A Bionic Model for Human-Like Machine Perception

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A Bionic Model for Human-Like Machine Perception Die approbierte Originalversion dieser Dissertation ist an der Hauptbibliothek der Technischen Universität Wien aufgestellt (http://www.ub.tuwien.ac.at). The approved original version of this thesis is available at the main library of the Vienna University of Technology (http://www.ub.tuwien.ac.at/englweb/). DISSERTATION A Bionic Model for Human-like Machine Perception Submitted at the Faculty of Electrical Engineering, Vienna University of Technology in partial fulfillment of the requirements for the degree of Doctor of Technical Sciences under supervision of Prof. Dr. Dietmar Dietrich Institute number: 384 Institute of Computer Technology and Prof. Dr. Peter Palensky Department of Electrical, Electronic and Computer Engineering University of Pretoria and Prof. Dr. Wolfgang Kastner Institute number: 183/1 Institute of Computer Aided Automation by Dipl.-Ing. Rosemarie Velik Matr.Nr.: 0125411 Lassendorf 43 A-9064 Pischeldorf Vienna, 11.4.2008 Abstract Machine perception is a research field that is still in its infancy and is confronted with many unsolved problems. In contrast, humans generally perceive their environment without problems. For the work at hand, these facts were the motivation to develop a bionic model for human-like machine perception, which is based on neuroscientific and neuropsychological research findings about the structural organization and function of the perceptual system of the human brain. Having systems available that are capable of a human-like perception of their environment would allow the automation of processes for which, today, human observers and their cognitive abilities are necessary. Potential applications are, among others, security and safety surveillance of public and private buildings and the automatic observation of the state of health of persons in retirement homes and hospitals. Furthermore, autonomous robots and interactive environments would take advantage of more effective mechanisms to perceive their surrounding. The introduced model is designated for applications in the field of building automation for au- tonomous monitoring and surveillance systems to observe objects, events, scenarios, and situations in buildings. Therefore, buildings have to be equipped with a huge number of diverse sensors. The challenge is to merge and interpret the information coming from these different sources. For this purpose, an information processing principle called neuro-symbolic information processing is introduced using neuro-symbols as basic information processing units. The inspiration for the utilization of neuro-symbols comes from the fact that humans think in terms of symbols, which emerge from information processed by neurons. Neuro-symbols are connected in a modular hierarchical fashion to a so-called neuro-symbolic network to process sensor data. The architecture of the neuro-symbolic network is derived from the structural organization of the perceptual system of the human brain. Connections and correlations between neuro-symbols can be acquired from examples in different learning phases. Besides sensor data processing, memory, knowledge, and focus of attention influence perception to resolve ambiguous sensory information and to devote processing power to relevant features. The introduced model was implemented with AnyLogic. The model proved to be successful in perceiving all test cases specified in the simulation environment. Furthermore, the insights gained during development allowed it to draw certain conclusions about the inconsistency or incomplete- ness of neuroscientific and neuropsychological theories including issues like the binding problem, the processing and storage of perceptual information, the computation of location information, and stability considerations. I Kurzfassung “Machine Perception” ist ein junges Forschungsgebiet, das mit vielen ungel¨osten Problemen kon- frontiert ist. In Gegensatz zu Maschinen k¨onnen Menschen ihre Umgebung im Allgemeinen m¨uhelos wahrnehmen. Diese beiden Tatsachen waren ausschlaggebend, um im Rahmen der vor- liegenden Arbeit ein bionisches Modell f¨ur menschen¨ahnliche Wahrnehmung zu entwickeln. Dieses Modell beruht auf neurowissenschaftlichen und neuropsychologischen Forschungserkenntnissen ¨uber die strukturelle Organisation und Funktion des menschlichen Wahrnehmungssystems. Ein technisches System mit menschen¨ahnlichem Wahrnehmungsverm¨ogen w¨urde es erlauben, eine Vielzahl von Prozessen zu automatisieren, f¨ur die bis jetzt immer noch menschliche Beobachter und deren kognitive F¨ahigkeiten notwendig sind. Potentielle Anwendungsbereiche sind die Sicher- heits¨uberwachung von ¨offentlichen und privaten Geb¨auden und die Beobachtung des Gesundheits- zustands von Personen in Krankenh¨ausern oder Altenwohnheimen. Abgesehen davon w¨urden au- tonome Robotersysteme und interaktive Umgebungen von effektiveren Mechanismen zur Wahr- nehmung ihres Umfeldes profitieren. Das entwickelte Modell ist im Bereich der Geb¨audeautomatisierung f¨ur autonome Uberwachungs-¨ systeme vorgesehen, um Objekte, Ereignisse, Szenarien und Situationen in Geb¨auden zu beobach- ten. Zu diesem Zweck m¨ussen Geb¨aude mit einer Vielzahl verschiedener Sensoren ausgestattet werden. Die Herausforderung besteht darin, Information von diesen Quellen zu kombinieren und zu interpretieren. Daf¨ur wird ein Informationsverarbeitungsprinzip genannt neuro-symbolische Informationsverarbeitung eingef¨uhrt. Dieses verwendet Neuro-Symbole als elementare Informa- tionsverarbeitungseinheiten. Die Verwendung von Neuro-Symbolen ist von der Tatsache inspi- riert, dass Menschen in Form von Symbolen denken, welche jedoch aus einer neuronalen Informa- tionsverarbeitung resultieren. Um Sensordaten zu verarbeiten, werden Neuro-Symbole zu einem so genannten Neuro-Symbolischen Netzwerk verbunden, welches eine modulare und hierarchi- sche Struktur aufweist, die vom Aufbau des menschlichen Wahrnehmungssystems abgeleitet ist. Verbindungen und Zusammenh¨ange zwischen Neuro-Symbolen k¨onnen aus Beispielen in einer Reihe von Lernphasen ermittelt werden. Neben der Sensordatenverarbeitung beeinflussen die Mechanismen Memory, Knowledge und Focus of Attention die Wahrnehmung, um zweideutige Sensorinformation behandeln und Rechnerkapazit¨aten auf relevante Merkmale konzentrieren zu k¨onnen. Das vorgestellte Modell wurde mit AnyLogic implementiert und erwies sich als erfolgreich bei der Erkennung aller spezifizierten Testf¨alle. Des Weiteren erlaubten die w¨ahrend der Entwicklung gewonnenen Erkenntnisse bestimmte R¨uckschl¨usse ¨uber die Inkonsistenz oder Unvollst¨andigkeit neurowissenschaftlicher und neuropsychologischer Modellvorstellungen. Diese beziehen sich unter anderem auf das sogenannte Binding Problem, auf die Verarbeitung und Speicherung von Wahr- nehmungsbildern im Allgemeinen und Ortsinformation im Speziellen, sowie auf Stabilit¨atsbe- trachtungen. II Preface The topic of this thesis lies in the field of cognitive science, cognitive computing, and cognitive automation and aims to develop a bionic model for human-like machine perception based on neuroscientific and neuropsychological research findings. To understand the intricacy of the tasks attempted by these research fields, a brief retrospect of history shall be given first: The fields just mentioned overlap with the research field of artificial intelligence (AI). About 50 years ago, in the fifties, this research field started to evolve with the aim to build intelligent machines. The first years of AI were marked by a strong optimism. It was believed that computers would soon be able to think and reason in a similar effective manner as humans do. However, at the end of the sixties, it got clear that making computers think – even on a childlike level – is an extremely complex problem. Therefore, researchers started to focus on far more simple problems like reacting to situations by using certain rules. Until today, there does not exist any technical system that can even nearly compete with the capacity and capabilities of the human mind. Within the last years, several research groups recognized that the reduced approaches often focused on in current AI projects cannot lead to technical systems with skills and capabilities comparable to human mental abilities. The AI researcher Marvin Minsky postulates that, like at the beginning of artificial intelligence research, findings how natural intelligence works should be the basis for the development of concepts for artificial intelligence [Min06]. This is the aim of the research fields of cognitive science and cognitive computing. Cognitive automation aims to automate cognitive activities that are currently performed by human operators. At the Institute of Computer Technology of the Vienna University of Technology, in the year 2000, Prof. Dr. techn. Dietmar Dietrich formed a research group of interdisciplinary researches with the aim to model and implement functions of the human brain into technical systems. These approaches are guided by insights from neuroscience, neuropsychology, and neuro-psychoanalysis about the human brain and mind. This thesis is a part of this research project and focuses on the perceptual system and the perceptual capabilities of the human brain. III Figure 1: Structure of Work Figure 1 illustrates the structure and organization of this work graphically. In chapter 1, which is the introductory chapter, the challenges of machine perception are outlined as well as characteris- tics of human perception are pointed out. The aim of the work is specified in detail, and possible applications and implications are
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