Pedro Sequeira

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In this paper, we propose an adaptation of four common appraisal dimensions that evaluate the relation of an agent with its environment into reward features within an intrinsically motivated reinforcement learning framework. We show that, by optimizing the relative weights of such features for a given environment, the agents attain a greater degree of(More)
Virtual environments are often populated by autonomous synthetic agents capable of acting and interacting with other agents as well as with humans. These virtual worlds also include objects that may have different uses and types of interactions. As such, these agents need to identify possible interactions with the objects in the environment and measure the(More)
This demo features FearNot!, a school-based Virtual Learning Environment (VLE) populated by synthetic characters representing the various actors in a bullying scenario. FearNot! uses emergent narrative to create improvised dramas with those characters. The goal is to enable children to explore bullying issues, and coping strategies, interacting with(More)
In this demonstration, we describe a scenario developed in the EMOTE project [2]. The overall goal of the EMOTE project is to develop an empathic robot tutor for 11-13 year old school students in an educational setting. The pedagogical domain we demonstrate here is to assist students in learning and testing their map-reading skills typically learned as part(More)
The positive impact of emotions in decision-making has long been established in both natural and artificial agents. In the perspective of appraisal theories, emotions complement perceptual information, coloring our sensations and guiding our decision-making. However, when designing autonomous agents, is emotional appraisal the best complement to the(More)
In this paper, we investigate the use of emotional information in the learning process of autonomous agents. Inspired in four dimensions commonly postulated by appraisal theories of emotions, we construct a set of reward features to guide the learning process and behavior of a reinforcement learning (RL) agent inhabiting an environment of which it has only(More)
In order to explore the impact of integrating a robot as a facilitator in a collaborative activity, we examined interpersonal distancing of children both with a human adult and a robot facilitator. Our scenario involves two children performing a collaborative learning activity, which included the writing of a word/letter on a tactile tablet. Based on the(More)