INFORMATION, PHYSICS AND THE REPRESENTING MIND.
A primary function of mind is to form and manipulate representations to identify and choose survival-enhancing behaviors. Representations are themselves physical systems that can be manipulated to reason about, predict, or plan actions involving the objects they designate. The field of knowledge rep...
| Publicado en: | Cosmos & History Vol. 10; no. 1; pp. 131 - 140 |
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| Formato: | Artículo |
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Ashton & Rafferty
2014
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=101548813&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 101548813 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 18329101 78JL jtl: Cosmos & History issn: 18329101 maglogo: N pubinfo: dt: 2014 vid: 10 iid: 1 pid: 47219 pub: Ashton & Rafferty artinfo: ui: 101548813 ppf: 131 ppct: 9 formats: fmt: @attributes: type: P size: 179KB tig: atl: INFORMATION, PHYSICS AND THE REPRESENTING MIND. aug: au: Laskey, Kathryn Blackmond affil: SEOR Department, George Mason University, Fairfax, VA, 20030, USA su: Knowledge representation (Information theory) Computational physics Digital computer simulation Metaphor Cognition Algorithms sug: subj: Knowledge representation (Information theory) Computational physics Digital computer simulation Metaphor Cognition Algorithms keyword: Knowledge representation and reasoning Markov Chain Monte Carlo Physical Symbol System Quantum Zeno Effect ab: A primary function of mind is to form and manipulate representations to identify and choose survival-enhancing behaviors. Representations are themselves physical systems that can be manipulated to reason about, predict, or plan actions involving the objects they designate. The field of knowledge representation and reasoning (KRR) turns representation upon itself to study how representations are formed and used by biological and computer systems. Some of the most versatile and successful KRR methods have been imported from computational physics. Features of a problem are mapped onto dimensions of an imaginary physical system in which solution quality is inversely related to energy. Simulating the fictitious physical system on a digital computer yields a low-energy, and hence high-quality, solution to the original problem. This paper suggests a rethinking of the traditional metaphor of cognition as execution of algorithms on a digital computer. It may be both more fruitful and more accurate to conceive of representation as mapping problem features to an energy surface, learning as identifying representations that map good solutions to low free energy, and problem solving as efficient search for low free energy states. This conception of cognition is in natural accord with Stapp's theory of efficacious conscious choice. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Copyright of Cosmos & History is the property of Ashton & Rafferty and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. item: Cosmos & History holder: Ashton & Rafferty dt: @attributes: year: 2014 holdings: @attributes: islocal: N |
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