir, A. (2010). Using affective parameters in a content-based recommender system for images. User Modeling and User-Adapted Interaction, 20(4), 279-311. doi:10.1007/s11257-010-9079-zRentfrow, P. J., & Gosling, S. D. (2003). The do re mi’s of everyday life: The structure and personality correlates of music preferences. Journal of Personality and Social Psychology, 84(6), 1236-1256. doi:10.1037/0022-3514.84.6.1236Elahi, M., Braunhofer, M., Ricci, F., & Tkalcic, M. (2013). Personality-Based Active Learning for Collaborative Filtering Recommender Systems. Lecture Notes in Computer Science, 360-371. doi:10.1007/978-3-319-03524-6_31Tkalcic, M., Odic, A., Kosir, A., & Tasic, J. (2013). Affective Labeling in a Content-Based Recommender System for Images. IEEE Transactions on Multimedia, 15(2), 391-400. doi:10.1109/tmm.2012.2229970Quercia, D., Kosinski, M., Stillwell, D., & Crowcroft, J. (2011). Our Twitter Profiles, Our Selves: Predicting Personality with Twitter. 2011 IEEE Third Int’l Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third Int’l Conference on Social Computing. doi:10.1109/passat/socialcom.2011.26Mairesse, F., Walker, M. A., Mehl, M. R., & Moore, R. K. (2007). Using Linguistic Cues for the Automatic Recognition of Personality in Conversation and Text. Journal of Artificial Intelligence Research, 30, 457-500. doi:10.1613/jair.2349Golbeck, J., Robles, C., & Turner, K. (2011). Predicting personality with social media. Proceedings of the 2011 annual conference extended abstracts on Human factors in computing systems - CHI EA ’11. doi:10.1145/1979742.1979614Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009). The WEKA data mining software. ACM SIGKDD Explorations Newsletter, 11(1), 10. doi:10.1145/1656274.1656278Tausczik, Y. R., & Pennebaker, J. W. (2009). The Psychological Meaning of Words: LIWC and Computerized Text Analysis Methods. Journal of Language and Social Psychology, 29(1), 24-54. doi:10.1177/0261927x09351676Islam, M. J., Wu, Q. M. J., Ahmadi, M., & Sid-Ahmed, M. A. (2007). Investigating the Performance of Naive- Bayes Classifiers and K- Nearest Neighbor Classifiers. 2007 International Conference on Convergence Information Technology (ICCIT 2007). doi:10.1109/iccit.2007.14

Play Experience Enhancement Using Emotional Feedback

Innovations in computer game interfaces continue to enhance the experience of players. Affective games - those that adapt or incorporate a player’s emotional state - have shown promise in creating exciting and engaging user experiences. However, a dearth of systematic exploration into what types of game elements should adapt to affective state leaves game designers with little guidance on how to incorporate affect into their games. We created an affective game engine, using it to deploy a design probe into how adapting the player’s abilities, the enemy’s abilities, or variables in the environment affects player performance and experience. Our results suggest that affectively adapting games can increase player arousal. Furthermore, we suggest that reducing challenge by adapting non-player characters is a worse design choice than giving players the tools that they need (through enhancing player abilities or a supportive environment) to master greater challenges