Representing and Reasoning about Complex Human Activities - an Activity-Centric Argumentation-Based Approach

Abstract: The aim of this thesis is to develop theories and formal methods to endow a computing machinery with capabilities to identify, represent, reason and evaluate complex activities that are directed by an individual’s needs, goals, motives, preferences and environment, information which can be inconsistent and incomplete.Current methods for formalising and reasoning about human activity are typically limited to basic actions, e.g., walking, sitting, sleeping, etc., excluding elements of an activity. This research proposes a new formal activity-centric model that captures complex human activity based on a systemic activity structure that is understood as a purposeful, social, mediated, hierarchically organized and continuously developing interaction between people and word.This research has also resulted in a common-sense reasoning method based on argumentation, in order to provide defeasible explanations of the activity that an individual performs based on the activity-centric model of human activity. Reasoning about an activity is based on the novel notion of an argument under semantics-based inferences that is developed in this research, which allows the building of structured arguments and inferring consistent conclusions.Structured arguments are used for explaining complex activities in a bottom-up manner, by introducing the notion of fragments of activity. Based on these fragments, consistent argumentation based interpretations of activity can be generated, which adhere to the activity-centric model of complex human activity.For resembling the kind of deductive analysis that a clinician performs in the assessment of activities, two quantitative measurements for evaluating performance and capacity are introduced and formalized. By analysing these qualifiers using different argumentation semantics, information useful for different purposes can be generated. e.g., such as detecting risk in older adults for falling down, or more specific information about activity performance and activity completion. Both types of information can form the base for an intelligent machinery to provide tailored recommendation to an individual.The contributions were implemented in different proof-of-concept systems, designed for evaluating complex activities and improving individual’s health in daily life. These systems were empirically evaluated with the purpose of evaluating theories and methodologies with potential users. The results have the potential to be utilized in domains such as ambient assisted living, assistive technology, activity assessment and self-management systems for improving health.