Incorporating Human Intention into Self-Adaptive Systems

Self-adaptive systems are fed with contextual information from the environments in which the systems operate,from within themselves, and from the users. Traditional self-adaptive systems research has focused on inputs of systems performance, resources, exception, and error recovery that drive systems\´ reaction to their environments. The intelligent ability ofthese self-adaptive systems is impoverished without knowledge ofa user\´s covert attention (thoughts, emotions, feelings). As a result, it is difficult to build effective systems that anticipate and react to users\´ needs as projected by covert behavior. This paperpresents 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.