Recent work has demonstrated that many social networks, and indeed many networks of other types also, have broad distributions of vertex degree. Here we show that this has a substantial impact on the shape of ego-centered networks, i.e., sets of network vertices that are within a given distance of a specified central vertex, the ego. This in turn affects concepts and methods based on ego-centered networks, such as snowball sampling and the "ripple effect". In particular, we argue that one's acquaintances, one's immediate neighbors in the acquaintance network, are far from being a random sample of the population, and that this biases the numbers of neighbors two and more steps away. We demonstrate this concept using data drawn from academic collaboration networks, for which, as we show, current simple theories for the typical size of ego-centered networks give numbers that differ greatly from those measured in reality. We present an improved theoretical model which gives significantly better results.

Ego-centered networks and the ripple effect

This paper aims to provide a comprehensive review of research on robotics in travel, tourism and hospitality, and to identify research gaps and directions for future research.,This paper analyzes 131 publications published during 1993-2019, identified via Scopus, Web of Science, ResearchGate, Academia.edu and Google Scholar. It offers quantitative analysis of frequencies and cross-tables and qualitative thematic analysis of the publications within each of seven identified domains.,The paper identifies “Robot,” “Human,” “Robot manufacturer,” “Travel/tourism/hospitality company,” “Servicescape,” “External environment” and “Education, training and research” as the research domains. Most research studies are dedicated to robots in restaurants, airports, hotels and bars. Papers tend to apply engineering methods, but experiments and surveys grow in popularity. Asia-Pacific countries account for much of the empirical research.,The analysis was limited to publications indexed in four databases and one search engine. Only publications in English were considered. Growing opportunities for those who are anxious to publish in the field are identified. Importantly, emerging research is branching out from the engineering of robots to the possibilities for human/robot interactions and their use for service providers, opening up new avenues of research for tourism and hospitality scholars.,The paper identified a myriad of application areas for robots across various tourism and hospitality sectors. Service providers must critically think about how robots affect the servicescape and how it needs to be adjusted or re-imagined to ensure that robots and employees can augment the service experiences (co-)created within it.,This is the first study to systematically analyze research publications on robotics in travel, tourism and hospitality.,本论文全面评论了在旅游酒店业中的机器人技术的研究, 并指出文献缺口和未来研究方向。,本论文分析了在1993年至2019年发布在Scopus、Web of Science、ResearchGate、Academia.edu、和Google Scholar的131篇文献。本论文对文献做了一系列定量分析, 包括频率分析、交叉表、定性文本分析、在七大确立的领域中对每个领域的文献进行分析。,本论文确立了七个研究领域：机器人、人类、机器生产者、旅游酒店企业、Servicescape、外部环境、和教育培训和研究。大多数文献集中在对饭店、机场、酒店、和酒吧的机器人研究。文献往往采用工程手段进行研究, 但是实验和问卷方式正在呈增长趋势。亚太国家占据大多数实证研究作品。,本论文样本库局限于四个数据库和一个搜索引擎。只有英文文献被采样。本论文为对相关领域感兴趣的学者指出研究方向。重要的是, 本论文发现用工程角度研究机器人的文献有了分支, 有一小部分文献开始着手研究人/机器人交互和其在服务过程中的使用的研究, 这对旅游酒店学者提供新研究视角。,本论文指出了一系列有关旅游酒店领域中机器人的应用。服务商必须重视机器人如何影响Servicescape以及如何审视机器人与人的交互, 确保其与员工加强消费者的服务体验（价值共创）。,本论文是首篇系统评论旅游酒店领域中机器人研究文献的文章。,关键词：机器人、机器人经济、机器人设计、机器人使用、Servicescape、rService、人-机器人交互、研究议程

Progress on robotics in hospitality and tourism: a review of the literature

Systems simulation - the art and science

This article presents Kinova's modular robotic systems, including the robots JACO2 and MICO2, actuators and grippers. Kinova designs and manufactures robotics platforms and components that are simple, sexy and safe under two business units: Assistive Robotics empowers people living with disabilities to push beyond their current boundaries and limitations while Service Robotics empowers people in industry to interact with their environment more efficiently and safely. Kinova is based in Boisbriand, Quebec, Canada. Its technologies are exploited in over 25 countries and are used in many applications, including as service robotics, physical assistance, medical applications, mobile manipulation, rehabilitation, teleoperation and in research in different areas such as computer vision, artificial intelligence, grasping, planning and control interfaces. The article describes Kinova's hardware platforms, their different control modes position, velocity and torque, control features and possible control interfaces. Integration to other systems and application examples are also presented.

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Kinova Modular Robot Arms for Service Robotics Applications

This book includes the thoroughly refereed post-conference proceedings of the 15th Annual RoboCup International Symposium, held in Istanbul, Turkey, in July 2011. The 12 revised papers and 32 poster presentation presented were carefully reviewed and selected from 97 submissions. The papers are orginazed on topical sections on robot hardware and software, perception and action, robotic cognition and learning, multi-robot systems, human-robot interaction, education and edutainment and applications.

RoboCup 2011: Robot Soccer World Cup XV

This paper reports on the aims, the approach, and the results of the European project RACE. The project aim was to enhance the behavior of an autonomous robot by having the robot learn from conceptualized experiences of previous performance, based on initial models of the domain and its own actions in it. This paper introduces the general system architecture; it then sketches some results in detail regarding hybrid reasoning and planning used in RACE, and instances of learning from the experiences of real robot task execution. Enhancement of robot competence is operationalized in terms of performance quality and description length of the robot instructions, and such enhancement is shown to result from the RACE system.

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The RACE Project : Robustness by Autonomous Competence Enhancement

Intelligent service robots should be able to improve their knowledge from accumulated experiences through continuous interaction with the environment, and in particular with humans. A human user may guide the process of experience acquisition, teaching new concepts, or correcting insufficient or erroneous concepts through interaction. This paper reports on work towards interactive learning of objects and robot activities in an incremental and open-ended way. In particular, this paper addresses human-robot interaction and experience gathering. The robot's ontology is extended with concepts for representing human-robot interactions as well as the experiences of the robot. The human-robot interaction ontology includes not only instructor teaching activities but also robot activities to support appropriate feedback from the robot. Two simplified interfaces are implemented for the different types of instructions including the teach instruction, which triggers the robot to extract experiences. These experiences, both in the robot activity domain and in the perceptual domain, are extracted and stored in memory, and they are used as input for learning methods. The functionalities described above are completely integrated in a robot architecture, and are demonstrated in a PR2 robot.

Interactive teaching and experience extraction for learning about objects and robot activities

Learning and deliberation are required to endow a robot with the capabilities to acquire knowledge, perform a variety of tasks and interactions, and adapt to open-ended environments. This paper explores the notion of experience-based planning domains (EBPDs) for task-level learning and planning in robotics. EBPDs rely on methods for a robot to: (i) obtain robot activity experiences from the robot's performance; (ii) conceptualize each experience to a task model called activity schema; and (iii) exploit the learned activity schemata to make plans in similar situations. Experiences are episodic descriptions of plan-based robot activities including environment perception, sequences of applied actions and achieved tasks. The conceptualization approach integrates different techniques including deductive generalization, abstraction and feature extraction to learn activity schemata. A high-level task planner was developed to find a solution for a similar task by following an activity schema. In this paper, we extend our previous approach by integrating goal inference capabilities. The proposed approach is illustrated in a restaurant environment where a service robot learns how to carry out complex tasks.

Experience-based robot task learning and planning with goal inference

Learning from experiences is an effective approach to enhance robot’s competence. This paper focuses on developing capabilities for a robot to obtain robot activity experiences and conceptualize the experiences as plan schemata, which are used as heuristics for the robot to make plans in similar situations. The plan-based robot activity experiences are obtained through human-robot interactions where a teaching action from a command-line user interface triggers recording of an experience. To represent human-robot interaction activities, ontologies for experiences and user instructions are integrated into a robot ontology. Recorded experiences are episodic descriptions of the robot’s activities including relevant perceptions of the environment, the goals pursued, successes, and failures. Since the amount of experience data is large, a graph simplification algorithm based on ego networks is investigated to filter out irrelevant information in an experience. Finally, an approach to robot activity conceptualization based on deductive generalization and abstraction is presented. The proposed system was demonstrated in a scenario where a PR2 robot is taught how to “serve a coffee” to a guest, in the EU project RACE.

Gathering and Conceptualizing Plan-Based Robot Activity Experiences

Opponent Modeling is one of the most attractive and practical arenas in Multi Agent System (MAS) for predicting and identifying the future behaviors of opponent This paper introduces an approach towards opponent modeling in RoboCup Soccer Coach Simulation In this scene, an autonomous coach agent is able to identify the weaknesses or patterns of the opponent by analyzing the opponent's past games and advising own players To gain this goal, we introduce a 3-tier learning architecture At first, by gathering data from the environment, sequential events of the players are identified Then the weaknesses or patterns of the opponent are predicted using statistical calculations Eventually, by comparing the opponent patterns with the rest of team's behavior, a model of the opponent is constructed According to this architecture, coach models the opponent and to simplify pattern recognition, provides an appropriate strategy to play against the opponent This structure is tested in RoboCup Soccer Coach Simulation and MRLCoach was the champion at Iran Open 2006

Vahid Mokhtari

Papers

The RACE Project : Robustness by Autonomous Competence Enhancement

Interactive teaching and experience extraction for learning about objects and robot activities

Experience-based robot task learning and planning with goal inference

Gathering and Conceptualizing Plan-Based Robot Activity Experiences

Mining Opponent Behavior: A Champion of RoboCup Coach Competition