Incorporating Human Intention into Self-Adaptive Systems

Shihong Huang, Pedro Miranda
2015 2015 IEEE/ACM 37th IEEE International Conference on Software Engineering  
Self-adaptive systems are fed with contextual information from the environments in which the systems operate, from within themselves, and from the users. Traditional selfadaptive systems research has focused on inputs of systems performance, resources, exception, and error recovery that drive systems' reaction to their environments. The intelligent ability of these self-adaptive systems is impoverished without knowledge of a user's covert attention (thoughts, emotions, feelings). As a result,
more » ... is difficult to build effective systems that anticipate and react to users' needs as projected by covert behavior. This paper presents the preliminary research results on capturing users' intention through neural input, and in reaction, commanding actions from software systems (e.g., load an application) based on human intention. Further, systems can self-adapt and refine their behaviors driven by such human covert behavior. The long-term research goal is to incorporate and synergize human neural input. Thus establishing software systems with a self-adaptive capability to "feel" and "anticipate" users intentions and put the human in the loop. Index Terms-Brain computer interface (BCI), human computer interface (HCI), neural input, self-adaptive systems, overt and covert behavior, human in the loop 2015 IEEE/ACM 37th IEEE International Conference on Software Engineering 978-1-4799-1934-5/15 $31.00
doi:10.1109/icse.2015.196 dblp:conf/icse/HuangM15 fatcat:w6fy5iezrvh5piphsjpzvv6z5q