Learning and Mining Player Motion Profiles in Physically Interactive Robogames
AbstractPhysically-Interactive RoboGames (PIRG) are an emerging application whose aim is to develop robotic agents able to interact and engage humans in a game situation. In this framework, learning a model of players’ activity is relevant both to understand their engagement, as well as to understand specific strategies they adopted, which in turn can foster game adaptation. Following such directions and given the lack of quantitative methods for player modeling in PIRG, we propose a methodology for representing players as a mixture of existing player’s types uncovered from data. This is done by dealing both with the intrinsic uncertainty associated with the setting and with the agent necessity to act in real time to support the game interaction. Our methodology first focuses on encoding time series data generated from player-robot interaction into images, in particular Gramian angular field images, to represent continuous data. To these, we apply latent Dirichlet allocation to summarize the player’s motion style as a probabilistic mixture of different styles discovered from data. This approach has been tested in a dataset collected from a real, physical robot game, where activity patterns are extracted by using a custom three-axis accelerometer sensor module. The obtained results suggest that the proposed system is able to provide a robust description for the player interaction. View Full-Text
Scifeed alert for new publicationsNever miss any articles matching your research from any publisher
- Get alerts for new papers matching your research
- Find out the new papers from selected authors
- Updated daily for 49'000+ journals and 6000+ publishers
- Define your Scifeed now
Oliveira, E.L.S.; Orrù, D.; Morreale, L.; Nascimento, T.P.; Bonarini, A. Learning and Mining Player Motion Profiles in Physically Interactive Robogames. Future Internet 2018, 10, 22.
Oliveira ELS, Orrù D, Morreale L, Nascimento TP, Bonarini A. Learning and Mining Player Motion Profiles in Physically Interactive Robogames. Future Internet. 2018; 10(3):22.Chicago/Turabian Style
Oliveira, Ewerton L.S.; Orrù, Davide; Morreale, Luca; Nascimento, Tiago P.; Bonarini, Andrea. 2018. "Learning and Mining Player Motion Profiles in Physically Interactive Robogames." Future Internet 10, no. 3: 22.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.