Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

The theory of affordances

The quarterly journal of experimental psychology

We study the average cost Linear Quadratic (LQ) control problem with unknown model parameters, also known as the adaptive control problem in the control community. We design an algorithm and prove that apart from logarithmic factors its regret up to time T is O( p T ). Unlike previous approaches that use a forced-exploration scheme, we construct a high-probability condence set around the model parameters and design an algorithm that plays optimistically with respect to this condence set. The construction of the condence set is based on the recent results from online least-squares estimation and leads to improved worst-case regret bound for the proposed algorithm. To the best of our knowledge this is the the rst time that a regret bound is derived for the LQ control problem.

/pdf/regret-bounds-for-the-adaptive-control-of-linear-quadratic-4r60v290d0.pdf

Regret Bounds for the Adaptive Control of Linear Quadratic Systems

Conversational agents (CAs) are software-based systems designed to interact with humans using natural language and have attracted considerable research interest in recent years. Following the Computers Are Social Actors paradigm, many studies have shown that humans react socially to CAs when they display social cues such as small talk, gender, age, gestures, or facial expressions. However, research on social cues for CAs is scattered across different fields, often using their specific terminology, which makes it challenging to identify, classify, and accumulate existing knowledge. To address this problem, we conducted a systematic literature review to identify an initial set of social cues of CAs from existing research. Building on classifications from interpersonal communication theory, we developed a taxonomy that classifies the identified social cues into four major categories (i.e., verbal, visual, auditory, invisible) and ten subcategories. Subsequently, we evaluated the mapping between the identified social cues and the categories using a card sorting approach in order to verify that the taxonomy is natural, simple, and parsimonious. Finally, we demonstrate the usefulness of the taxonomy by classifying a broader and more generic set of social cues of CAs from existing research and practice. Our main contribution is a comprehensive taxonomy of social cues for CAs. For researchers, the taxonomy helps to systematically classify research about social cues into one of the taxonomy's categories and corresponding subcategories. Therefore, it builds a bridge between different research fields and provides a starting point for interdisciplinary research and knowledge accumulation. For practitioners, the taxonomy provides a systematic overview of relevant categories of social cues in order to identify, implement, and test their effects in the design of a CA.

A Taxonomy of Social Cues for Conversational Agents

As witnessed in several behavioural studies, a complex relationship exists between people’s cultural background and their general acceptance towards robots. However, very few studies have investigated whether a robot’s original language and gesture based on certain culture have an impact on the people of the different cultures. The purpose of this work is to provide experimental evidence which supports the idea that humans may accept more easily a robot that can adapt to their specific culture. Indeed, improving acceptance and reducing discomfort is fundamental for future deployment of robots as assistive, health-care or companion devices into a society. We conducted a Human- Robot Interaction experiment both in Egypt and in Japan. Human subjects were engaged in a simulated video conference with robots that were greeting and speaking either in Arabic or in Japanese. The subjects completed a questionnaire assessing their preferences and their emotional state, while their spontaneous reactions were recorded in different ways. The results suggest that Egyptians prefer the Arabic robot, while they feel a sense of discomfort when interacting with the Japanese robot; the opposite is also true for the Japanese. These findings confirm the importance of the localisation of a robot in order to improve human acceptance during social human-robot interaction

/pdf/cross-cultural-study-on-human-robot-greeting-interaction-318oi7ivec.pdf

Cross-cultural study on human-robot greeting interaction: acceptance and discomfort by Egyptians and Japanese

This paper provides a new concept for the improvement of human-robot interaction HRI models: 'emotional affordances'. Emotional affordances are all the mechanisms that have emotional content as a way to transmit and/or collect emotional meaning about any context; it can include bodily expressions, social norms, values-laden objects or extended space, among others. With this rich concept, we open the way to new ways to understand the multimodal and complex nature of emotional mechanisms. Based on the grounded emotional mechanisms of human cognition and behaviour that is, based and result of the bodily structure and its coupled relationship with the natural and/or social environment, the purpose of this paper is focused on the definition of a framework for the design of a taxonomy of emotional affordances, useful for a modal and improved understanding of the domains of emotional interactions that can emerge between humans and robots. This process will make possible in next research steps to define processing modules as well as to elicit visual display outputs expressing emotions. Consequently, with this project we provide robotic experts with a unified taxonomy of human emotional affordances, useful for the improvement of HRI projects.

Emotional affordances for human-robot interaction

Purpose
Fibre optic colonoscopy is usually performed with manual introduction and advancement of the endoscope, but there is potential for a robot capable of locomoting autonomously from the rectum to the caecum. A prototype robot was designed and tested.

Development of a colon endoscope robot that adjusts its locomotion through the use of reinforcement learning

In the design of robots that have to share spaces with humans, design plays an important role in the acceptance, and sound is one further aspect that should not be neglected, in order to facilitate interaction and minimise repulsion. Following a previous research in which an uncomfortable noise from a robot was identified, we present an interaction study in which the same noise was tested in order to measure the effect on proxemics, as opposed to a silent condition and to a different version of the sound, masked with ambient music. The results obtained with the participation of 60 subjects show the effectiveness of the mask in avoiding the negative effect of the noise.

The Sound or Silence: Investigating the Influence of Robot Noise on Proxemics

https://www.mdpi.com/2218-6581/7/3/50/pdf

Gabriele Trovato

Papers

Cross-cultural study on human-robot greeting interaction: acceptance and discomfort by Egyptians and Japanese

Emotional affordances for human-robot interaction

Development of a colon endoscope robot that adjusts its locomotion through the use of reinforcement learning

The Sound or Silence: Investigating the Influence of Robot Noise on Proxemics

She’s Electric—The Influence of Body Proportions on Perceived Gender of Robots across Cultures