The distracted robot: what happens when artificial agents behave like us

In everyday life, we are frequently exposed to different smart technologies. From our smartphones to avatars in computer games, and soon perhaps humanoid robots, we are surrounded by artificial agents created to interact with us. Already during the design phase of an artificial agent, engineers often endow it with functions aimed to promote the interaction and engagement with it, ranging from its \u201ccommunicative\u201d abilities to the movements it produces. Still, whether an artificial agent that can behave like a human could boost the spontaneity and naturalness of interaction is still an open question. Even during the interaction with conspecifics, humans rely partially on motion cues when they need to infer the mental states underpinning behavior. Similar processes may be activated during the interaction with embodied artificial agents, such as humanoid robots. At the same time, a humanoid robot that can faithfully reproduce human-like behavior may undermine the interaction, causing a shift in attribution: from being endearing to being uncanny. Furthermore, it is still not clear whether individual biases and prior knowledge related to artificial agents can override perceptual evidence of human-like traits. A relatively new area of research emerged in the context of investigating individuals\u2019 reactions towards robots, widely referred to as Human-Robot Interaction (HRI). HRI is a multidisciplinary community that comprises psychologists, neuroscientists, philosophers as well as roboticists, and engineers. However, HRI research has been often based on explicit measures (i.e. self-report questionnaires, a-posteriori interviews), while more implicit social cognitive processes that are elicited during the interaction with artificial agents took second place behind more qualitative and anecdotal results. The present work aims to demonstrate the usefulness of combining the systematic approach of cognitive neuroscience with HRI paradigms to further investigate social cognition processes evoked by artificial agents. Thus, this thesis aimed at exploring human sensitivity to anthropomorphic characteristics of a humanoid robot's (i.e. iCub robot) behavior, based on motion cues, under different conditions of prior knowledge. To meet this aim, we manipulated the human-likeness of the behaviors displayed by the robot and the explicitness of instructions provided to the participants, in both screen-based and real-time interaction scenarios. Furthermore, we explored some of the individual differences that affect general attitudes towards robots, and the attribution of human-likeness consequently

Toward autonomous architecture: The convergence of digital design, robotics, and the built environment

The way we design, construct, and inhabit buildings is changing—moving toward greater integration of robotic and autonomous systems that challenge our preconceived notions of how buildings are made, what they are, or what they should be

Integrating verbal and nonverbal communication in a dynamic neural field architecture for human–robot interaction

How do humans coordinate their intentions, goals and motor behaviors when performing joint action tasks? Recent experimental evidence suggests that resonance processes in the observer’s motor system are crucially involved in our ability to understand actions of others’, to infer their goals and even to comprehend their action-related language. In this paper, we present a control architecture for human–robot collaboration that exploits this close perception-action linkage as a means to achieve more natural and efficient communication grounded in sensorimotor experiences. The architecture is formalized by a coupled system of dynamic neural fields representing a distributed network of neural populations that encode in their activation patterns goals, actions and shared task knowledge. We validate the verbal and nonverbal communication skills of the robot in a joint assembly task in which the human–robot team has to construct toy objects from their components. The experiments focus on the robot’s capacity to anticipate the user’s needs and to detect and communicate unexpected events that may occur during joint task execution.Fundação para a Ciência e a Tecnologia (FCT) - Bolsa POCI/V.5/A0119/2005 and CONC-REEQ/17/2001European Commission through the project JAST (IP-003747

Artificial Cognition for Social Human-Robot Interaction: An Implementation

© 2017 The Authors Human–Robot Interaction challenges Artificial Intelligence in many regards: dynamic, partially unknown environments that were not originally designed for robots; a broad variety of situations with rich semantics to understand and interpret; physical interactions with humans that requires fine, low-latency yet socially acceptable control strategies; natural and multi-modal communication which mandates common-sense knowledge and the representation of possibly divergent mental models. This article is an attempt to characterise these challenges and to exhibit a set of key decisional issues that need to be addressed for a cognitive robot to successfully share space and tasks with a human. We identify first the needed individual and collaborative cognitive skills: geometric reasoning and situation assessment based on perspective-taking and affordance analysis; acquisition and representation of knowledge models for multiple agents (humans and robots, with their specificities); situated, natural and multi-modal dialogue; human-aware task planning; human–robot joint task achievement. The article discusses each of these abilities, presents working implementations, and shows how they combine in a coherent and original deliberative architecture for human–robot interaction. Supported by experimental results, we eventually show how explicit knowledge management, both symbolic and geometric, proves to be instrumental to richer and more natural human–robot interactions by pushing for pervasive, human-level semantics within the robot's deliberative system

Real World of Artificial Intelligence - A Review

Artificial Intelligence is the combination of machine and human intelligence, which are in research trends from the last many years. Different Artificial Intelligence programs have become capable of challenging humans by providing Expert Systems, Neural Networks, Robotics, Natural Language Processing, Face Recognition and Speech Recognition. Artificial Intelligence brings a bright future for different technical inventions in various fields. This review paper shows the general concept of Artificial Intelligence and presents an impact of Artificial Intelligence in the present and future world