Building a long term human-robot relationship: how emotional interaction plays a key role in attachment

The current research aims to develop a system for long-term relationships with a robot, based on emotional interaction. The first experiment of the research is to prove the hypothesis, which is, 'can a robot can engage with a user in a long-term relationship based on emotions?' This demonstrates the important aspects of human-human and human-robot interaction. This experiment is being conducted with the robot head called ERWIN (Emotional Robot With Interactive Networks), which is capable of simple prototypical emotions and will be conducted via a simple ‘wizard of oz’ procedure

A model for long-term human-robot interaction and relationships in a companion robot

Humans have different cognitive thoughts, own personality, traits, cognitive characteristics and certain anxiety which have large impact on their thoughts. Humans are not perfect, but cognitive characteristics make humans what they are, and when humans interact, their cognitive personality reflects on their characteristics behaviours. Humans like/ dislike each other depending on their cognitive characteristics and then relationship forms. But, in human-robot interactions, the robot usually lacks of human-like cognitive personality, mood & emotions and cognitive bias effect in its behavioural characteristics. The current research is solely inspired by human’s cognitive characteristics and interaction processes, and it aims to develop above human-like factors in autonomous robotic system and test it with human users to study how that will affect the human-robot interactions and long-term relationships

Determining the effect of human cognitive biases in social robots for human-robotm interactions

The research presented in this thesis describes a model for aiding human-robot interactions based on the principle of showing behaviours which are created based on 'human' cognitive biases by a robot in human-robot interactions. The aim of this work is to study how cognitive biases can affect human-robot interactions in the long term. Currently, most human-robot interactions are based on a set of well-ordered and structured rules, which repeat regardless of the person or social situation. This trend tends to provide an unrealistic interaction, which can make difficult for humans to relate ‘naturally’ with the social robot after a number of relations. The main focus of these interactions is that the social robot shows a very structured set of behaviours and, as such, acts unnaturally and mechanical in terms of social interactions. On the other hand, fallible behaviours (e.g. forgetfulness, inability to understand other’ emotions, bragging, blaming others) are common behaviours in humans and can be seen in regular social interactions. Some of these fallible behaviours are caused by the various cognitive biases. Researchers studied and developed various humanlike skills (e.g. personality, emotions expressions, traits) in social robots to make their behaviours more humanlike, and as a result, social robots can perform various humanlike actions, such as walking, talking, gazing or emotional expression. But common human behaviours such as forgetfulness, inability to understand other emotions, bragging or blaming are not present in the current social robots; such behaviours which exist and influence people have not been explored in social robots. The study presented in this thesis developed five cognitive biases in three different robots in four separate experiments to understand the influences of such cognitive biases in human–robot interactions. The results show that participants initially liked to interact with the robot with cognitive biased behaviours more than the robot without such behaviours. In my first two experiments, the robots (e.g., ERWIN, MyKeepon) interacted with the participants using a single bias (i.e., misattribution and empathy gap) cognitive biases accordingly, and participants enjoyed the interactions using such bias effects: for example, forgetfulness, source confusions, always showing exaggerated happiness or sadness and so on in the robots. In my later experiments, participants interacted with the robot (e.g., MARC) three times, with a time interval between two interactions, and results show that the likeness the interactions where the robot shows biased behaviours decreases less than the interactions where the robot did not show any biased behaviours. In the current thesis, I describe the investigations of these traits of forgetfulness, the inability to understand others’ emotions, and bragging and blaming behaviours, which are influenced by cognitive biases, and I also analyse people’s responses to robots displaying such biased behaviours in human–robot interactions

Effect of cognitive biases on human-robot interaction: a case study of robot's misattribution

This paper presents a model for developing long-term human-robot interactions and social relationships based on the principle of 'human' cognitive biases applied to a robot. The aim of this work is to study how a robot influenced with human ‘misattribution’ helps to build better human-robot interactions than unbiased robots. The results presented in this paper suggest that it is important to know the effect of cognitive biases in human characteristics and interactions in order to better understand how this plays a role in human-human social relationship development. The results presented in this paper show how a single cognitive memory bias i.e. misattribution in robot-human verbal communication allows for better human-robot interaction than similar robot-human communication without misattribution biases

The effects of cognitive biases and imperfectness in long-term robot-human interactions: case studies using five cognitive biases on three robots

The research presented in this paper demonstrates a model for aiding human-robot companionship based on the principle of 'human' cognitive biases applied to a robot. The aim of this work is to study how cognitive biases can affect human-robot companionship in long-time. In the current paper, we show comparative results of the experiments using five biased algorithms in three different robots such as ERWIN, MyKeepon and MARC. The results were analysed to determine what difference if any of biased vs unbiased interaction has on the interaction with the robot and if the participants were able to form any kind of ‘preference’ toward the different algorithms. The experimental presented show that the participants have more of a preference towards the biased algorithm interactions than the robot without the bias

Towards an imperfect robot for long-term companionship: case studies using cognitive biases

The research presented in this paper aims to find out what affect cognitive biases play in a robot’s interactive behaviour for the goal of developing human-robot long-term companionship. It is expected that by utilising cognitive biases in a robot’s interactive behaviours, making the robot cognitively imperfect, will affect how people relate to the robot thereby changing the process of long-term companionship. Previous research carried out in this area based on human-like cognitive characteristics in robots to create and maintain long-term relationship between robots and humans have yet to focus on developing human-like cognitive biases and as such is new to this application in robotics. To start working with cognitive biases ‘misattribution’ and ‘empathic gap’ have been selected which have been shown to be very common biases in humans and as such play a role on human-human interactions and long-term relationships

The effects of cognitive biases in long-term human-robot interactions: case studies using three cognitive biases on MARC the humanoid robot

The research presented in this paper is part of a wider study investigating the role cognitive bias plays in developing long-term companionship between a robot and human. In this paper we discuss, how cognitive biases such as misattribution, Empathy gap and Dunning-Kruger effects can play a role in robot-human interaction with the aim of improving long-term companionship. One of the robots used in this study called MARC (See Fig. 1) was given a series of biased behaviours such as forgetting participant’s names, denying its own faults for failures, unable to understand what a participant is saying, etc. Such fallible behaviours were compared to a non-biased baseline behaviour. In the current paper, we present a comparison of two case studies using these biases and a non-biased algorithm. It is hoped that such humanlike fallible characteristics can help in developing a more natural and believable companionship between Robots and Humans. The results of the current experiments show that the participants initially warmed to the robot with the biased behaviours

Robots that refuse to admit losing: a case study in game playing using self-serving bias in the humanoid robot MARC

Abstract. The research presented in this paper is part of a wider study investi-gating the role cognitive bias plays in developing long-term companionship between a robot and human. In this paper we discuss how the self-serving cog-nitive bias can play a role in robot-human interaction. One of the robots used in this study called MARC (See fig.1) was given a series of self-serving trait behaviours such as denying own faults for failures, blaming on others and bragging. Such fallible behaviours were compared to the robot’s non-biased friendly behaviours. In the current paper, we present comparisons of two case studies using the self-serving bias and a non-biased algorithm. It is hoped that such humanlike fallible characteristics can help in developing a more natural and believable companionship between Robots and Humans. The results of the current experiments show that the participants initially warmed to the robot with the self-serving traits

Multiplicity dependence of light (anti-)nuclei production in p–Pb collisions at sNN=5.02 TeV

The measurement of the deuteron and anti-deuteron production in the rapidity range −1 < y < 0 as a function of transverse momentum and event multiplicity in p–Pb collisions at √sNN = 5.02 TeV is presented. (Anti-)deuterons are identified via their specific energy loss dE/dx and via their time-of- flight. Their production in p–Pb collisions is compared to pp and Pb–Pb collisions and is discussed within the context of thermal and coalescence models. The ratio of integrated yields of deuterons to protons (d/p) shows a significant increase as a function of the charged-particle multiplicity of the event starting from values similar to those observed in pp collisions at low multiplicities and approaching those observed in Pb–Pb collisions at high multiplicities. The mean transverse particle momenta are extracted from the deuteron spectra and the values are similar to those obtained for p and particles. Thus, deuteron spectra do not follow mass ordering. This behaviour is in contrast to the trend observed for non-composite particles in p–Pb collisions. In addition, the production of the rare 3He and 3He nuclei has been studied. The spectrum corresponding to all non-single diffractive p-Pb collisions is obtained in the rapidity window −1 < y < 0 and the pT-integrated yield dN/dy is extracted. It is found that the yields of protons, deuterons, and 3He, normalised by the spin degeneracy factor, follow an exponential decrease with mass number