PRE2020 3 Group8
Group description
Abstract
A pure software end-user application that supports people in their need to socialize while motivating self-improvement. Anthropomorphism is intentionally used to increase user commitment and experience. Machine learning techniques are used to process user's data and provide feedback, and to facilitate the anthropomorphized interface.
Members
(in alphabetical order):
- Edwin Steenkamer
- Emi Kuijpers (1227154)
- Fanni Egresits
- Morris Boers (1253107)
- Lulof Pirée (1363638)
GitHub Page:
Logbook
See the page logbook_group_8
Problem statement and objectives
Goals
The software application should:
- Significantly reduce symptoms of loneliness as induced by infrequent social contact in users
- Register personal goals set by the users
- Collect data on the user's behavior and progress towards goals
- Provide the user with feedback and constructive nudges
Beyond the scope
The following features are probably valuable additions to the product, but they are beyond the scope of what can be achieved in one quartile:
- Voice recognition
- Animated anthropomorphized interface (e.g. simulated face)
Who are the users
The target of the application is to support civilians in daily life. The audience of the prototype is narrowed down to adolescents and adults who use computers on a daily basis.
TODO...
Approach, milestones and deliverables
TODO...
Literature Review
Statistical dialog systems
Statistical dialog systems can be divided into two major categories[1]. The first category learns mappings from input messages to responses. In the simplest case this learning a probability distribution. More advanced algorithms, such as Seq2Seq, do take prior context into account. In particular, Seq2Seq uses two LSTMs (Long Short-Term Memory, a commonly used variant of Recurrent Neural Networks): one to encode input messages to an abstract feature vector, and another to convert such vectors to a reply [2].
Overview
WIP below
Possibly relevant papers:
- Identifying Evidence-Informed Physical Activity Apps: Content Analysis
- This one is a bachelor thesis, but it does include useful references: [3]
- The efficacy of using mobile applications in changing adolescent girls’ physical activity behaviour during weekends
possibly relevant papers for theoretical background:
- Literature review about different robotics (ours is assistive i think)
Royakkers, L., & van Est, R. (2015). A literature review on new robotics: automation from love to war. International journal of social robotics, 7(5), 549-570. (Summarized)
- ANTHROPOMORPHISM:
Melson GF, Kahn PH, Beck A, Friedman B (2009) Robotic pets in human lives: implications for the human-animal bond and for human relationships with personified technologies. J Soc Issues 65(3):545–569
- ANTHROPOMORPHISM:
Duffy BR (2003) Anthropomorphism and the social robot. Robot Auton Syst 42(3–4):170–190
- DESIGNING and taking into account human emotions:
chapter 5 of Affective Interaction: Understanding, Evaluating, and Designing for Human Emotion (See summary)
- SOCIAL FACILITATION
Riether, N., Hegel, F., Wrede, B., & Horstmann, G. (2012, March). Social facilitation with social robots?. In 2012 7th ACM/IEEE International Conference on Human-Robot Interaction (HRI) (pp. 41-47). IEEE. (see summary)
- SOCIAL FACILITATION
Woods, S., Dautenhahn, K., & Kaouri, C. (2005, June). Is someone watching me?-consideration of social facilitation effects in human-robot interaction experiments. In 2005 international symposium on computational intelligence in robotics and automation (pp. 53-60). IEEE. (see summary)
- LONELINESS
Eyssel, F., & Reich, N. (2013, March). Loneliness makes the heart grow fonder (of robots)—On the effects of loneliness on psychological anthropomorphism. In 2013 8th acm/ieee international conference on human-robot interaction (hri) (pp. 121-122). IEEE.
- INCREASING MOTIVATION USING ROBOTICS
van Minkelen, P., Gruson, C., van Hees, P., Willems, M., de Wit, J., Aarts, R., ... & Vogt, P. (2020, March). Using self-determination theory in social robots to increase motivation in L2 word learning. In Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction (pp. 369-377).
User guide
TODO...
Software documentation
TODO...
References
- ↑ 1.0 1.1 Jiwei Li, Will Monroe, Alan Ritter, Michel Galley, Jianfeng Gao, Dan Jurafsky (2016). Deep Reinforcement Learning for Dialogue Generation. Published: arXiv.org. DOI: [1]. Date accessed: 01-02-2021.
- ↑ Ilya Sutskever, Oriol Vinyals, and Quoc V. Le (2014). Sequence to sequence learning with neural networks. Published: Advances in neural information processing systems, pages 3104-3112. URL: [2]. Date accessed: 02-02-2021.