Other affiliations: Hitec
Bio: Wilfried Bohlken is an academic researcher from University of Hamburg. The author has contributed to research in topics: Ontology (information science) & Description logic. The author has an hindex of 5, co-authored 7 publications receiving 70 citations. Previous affiliations of Wilfried Bohlken include Hitec.
TL;DR: The general system architecture is introduced and some results in detail regarding hybrid reasoning and planning used in RACE are sketches, and instances of learning from the experiences of real robot task execution are sketched.
Abstract: 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.
••20 Sep 2011
TL;DR: This contribution presents a realtime activity monitoring system, called SCENIOR (SCEne Interpretation with Ontology-based Rules) with several innovative features, which is evaluated with real-life data of aircraft service activities.
Abstract: In this contribution we present a realtime activity monitoring system, called SCENIOR (SCEne Interpretation with Ontology-based Rules) with several innovative features. Activity concepts are defined in an ontology using OWL, extended by SWRL rules for the temporal structure, and are automatically transformed into a high-level scene interpretation system based on JESS rules. Interpretation goals are transformed into hierarchical hypotheses structures associated with constraints and embedded in a probabilistic scene model. The incremental interpretation process is organised as a Beam Search with multiple parallel interpretation threads. At each step, a context-dependent probabilistic rating is computed for each partial interpretation reflecting the probability of that interpretation to reach completion. Low-rated threads are discarded depending on the beam width. Fully instantiated hypotheses may be used as input for higher-level hypotheses, thus realising a doubly hierarchical recognition process. Missing evidence may be "hallucinated" depending on the context. The system has been evaluated with real-life data of aircraft service activities.
••04 Nov 2009
TL;DR: It is shown that the object-centered structure of the ontology can be transformed into a rule-based system in a native and systematic way, and the integration of constraints - which are essential for scene interpretation - is demonstrated with a temporal constraint net.
Abstract: In this paper, a novel architecture for high-level scene interpretation is introduced, which is based on the generation of rules from an OWL-DL ontology It is shown that the object-centered structure of the ontology can be transformed into a rule-based system in a native and systematic way Furthermore the integration of constraints - which are essential for scene interpretation - is demonstrated with a temporal constraint net, and it is shown how parallel computing of alternatives can be realised First results are given using examples of airport activities
••01 Jan 2013
TL;DR: A generic framework for model-based behaviour interpretation and its application to monitoring aircraft service activities is described and it has been designed to closely correspond to the compositional hierarchy of behaviour concepts.
Abstract: The authors describe a generic framework for model-based behaviour interpretation and its application to monitoring aircraft service activities. Behaviour models are represented in a standardised conceptual knowledge base using OWL-DL for concept definitions and the extension SWRL for constraints. The conceptual knowledge base is automatically converted into an operational scene interpretation system implemented in Java and JESS that accepts tracked objects as input and delivers high-level activity descriptions as output. The interpretation process employs Beam Search for exploring the interpretation space, guided by a probabilistic rating system. The probabilistic model cannot be efficiently represented in the ontology, but it has been designed to closely correspond to the compositional hierarchy of behaviour concepts. Experiments are described that demonstrate the system performance with real airport data.
••06 Mar 2014
TL;DR: It is shown that a high-level scene interpretation system, implemented as part of a comprehensive robotic system developed in the XXX project, can also be used for prediction, and can foresee possible developments of the environment and the effect they may have on its activities.
Abstract: Being able to predict events and occurrences which may arise from a current situation is a desirable capability of an intelligent agent. In this paper, we show that a high-level scene interpretation system, implemented as part of a comprehensive robotic system developed in the XXX project, can also be used for prediction. This way, the robot can foresee possible developments of the environment and the effect they may have on its activities. As a guiding example, we consider a robot acting as a waiter in a restaurant and the task of predicting possible occurrences and courses of action, e.g. when serving a coffee to a guest. Our approach requires that the robot possesses conceptual knowledge about occurrences in the restaurant and its own activities, represented in the standardized ontology language OWL and augmented by constraints using SWRL. Conceptual knowledge may be acquired by conceptualizing experiences collected in the robotâs memory. Predictions are generated by a model-construction process which seeks to explain evidence as parts of such conceptual knowledge, this way generating possible future developments. The experimental results show, among others, the prediction of possible obstacle situations and their effect on the robot actions and estimated execution times.
TL;DR: In this paper, it was shown 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.
Abstract: 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.
TL;DR: Wang et al. as discussed by the authors provided a comprehensive review of research on robotics in travel, tourism and hospitality, and identified research gaps and directions for future research, and analyzed 131 publications published during 1993-2019, identified via Scopus, Web of Science, ResearchGate, Academia.edu and Google Scholar.
Abstract: 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
TL;DR: Experimental results show that the proposed system is able to interact with human users, learn new object categories over time, as well as perform complex tasks.
Abstract: This paper presents an artificial cognitive system tightly integrating object perception and manipulation for assistive robotics. This is necessary for assistive robots, not only to perform manipulation tasks in a reasonable amount of time and in an appropriate manner, but also to robustly adapt to new environments by handling new objects. In particular, this system includes perception capabilities that allow robots to incrementally learn object categories from the set of accumulated experiences and reason about how to perform complex tasks. To achieve these goals, it is critical to detect, track and recognize objects in the environment as well as to conceptualize experiences and learn novel object categories in an open-ended manner, based on human–robot interaction. Interaction capabilities were developed to enable human users to teach new object categories and instruct the robot to perform complex tasks. A naive Bayes learning approach with a Bag-of-Words object representation are used to acquire and refine object category models. Perceptual memory is used to store object experiences, feature dictionary and object category models. Working memory is employed to support communication purposes between the different modules of the architecture. A reactive planning approach is used to carry out complex tasks. To examine the performance of the proposed architecture, a quantitative evaluation and a qualitative analysis are carried out. Experimental results show that the proposed system is able to interact with human users, learn new object categories over time, as well as perform complex tasks.
TL;DR: An object perception and perceptual learning system developed for a complex artificial cognitive agent working in a restaurant scenario that integrates detection, tracking, learning and recognition of tabletop objects and the Point Cloud Library is used in nearly all modules.
Abstract: This paper describes a 3D object perception and perceptual learning system developed for a complex artificial cognitive agent working in a restaurant scenario. This system, developed within the scope of the European project RACE, integrates detection, tracking, learning and recognition of tabletop objects. Interaction capabilities were also developed to enable a human user to take the role of instructor and teach new object categories. Thus, the system learns in an incremental and open-ended way from user-mediated experiences. Based on the analysis of memory requirements for storing both semantic and perceptual data, a dual memory approach, comprising a semantic memory and a perceptual memory, was adopted. The perceptual memory is the central data structure of the described perception and learning system. The goal of this paper is twofold: on one hand, we provide a thorough description of the developed system, starting with motivations, cognitive considerations and architecture design, then providing details on the developed modules, and finally presenting a detailed evaluation of the system; on the other hand, we emphasize the crucial importance of the Point Cloud Library (PCL) for developing such system.11This paper is a revised and extended version of Oliveira et?al. (2014). We describe an object perception and perceptual learning system.The system is able to detect, track and recognize tabletop objects.The system learns novel object categories in an open-ended fashion.The Point Cloud Library is used in nearly all modules of the system.The system was developed and used in the European project RACE.
TL;DR: An efficient approach capable of learning and recognizing object categories in an interactive and open-ended manner, which is able to interact with human users, learning new object categories continuously over time is presented.
Abstract: 3D object detection and recognition is increasingly used for manipulation and navigation tasks in service robots. It involves segmenting the objects present in a scene, estimating a feature descriptor for the object view and, finally, recognizing the object view by comparing it to the known object categories. This paper presents an efficient approach capable of learning and recognizing object categories in an interactive and open-ended manner. In this paper, “open-ended” implies that the set of object categories to be learned is not known in advance. The training instances are extracted from on-line experiences of a robot, and thus become gradually available over time, rather than at the beginning of the learning process. This paper focuses on two state-of-the-art questions: (1) How to automatically detect, conceptualize and recognize objects in 3D scenes in an open-ended manner? (2) How to acquire and use high-level knowledge obtained from the interaction with human users, namely when they provide category labels, in order to improve the system performance? This approach starts with a pre-processing step to remove irrelevant data and prepare a suitable point cloud for the subsequent processing. Clustering is then applied to detect object candidates, and object views are described based on a 3D shape descriptor called spin-image. Finally, a nearest-neighbor classification rule is used to predict the categories of the detected objects. A leave-one-out cross validation algorithm is used to compute precision and recall, in a classical off-line evaluation setting, for different system parameters. Also, an on-line evaluation protocol is used to assess the performance of the system in an open-ended setting. Results show that the proposed system is able to interact with human users, learning new object categories continuously over time.