000			02737cam a2200277zu 4500
001			88865309
003			FRCYB88865309
005			20250107153651.0
006			m o d
007			cr un
008			250107s2019 fr | o|||||0|0|||eng d
020			_a9780262038942
035			_aFRCYB88865309
040			_aFR-PaCSA _ben _c _erda
100	1		_aForbus, Kenneth D.
245	0	1	_aQualitative Representations _bHow People Reason and Learn about the Continuous World _c['Forbus, Kenneth D.']
264		1	_bMIT Press _c2019
300			_a p.
336			_btxt _2rdacontent
337			_bc _2rdamdedia
338			_bc _2rdacarrier
650		0	_a
700	0		_aForbus, Kenneth D.
856	4	0	_2Cyberlibris _uhttps://international.scholarvox.com/netsen/book/88865309 _qtext/html _a
520			_aAn argument that qualitative representations?symbolic representations that carve continuous phenomena into meaningful units?are central to human cognition.In this book, Kenneth Forbus proposes that qualitative representations hold the key to one of the deepest mysteries of cognitive science: how we reason and learn about the continuous phenomena surrounding us. Forbus argues that qualitative representations?symbolic representations that carve continuous phenomena into meaningful units?are central to human cognition. Qualitative representations provide a basis for commonsense reasoning, because they enable practical reasoning with very little data; this makes qualitative representations a useful component of natural language semantics. Qualitative representations also provide a foundation for expert reasoning in science and engineering by making explicit the broad categories of things that might happen and enabling causal models that help guide the application of more quantitative knowledge as needed. Qualitative representations are important for creating more human-like artificial intelligence systems with capabilities for spatial reasoning, vision, question answering, and understanding natural language.Forbus discusses, among other topics, basic ideas of knowledge representation and reasoning; qualitative process theory; qualitative simulation and reasoning about change; compositional modeling; qualitative spatial reasoning; and learning and conceptual change. His argument is notable both for presenting an approach to qualitative reasoning in which analogical reasoning and learning play crucial roles and for marshaling a wide variety of evidence, including the performance of AI systems. Cognitive scientists will find Forbus's account of qualitative representations illuminating; AI scientists will value Forbus's new approach to qualitative representations and the overview he offers.
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