Motivational digital systems offer capabilities to engage and motivate end-users to foster behavioral changes towards a common goal. In general these systems use gamification principles in non-games contexts. Over the years, gamification has gained consensus among researchers and practitioners as a tool to motivate people to perform activities with the ultimate goal of promoting behavioural change, or engaging the users to perform activities that can offer relevant benefits but which can be seen as unrewarding and even tedious. There exists a plethora of heterogeneous application scenarios towards reaching the common good that can benefit from gamification. However, an open problem is how to effectively combine multiple motivational campaigns to maximise the degree of participation without exposing the system to counterproductive behaviours. We conceive motivational digital systems as multi-agent systems: self-adaptation is a feature of the overall system, while individual agents may self-adapt in order to leverage other agents' resources, functionalities and capabilities to perform tasks more efficiently and effectively. Consequently, multiple campaigns can be run and adapted to reach common good. At the same time, agents are grouped into micro-communities in which agents contribute with their own social capital and leverage others' capabilities to balance their weaknesses. In this paper we propose our vision on how the principles at the base of the autonomous and multi-agent systems can be exploited to design multi-challenge motivational systems to engage smart communities towards common goals. We present an initial version of a general framework based on the MAPE-K loop and a set of research challenges that characterise our research roadmap for the implementation of our vision.
翻译:激励性数字系统提供能力,让最终用户参与和激励最终用户推动行为变化,以实现共同的目标。一般来说,这些系统在非游戏环境中使用强化原则。多年来,研究人员和从业者已达成共识,以此激励人们开展活动,最终目标是促进行为变化,或让用户参与能够带来相关好处、但可被视为不值得称赞、甚至乏味的活动。因此,为实现共同的利益,存在着许许多多不同的应用情景,从解读中受益。然而,一个公开的问题是,如何有效地结合多种激励运动,使参与的程度最大化,而不会使系统暴露为适得其反的行为。我们把激励性数字系统视为多试剂系统:自我适应是整个系统的一个特征,而单个从业者可以进行自我适应,以便利用其他从业者的资源、功能和能力,更高效和更有效地执行任务。因此,可以运行和调整多种运动,以达到共同的利益。与此同时,代理人被组合成微观网络,使共同参与程度最大化的参与程度,而同时又不使系统暴露出适得其反效果的行为。我们把激励性数字系统视为多剂系统:自我调整的动力,我们用自己的智能研究基础,我们用自己的智能研究的动力来利用自己的智能研究能力,我们用自己的智能研究基础系统。