Human-Machine Cooperative Decision Making

Diese Dissertation beschäftigt sich mit der gemeinsamen Entscheidungsfindung in der Mensch-Maschine-Kooperation und liefert neue Erkenntnisse, welche von der theoretischen Modellierung bis zu experimentellen Untersuchungen reichen. Zunächst wird eine methodische Klassifikation bestehender Forschung zur Mensch-Maschine-Kooperation vorgenommen und der Forschungsfokus dieser Dissertation mithilfe eines vorgestellten Taxonomiemodells der Mensch-Maschine-Kooperation, dem Butterfly-Modell, abgegrenzt. Darauffolgend stellt die Dissertation zwei mathematische Verhaltensmodelle der gemeinsamen Entscheidungsfindung von Mensch und Maschine vor: das Adaptive Verhandlungsmodell und den n-stufigen War of Attrition. Beide modellieren den Einigungsprozess zweier emanzipierter Kooperationspartner und unterscheiden sich hinsichtlich ihrer Ursprünge, welche in der Verhandlungs- beziehungsweise Spieltheorie liegen. Zusätzlich wird eine Studie vorgestellt, die die Eignung der vorgeschlagenen mathematischen Modelle zur Beschreibung des menschlichen Nachgebeverhaltens in kooperativen Entscheidungsfindungs-Prozessen nachweist. Darauf aufbauend werden zwei modellbasierte Automationsdesigns bereitgestellt, welche die Entwicklung von Maschinen ermöglichen, die an einem Einigungsprozess mit einem Menschen teilnehmen können. Zuletzt werden zwei experimentelle Untersuchungen der vorgeschlagenen Automationsdesigns im Kontext von teleoperierten mobilen Robotern in Such- und Rettungsszenarien und anhand einer Anwendung in einem hochautomatisierten Fahrzeug präsentiert. Die experimentellen Ergebnisse liefern empirische Evidenz für die Überlegenheit der vorgestellten modellbasierten Automationsdesigns gegenüber den bisherigen Ansätzen in den Aspekten der objektiven kooperativen Performanz, des menschlichen Vertrauens in die Interaktion mit der Maschine und der Nutzerzufriedenheit. So zeigt diese Dissertation, dass Menschen eine emanzipierte Interaktion mit Bezug auf die Entscheidungsfindung bevorzugen, und leistet einen wertvollen Beitrag zur vollumfänglichen Betrachtung und Verwirklichung von Mensch-Maschine-Kooperationen

Human-Machine Cooperative Decision Making

The research reported in this thesis focuses on the decision making aspect of human-machine cooperation and reveals new insights from theoretical modeling to experimental evaluations: Two mathematical behavior models of two emancipated cooperation partners in a cooperative decision making process are introduced. The model-based automation designs are experimentally evaluated and thereby demonstrate their benefits compared to state-of-the-art approaches

Human-Machine Cooperative Decision Making

The research reported in this thesis focuses on the decision making aspect of human-machine cooperation and reveals new insights from theoretical modeling to experimental evaluations: Two mathematical behavior models of two emancipated cooperation partners in a cooperative decision making process are introduced. The model-based automation designs are experimentally evaluated and thereby demonstrate their benefits compared to state-of-the-art approaches

Experts Playing the Traveler's Dilemma

We analyze a one-shot experiment on the traveler's dilemma in which members of the Game Theory Society, were asked to submit both a (possibly mixed) strategy and their belief concerning the average strategy of their opponents. Very few entrants expect and play the unique Nash equilibrium, while we observe a fifth playing the cooperative solution of the game, i.e. a strictly dominated strategy. The experimental data suggest to analyze the game as one of incomplete information. Most strategies observed are in the support of its Bayesian Nash equilibria. A notable exception is the Nash equilibrium strategy of the original game.Traveler's Dilemma; Experiment; Experts; Incomplete Information

Driving style recognition for intelligent vehicle control and advanced driver assistance: a survey

Driver driving style plays an important role in vehicle energy management as well as driving safety. Furthermore, it is key for advance driver assistance systems development, toward increasing levels of vehicle automation. This fact has motivated numerous research and development efforts on driving style identification and classification. This paper provides a survey on driving style characterization and recognition revising a variety of algorithms, with particular emphasis on machine learning approaches based on current and future trends. Applications of driving style recognition to intelligent vehicle controls are also briefly discussed, including experts' predictions of the future development

The limits of commodification arguments: framing, motivation crowding, and shared valuations

I connect commodification arguments to an empirical literature, present a mechanism by which commodification may occur, and show how this may restrict the range of goods and services that are subject to commodification, therefore having implications for the use of commodification arguments in political theory. Commodification arguments assert that some people’s trading a good or service can debase it for third parties. They consist of a normative premise, a theory of value, and an empirical premise, a mechanism whereby some people’s market exchange affects how goods can be valued by others. Hence, their soundness depends on the existence of a suitable candidate mechanism for the empirical premise. The ‘motivation crowding effect’ has been cited as the empirical base of commodification. I show why the main explanations of motivation crowding – signaling and over-justification – do not provide mechanisms that could underpin the empirical premise. In doing this, I reveal some requirements on any candidate mechanism. I present a third explanation of motivation crowding, based on the crowding out of frames, and show how it fulfills the requirements. With a mechanism in hand, I explore the type of goods and services to which commodification arguments are applicable. The mechanism enables markets to break down ‘shared valuations’, which is a subset of the valuations that proponents of commodification arguments are concerned with. Further, it can only break down relatively fragile shared understandings and therefore, I suggest, it cannot support a commodification argument regarding the sale of sexual services