The power of physical representations
dc.citation.epage | 65 | en_US |
dc.citation.issueNumber | 3 | en_US |
dc.citation.spage | 49 | en_US |
dc.citation.volumeNumber | 10 | en_US |
dc.contributor.author | Akman, V. | en_US |
dc.contributor.author | ten Hagen, P. J. W. | en_US |
dc.date.accessioned | 2016-02-08T10:56:50Z | |
dc.date.available | 2016-02-08T10:56:50Z | |
dc.date.issued | 1989 | en_US |
dc.department | Department of Computer Engineering | en_US |
dc.description.abstract | Commonsense reasoning about the physical world, as exemplified by 'Iron sinks in water' or 'If a ball is dropped it gains speed,' will be indispensable in future programs. We argue that to make such predictions (namely, envisioning), programs should use abstract entities (such as the gravitational field), principles (such as the principle of superposition), and laws (such as the conservation of energy) of physics for representation and reasoning. These arguments are in accord with a recent study in physics instruction where expert problem solving is related to the construction of physical representations that contain fictitious, imagined entities such as forces and momenta. We give several examples showing the power of physical representations. | en_US |
dc.description.provenance | Made available in DSpace on 2016-02-08T10:56:50Z (GMT). No. of bitstreams: 1 bilkent-research-paper.pdf: 70227 bytes, checksum: 26e812c6f5156f83f0e77b261a471b5a (MD5) Previous issue date: 1989 | en |
dc.identifier.issn | 0738-4602 | en_US |
dc.identifier.uri | http://hdl.handle.net/11693/26234 | en_US |
dc.language.iso | English | en_US |
dc.publisher | AAAI Press | en_US |
dc.source.title | AI Magazine | en_US |
dc.subject | Physics | en_US |
dc.subject | Expert problem Ssolving | en_US |
dc.subject | Physical representations | en_US |
dc.subject | Reasoning | en_US |
dc.subject | Artificial intelligence | en_US |
dc.title | The power of physical representations | en_US |
dc.type | Article | en_US |
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