Difference between revisions of "Robogame Strategy"
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| − | Due to a steady progress in interactive systems and robots, a natural evolution in the context of gaming experience is to bring the elimination of screens and devices for presenting the users with the possibility to physically interact with autonomous agents in their homes without the need to produce an entire virtual reality (such as that in classical videogames). This new style of robogames, known as Physically Interactive RoboGames (PIRG), exploit the real world (in both its dynamical unstructured and structured aspects) as environments and real, physical, autonomous devices as game companions. Considering this, | + | Due to a steady progress in interactive systems and robots, a natural evolution in the context of gaming experience is to bring the elimination of screens and devices for presenting the users with the possibility to physically interact with autonomous agents in their homes without the need to produce an entire virtual reality (such as that in classical videogames). This new style of robogames, known as Physically Interactive RoboGames (PIRG), exploit the real world (in both its dynamical unstructured and structured aspects) as environments and real, physical, autonomous devices as game companions. Considering this, this PhD research project proposes to investigate how to develop complex strategy-based abilities in autonomous robots for the purpose of PIRG design by the use of machine learning (ML) techniques. Specifically, the ability of intention detection for strategy adjustment will be targeted. The planned methodology aims to explore mobile robot bases with cheap sensors and algorithms requiring little power to be executed in real time ("green algorithms") in non-structured environments since these are interesting constraints currently addressed in robogames, and in the whole Robotics community, to enable the spread of robots in the society and make them reach the market. As formal contribution to scientific community, the proposed research may open up ways for the exploitation of new methods and approaches for designing PIRG in view of its relationship with ML-based techniques and human-robot interaction. Moreover, it will be possible to tackle the necessity of creating robots even more able of being perceived as rational agents, i.e., possibly smart enough to play the role of effective opponents or teammates and thus become more likely to reach the mass-market as a new robotic product. As consequence of this, this proposal directly contributes for the advancement of different scientific fields, such as Artificial Intelligence, Machine Learning and Robotics. The results obtained in Robogames will be made available for use in all other applications involving Human-Robot Interaction. |
Revision as of 16:31, 25 August 2015
General Description
Due to a steady progress in interactive systems and robots, a natural evolution in the context of gaming experience is to bring the elimination of screens and devices for presenting the users with the possibility to physically interact with autonomous agents in their homes without the need to produce an entire virtual reality (such as that in classical videogames). This new style of robogames, known as Physically Interactive RoboGames (PIRG), exploit the real world (in both its dynamical unstructured and structured aspects) as environments and real, physical, autonomous devices as game companions. Considering this, this PhD research project proposes to investigate how to develop complex strategy-based abilities in autonomous robots for the purpose of PIRG design by the use of machine learning (ML) techniques. Specifically, the ability of intention detection for strategy adjustment will be targeted. The planned methodology aims to explore mobile robot bases with cheap sensors and algorithms requiring little power to be executed in real time ("green algorithms") in non-structured environments since these are interesting constraints currently addressed in robogames, and in the whole Robotics community, to enable the spread of robots in the society and make them reach the market. As formal contribution to scientific community, the proposed research may open up ways for the exploitation of new methods and approaches for designing PIRG in view of its relationship with ML-based techniques and human-robot interaction. Moreover, it will be possible to tackle the necessity of creating robots even more able of being perceived as rational agents, i.e., possibly smart enough to play the role of effective opponents or teammates and thus become more likely to reach the mass-market as a new robotic product. As consequence of this, this proposal directly contributes for the advancement of different scientific fields, such as Artificial Intelligence, Machine Learning and Robotics. The results obtained in Robogames will be made available for use in all other applications involving Human-Robot Interaction.
