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Buchbeschreibung Zustand: New. Bestandsnummer des Verkäufers ABLIING23Mar3113020196674
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Buchbeschreibung Zustand: New. PRINT ON DEMAND Book; New; Fast Shipping from the UK. No. book. Bestandsnummer des Verkäufers ria9783639430912_lsuk
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Buchbeschreibung PF. Zustand: New. Bestandsnummer des Verkäufers 6666-IUK-9783639430912
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Buchbeschreibung Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Revision with unchanged content. We investigate the problem of descriptive learning--learning rules that describe the underlying structure of a domain--in rich, qualitative worlds. Previous approaches to this problem have searched for laws in top-down, enumerative fashion. We present algorithms that belong to an alternative, data-driven search paradigm. In our algorithms, search is guided not by relationships between the forms of the hypothesized rules, but by correlations in the data they represent. We exploit anomalies in this data, hypothesizing that that patterns that are unlikely to have arisen by chance must represent features of the domain. We describe data-driven methods that discover rules in both propositional and relational domains. We apply our methods to the problem of finding planning invariants: formulae that are true in every reachable state of a planning world. Our methods provide a novel inductive approach to this problem. They find invariants from just a few reachable-state descriptions. They discover laws comparable in quality and complexity to those discovered by specialized planning-invariant discovery systems that require a far greater deal of specialized knowledge about the domain. 200 pp. Englisch. Bestandsnummer des Verkäufers 9783639430912
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Buchbeschreibung PAP. Zustand: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000. Bestandsnummer des Verkäufers L0-9783639430912
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Buchbeschreibung Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Revision with unchanged content. We investigate the problem of descriptive learning--learning rules that describe the underlying structure of a domain--in rich, qualitative worlds. Previous approaches to this problem have searched for laws in top-down, enumerative fashion. We present algorithms that belong to an alternative, data-driven search paradigm. In our algorithms, search is guided not by relationships between the forms of the hypothesized rules, but by correlations in the data they represent. We exploit anomalies in this data, hypothesizing that that patterns that are unlikely to have arisen by chance must represent features of the domain. We describe data-driven methods that discover rules in both propositional and relational domains. We apply our methods to the problem of finding planning invariants: formulae that are true in every reachable state of a planning world. Our methods provide a novel inductive approach to this problem. They find invariants from just a few reachable-state descriptions. They discover laws comparable in quality and complexity to those discovered by specialized planning-invariant discovery systems that require a far greater deal of specialized knowledge about the domain. Bestandsnummer des Verkäufers 9783639430912
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Buchbeschreibung Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Mukherji ProshantoProshanto Mukherji grew up in Bombay, India. He received his BS in Computer Science from the New Jersey Institute of Technology in 2000, and his Masters and Ph.D. from the University of Rochester in 2000 and 2007 re. Bestandsnummer des Verkäufers 4987300
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Buchbeschreibung PAP. Zustand: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000. Bestandsnummer des Verkäufers L0-9783639430912
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