In this section we will list the various mappings between the components of our proposed model of affect, i.e. between personality, motivation, emotions, and mood.

The objective of goal recognition is to infer a goal that accounts for the observed behavior of an actor. In this work, we introduce and formalize the notion of active goal recognition in which we endow the observer with agency to sense, reason, and act in the world with a view to enhancing and possibly expediting goal recognition, and/or to intervening in goal achievement. To this end, we present an algorithm for active goal recognition and a landmark-based approach to the elimination of hypothesized goals which leverages automated planning. Experiments demonstrate the merits of providing agency to the observer, and the effectiveness of our approach in potentially enhancing the observational power of the observer, as well as expediting and in some cases making possible the recognition of the actor's goal.

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Theory of Mind is commonly defined as the ability to attribute mental states (e.g., beliefs, goals) to oneself, and to others. A large body of previous work-from the social sciences to artificial intelligence-has observed that Theory of Mind capabilities are central to providing an explanation to another agent or when explaining that agent's behaviour. In this paper, we build and expand upon previous work by providing an account of explanation in terms of the beliefs of agents and the mechanism by which agents revise their beliefs given possible explanations. We further identify a set of desiderata for explanations that utilize Theory of Mind. These desiderata inform our belief-based account of explanation.

Human beings, even small children, quickly become adept at figuring out how to use applications on their mobile devices. Learning to use a new app is often achieved via trial-and-error, accelerated by transfer of knowledge from past experiences with like apps. The prospect of building a smarter smartphone -one that can learn how to achieve tasks using mobile apps -is tantalizing. In this paper we explore the use of Reinforcement Learning (RL) with the goal of advancing this aspiration. We introduce an RL-based framework for learning to accomplish tasks in mobile apps. RL agents are provided with states derived from the underlying representation of on-screen elements, and rewards that are based on progress made in the task. Agents can interact with screen elements by tapping or typing. Our experimental results, over a number of mobile apps, show that RL agents can learn to accomplish multi-step tasks, as well as achieve modest generalization across different apps. More generally, we develop a platform 2 which addresses several engineering challenges to enable an effective RL training environment. Our AppBuddy platform is compatible with OpenAI Gym and includes a suite of mobile apps and benchmark tasks that supports a diversity of RL research in the mobile app setting.