Augmented reality, augmented television and second screen are cutting edge technologies that provide end users extra and enhanced information related to certain events in real time. This enriched information helps users better understand such events, at the same time providing a more satisfactory experience. In the present paper, we apply this main idea to human–robot interaction (HRI), to how users and robots interchange information. The ultimate goal of this paper is to improve the quality of HRI, developing a new dialog manager system that incorporates enriched information from the semantic web. This work presents the augmented robotic dialog system (ARDS), which uses natural language understanding mechanisms to provide two features: (i) a non-grammar multimodal input (verbal and/or written) text; and (ii) a contextualization of the information conveyed in the interaction. This contextualization is achieved by information enrichment techniques that link the extracted information from the dialog with extra information about the world available in semantic knowledge bases. This enriched or contextualized information (information enrichment, semantic enhancement or contextualized information are used interchangeably in the rest of this paper) offers many possibilities in terms of HRI. For instance, it can enhance the robot's pro-activeness during a human–robot dialog (the enriched information can be used to propose new topics during the dialog, while ensuring a coherent interaction). Another possibility is to display additional multimedia content related to the enriched information on a visual device. This paper describes the ARDS and shows a proof of concept of its applications.

No abstract

One of the most challenging tasks in computer graphics and cyberworlds is the realistic animation of the behavior of virtual agents emulating human beings and evolving within virtual 3D worlds. In a previous paper, the authors presented a new, sophisticated behavioral system that allows the agents to take intelligent decisions by themselves [8]. A central issue of this process is the adequate choice of appropriate mechanisms for goal selection. This is actually the aim of the present contribution. In this paper a new scheme for goal selection is described. According to it, the goal's priority is calculated as a combination of different agent's internal states (given by mathematical functions also described in this paper) and external factors (which will determine the goal's feasibility). The architecture of the goal selection module as well as its simulation flow are also analyzed in this paper. Finally, the excellent performance of this new scheme is enlightened by means of a simple yet illustrative example.

This paper focuses on modeling the behavior of virtual agents living in a virtual 3D world. Our aim is to apply the most typical human behavior features to our virtual agents so that they behave as realistic as possible. To this end, a new architecture for the behavioral engine that incorporates a number of these typical characteristics of human behavior is introduced. This new proposal allows the virtual agents to interact among them and with the environment in a quite realistic way. The main features of this new architecture, such as perception, knowledge management, motion control and action selection (using internal states, world information, goals, and others) are carefully analyzed in the paper. Finally, some relevant functions (those describing sensations such as tiredness, agent's resistance and recovery capacities, happiness and anxiety) and parameters (those determining the vision range or sociability) are also described in the paper.