Human-Aware Planning for Promoting Social Behavior-Change in AutismAn action reasoning approach utilizing answer set programming

Abstract: This study aims to find ways for an intelligent software agent to understand a human’s behavior in a social situation, and in this process, plan its own actions in order to assist the human in reaching their goals.The use-case is a Virtual Reality (VR) game, developed for children with autism for practicing social scenarios, scenarios that children with autism often find stressful or scary. This study investigates how an intelligent software agent can, by continuously analyzing how the user interacts with a simulated social scenario, adapt the simulation through appropriate interventions. This helps the user to succeed in the social scenario while providing a challenging learning environment. In this human-aware planning problem, the variables of the environment and the human’s mental state constitutes Interaction Constraints (IC) for the system. Central questions in this study regard what, how and when appropriate interventions can be provided by the system to facilitate behavior change, and in this process, preserve dynamic sub-goals of the human. An action reasoning computational model is proposed inspired by three cognitive theories; (1) the theory of planned behavior, answering the what and how questions of the model by defining transitions between goals; (2) the stress staircase and (3) the zone of proximal development, together answering the when questions of the model. By defining the user’s physical and mental state based on fluents of the environment, plans can be generated for providing goal-oriented interventions. A figurative instance of the scenario can then evaluate the plans, according to weights tailored to the individual user, to select a final plan for adapting the virtual scenario. The proposed human-aware planning architecture can also be applied in environments that are not virtual, by utilizing modern mobile devices which have built-in sensors that measur emotion, orientation, and various environmental conditions. Future work concerns how automated learning approaches can be incorporated in the architecture to provide tailored levels of personalization.