Biblio

While language learning in infants and toddlers progresses somewhat seamlessly, in artificial systems the grounding of language in knowledge structures that are learned from sensorimotor experiences remains a hard challenge. Here we introduce LEARNA, which learns event-characterizing abstractions to resolve natural language ambiguity. LEARNA develops knowledge structures from simulated sensorimotor experiences. Given a possibly ambiguous descriptive utterance, the learned knowledge structures enable LEARNA to infer environmental scenes, and events unfolding within, which essentially constitute plausible imaginations of the utterance’s content. Similar event-predictive structures may help in developing artificial systems that can generate and comprehend descriptions of scenes and events.

In this paper, we present an embodied conversational agent (ECA) that includes a cognitive-affective architecture based on the Soar cognitive architecture, integrates an emotion model based on ALMA that uses a three-layered model of emotions, mood and personality, from the point of view of the user and the agent. These features allow to modify the behavior and personality of the agent to achieve a more realistic and believable interaction with the user. This ECA works as a virtual assistant to search information from Wikipedia and show personalized results to the user. It is only a prototipe, but can be used to show some of the possibilities of the system. A first evaluation was conducted to prove these possibilities, with satisfactory results that also give guidance for some future work that can be done with this ECA.

In this paper, we present E-VOX, an emotionally enhanced semantic ECA designed to work as a virtual assistant to search information from Wikipedia. It includes a cognitive-affective architecture that integrates an emotion model based on ALMA and the Soar cognitive architecture. This allows the ECA to take into account features needed for social interaction such as learning and emotion management. The architecture makes it possible to influence and modify the behavior of the agent depending on the feedback received from the user and other information from the environment, allowing the ECA to achieve a more realistic and believable interaction with the user. A completely functional prototype has been developed showing the feasibility of our approach.