1,285 research outputs found

    Decision Support Systems

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    Decision support systems (DSS) have evolved over the past four decades from theoretical concepts into real world computerized applications. DSS architecture contains three key components: knowledge base, computerized model, and user interface. DSS simulate cognitive decision-making functions of humans based on artificial intelligence methodologies (including expert systems, data mining, machine learning, connectionism, logistical reasoning, etc.) in order to perform decision support functions. The applications of DSS cover many domains, ranging from aviation monitoring, transportation safety, clinical diagnosis, weather forecast, business management to internet search strategy. By combining knowledge bases with inference rules, DSS are able to provide suggestions to end users to improve decisions and outcomes. This book is written as a textbook so that it can be used in formal courses examining decision support systems. It may be used by both undergraduate and graduate students from diverse computer-related fields. It will also be of value to established professionals as a text for self-study or for reference

    Evaluation of human movement qualities: A methodology based on transferable-utility games on graphs.

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    Abstract A novel computational method for the analysis of expressive full-body movement qualities is introduced, which exploits concepts and tools from graph theory and game theory. The human skeletal structure is modeled as an undirected graph, where the joints are the vertices and the edge set contains both physical and nonphysical links. Physical links correspond to connections between adjacent physical body joints (e.g., the forearm, which connects the elbow to the wrist). Nonphysical links act as \u201cbridges\u201d between parts of the body not directly connected by the skeletal structure, but sharing very similar feature values. The edge weights depend on features obtained by using Motion Capture data. Then, a mathematical game is constructed over the graph structure, where the vertices represent the players and the edges represent communication channels between them. Hence, the body movement is modeled in terms of a game built on the graph structure. Since the vertices and the edges contribute to the overall quality of the movement, the adopted game-theoretical model is of cooperative nature. A game-theoretical concept, called Shapley value, is exploited as a centrality index to estimate the contribution of each vertex to a shared goal (e.g., to the way a particular movement quality is transferred among the vertices). The proposed method is applied to a data set of Motion Capture data of subjects performing expressive movements, recorded in the framework of the H2020-ICT-2015 EU Project WhoLoDance, Project no. 688865. Results are presented: development of novel method, contribution to the scientific community with a new data corpus, application the discussed method to 100 movement recordings and creation of database archive of stimuli for further use in research studies in the framework of the WhoLoDance Project

    Real-time generation and adaptation of social companion robot behaviors

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    Social robots will be part of our future homes. They will assist us in everyday tasks, entertain us, and provide helpful advice. However, the technology still faces challenges that must be overcome to equip the machine with social competencies and make it a socially intelligent and accepted housemate. An essential skill of every social robot is verbal and non-verbal communication. In contrast to voice assistants, smartphones, and smart home technology, which are already part of many people's lives today, social robots have an embodiment that raises expectations towards the machine. Their anthropomorphic or zoomorphic appearance suggests they can communicate naturally with speech, gestures, or facial expressions and understand corresponding human behaviors. In addition, robots also need to consider individual users' preferences: everybody is shaped by their culture, social norms, and life experiences, resulting in different expectations towards communication with a robot. However, robots do not have human intuition - they must be equipped with the corresponding algorithmic solutions to these problems. This thesis investigates the use of reinforcement learning to adapt the robot's verbal and non-verbal communication to the user's needs and preferences. Such non-functional adaptation of the robot's behaviors primarily aims to improve the user experience and the robot's perceived social intelligence. The literature has not yet provided a holistic view of the overall challenge: real-time adaptation requires control over the robot's multimodal behavior generation, an understanding of human feedback, and an algorithmic basis for machine learning. Thus, this thesis develops a conceptual framework for designing real-time non-functional social robot behavior adaptation with reinforcement learning. It provides a higher-level view from the system designer's perspective and guidance from the start to the end. It illustrates the process of modeling, simulating, and evaluating such adaptation processes. Specifically, it guides the integration of human feedback and social signals to equip the machine with social awareness. The conceptual framework is put into practice for several use cases, resulting in technical proofs of concept and research prototypes. They are evaluated in the lab and in in-situ studies. These approaches address typical activities in domestic environments, focussing on the robot's expression of personality, persona, politeness, and humor. Within this scope, the robot adapts its spoken utterances, prosody, and animations based on human explicit or implicit feedback.Soziale Roboter werden Teil unseres zukĂŒnftigen Zuhauses sein. Sie werden uns bei alltĂ€glichen Aufgaben unterstĂŒtzen, uns unterhalten und uns mit hilfreichen RatschlĂ€gen versorgen. Noch gibt es allerdings technische Herausforderungen, die zunĂ€chst ĂŒberwunden werden mĂŒssen, um die Maschine mit sozialen Kompetenzen auszustatten und zu einem sozial intelligenten und akzeptierten Mitbewohner zu machen. Eine wesentliche FĂ€higkeit eines jeden sozialen Roboters ist die verbale und nonverbale Kommunikation. Im Gegensatz zu Sprachassistenten, Smartphones und Smart-Home-Technologien, die bereits heute Teil des Lebens vieler Menschen sind, haben soziale Roboter eine Verkörperung, die Erwartungen an die Maschine weckt. Ihr anthropomorphes oder zoomorphes Aussehen legt nahe, dass sie in der Lage sind, auf natĂŒrliche Weise mit Sprache, Gestik oder Mimik zu kommunizieren, aber auch entsprechende menschliche Kommunikation zu verstehen. DarĂŒber hinaus mĂŒssen Roboter auch die individuellen Vorlieben der Benutzer berĂŒcksichtigen. So ist jeder Mensch von seiner Kultur, sozialen Normen und eigenen Lebenserfahrungen geprĂ€gt, was zu unterschiedlichen Erwartungen an die Kommunikation mit einem Roboter fĂŒhrt. Roboter haben jedoch keine menschliche Intuition - sie mĂŒssen mit entsprechenden Algorithmen fĂŒr diese Probleme ausgestattet werden. In dieser Arbeit wird der Einsatz von bestĂ€rkendem Lernen untersucht, um die verbale und nonverbale Kommunikation des Roboters an die BedĂŒrfnisse und Vorlieben des Benutzers anzupassen. Eine solche nicht-funktionale Anpassung des Roboterverhaltens zielt in erster Linie darauf ab, das Benutzererlebnis und die wahrgenommene soziale Intelligenz des Roboters zu verbessern. Die Literatur bietet bisher keine ganzheitliche Sicht auf diese Herausforderung: Echtzeitanpassung erfordert die Kontrolle ĂŒber die multimodale Verhaltenserzeugung des Roboters, ein VerstĂ€ndnis des menschlichen Feedbacks und eine algorithmische Basis fĂŒr maschinelles Lernen. Daher wird in dieser Arbeit ein konzeptioneller Rahmen fĂŒr die Gestaltung von nicht-funktionaler Anpassung der Kommunikation sozialer Roboter mit bestĂ€rkendem Lernen entwickelt. Er bietet eine ĂŒbergeordnete Sichtweise aus der Perspektive des Systemdesigners und eine Anleitung vom Anfang bis zum Ende. Er veranschaulicht den Prozess der Modellierung, Simulation und Evaluierung solcher Anpassungsprozesse. Insbesondere wird auf die Integration von menschlichem Feedback und sozialen Signalen eingegangen, um die Maschine mit sozialem Bewusstsein auszustatten. Der konzeptionelle Rahmen wird fĂŒr mehrere AnwendungsfĂ€lle in die Praxis umgesetzt, was zu technischen Konzeptnachweisen und Forschungsprototypen fĂŒhrt, die in Labor- und In-situ-Studien evaluiert werden. Diese AnsĂ€tze befassen sich mit typischen AktivitĂ€ten in hĂ€uslichen Umgebungen, wobei der Schwerpunkt auf dem Ausdruck der Persönlichkeit, dem Persona, der Höflichkeit und dem Humor des Roboters liegt. In diesem Rahmen passt der Roboter seine Sprache, Prosodie, und Animationen auf Basis expliziten oder impliziten menschlichen Feedbacks an

    Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation

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    This paper surveys the current state of the art in Natural Language Generation (NLG), defined as the task of generating text or speech from non-linguistic input. A survey of NLG is timely in view of the changes that the field has undergone over the past decade or so, especially in relation to new (usually data-driven) methods, as well as new applications of NLG technology. This survey therefore aims to (a) give an up-to-date synthesis of research on the core tasks in NLG and the architectures adopted in which such tasks are organised; (b) highlight a number of relatively recent research topics that have arisen partly as a result of growing synergies between NLG and other areas of artificial intelligence; (c) draw attention to the challenges in NLG evaluation, relating them to similar challenges faced in other areas of Natural Language Processing, with an emphasis on different evaluation methods and the relationships between them.Comment: Published in Journal of AI Research (JAIR), volume 61, pp 75-170. 118 pages, 8 figures, 1 tabl

    Conceiving “personality”: Psychologist’s challenges and basic fundamentals of the Transdisciplinary Philosophy-of-Science Paradigm for Research on Individuals

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    Scientists exploring individuals, as such scientists are individuals themselves and thus not independent from their objects of research, encounter profound challenges; in particular, high risks for anthropo-, ethno- and ego-centric biases and various fallacies in reasoning. The Transdisciplinary Philosophy-of-Science Paradigm for Research on Individuals (TPS-Paradigm) aims to tackle these challenges by exploring and making explicit the philosophical presuppositions that are being made and the metatheories and methodologies that are used in the field. This article introduces basic fundamentals of the TPS-Paradigm including the epistemological principle of complementarity and metatheoretical concepts for exploring individuals as living organisms. Centrally, the TPS-Paradigm considers three metatheoretical properties (spatial location in relation to individuals’ bodies, temporal extension, and physicality versus “non-physicality”) that can be conceived in different forms for various kinds of phenomena explored in individuals (morphology, physiology, behaviour, the psyche, semiotic representations, artificially modified outer appearances and contexts). These properties, as they determine the phenomena’s accessibility in everyday life and research, are used to elaborate philosophy-of-science foundations and to derive general methodological implications for the elementary problem of phenomenon-methodology matching and for scientific quantification of the various kinds of phenomena studied. On the basis of these foundations, the article explores the metatheories and methodologies that are used or needed to empirically study each given kind of phenomenon in individuals in general. Building on these general implications, the article derives special implications for exploring individuals’ “personality”, which the TPS-Paradigm conceives of as individual-specificity in all of the various kinds of phenomena studied in individuals
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