Catálogo de publicaciones - libros

The animation of an ECA, for most of animation systems, implies that its behaviour is encoded in a representation language, giving a form for each modality of a behavior. Interested in gestures, several representation languages exist already that that usually give a physical description of the necessary information for the production and reproduction of gestures, and for its synchronization with the other modalities. In our work, we aim at enriching the SAIBA description of gestures with semantic considerations, enabling an ECA to use a gesture for a wide range of applications.

The form of improvisational drama allows participants to have their own choices to influence the ongoing story, and each play results in a different ending. However, authoring such story contents requires ad hoc scripting, and static story structures lose ingenuity once users hacked through them. Our purpose is to develop a toolkit for: (1) fast authoring the story content, and (2) allow it for repeated plays yet retaining fresh interactive experience. While most similar applications have explicit, sophisticated story structures to ensure the number of possible interactions and endings in specific situations, we argue that characters should have enough background knowledge to make any improvisational choices. The more knowledge they have, the more sophisticated course of actions they may express. As a result, we take a deep-model approach to implement virtual agents, allowing them to deliberate and act with established knowledge in unexpected situations.

The AtGentive project [1,2] focuses on the support of attention in learning environments. To achieve this objective the system analyses the learners’ computer activities and physical states and, on the basis of this analysis, it generates interventions. Such interventions either supply learners with information useful to support their current attentional focus, or are aimed at attracting the user’s attention to new foci.

Nowadays, the video gaming experience is shifting from merely realistic to believable. The behaviour of the computer driven player and non-playing characters is often poor when compared to their visual appearance. In this sense, there has been a recent interest in improving the video gaming experience with novel Artificial Intelligence (AI) techniques. This paper presents a robotics inspired behavioural AI to simulate characters’ personalities in a commercial video game.

This paper considers possible affective roles in an agent-based social simulation, and in particular the effect of adding a simple model of fear into a replication of an agent-based social simulation.

The JAST human-robot dialogue system [1] is designed as a platform to integrate empirical findings on cognition and cooperative dialogue with research on autonomous robots by supporting multimodal human-robot collaboration on a construction task. The robot consists of a pair of mechanical arms with grippers, mounted in a position to resemble human arms, and a Philips iCat animatronic talking head [2]. The user and the robot work together to assemble construction toys on a common work area, coordinating their actions through speech, gestures, and facial displays.

Modeling crowd behaviors is an important challenge for intelligent virtual agents. We propose a general cognitive model of simulating crowd behaviors, based on Festinger’s Social Comparison Theory (SCT), a prominent social psychology theory. We present the use of the SCT model (using the Soar cognitive architecture) in the generation of imitational behavior in loosely-coupled groups and show that SCT generates behavior more in-tune with human crowd behavior.

This work presents a market-based social model to produce good quality behavioral animations for groups of intelligent virtual agents. The social model coordinates the activities of groups of virtual characters and also includes social actions in the agent decision-making. We follow the Multi-Agent Resource Allocation approach presented in [2], where agents express their preferences using utility functions. The dynamics of social interactions is inspired by the theory of Piaget [3] over which we have implemented reciprocal task exchanges.

Embodied Conversational Agents (ECAs) are life-like CG characters that interact with human users in face-to-face conversations. To achieve natural conversations, they need to understand the inputs from human users, deliberate the responding behaviors and realize those behaviors in multiple modalities. They are sophisticated, require numbers of building assemblies and are thus difficult for individual research groups to develop. To facilitate result sharing and rapid prototyping of ECA researches, a Generic ECA Framework that is meant to integrate ECA assemblies seamlessly is being developed by our group. This framework is composed of a low-level communication platform (GECA Platform), a set of communication API libraries (GECA Plugs) and a high-level protocol (GECA Protocol, GECAP).

This article describes an attempt to build a quiz game kiosk for show-room use based on the Generic Embodied Conversational Agent (GECA) Framework [1] that provides a general purpose architecture for connecting modularized ECA functional components for multimodal human-agent interactions.