A formal measure of machine intelligence
| dc.contributor.author | Legg, Shane | |
| dc.contributor.author | Hutter, Marcus | |
| dc.date.accessioned | 2015-08-31T02:20:42Z | |
| dc.date.available | 2015-08-31T02:20:42Z | |
| dc.date.issued | 2006-05 | |
| dc.description.abstract | A fundamental problem in artificial intelligence is that nobody really knows what intelligence is. The problem is especially acute when we need to consider artificial systems which are significantly different to humans. In this paper we approach this problem in the following way: We take a number of well known informal definitions of human intelligence that have been given by experts, and extract their essential features. These are then mathematically formalised to produce a general measure of intelligence for arbitrary machines. We believe that this measure formally captures the concept of machine intelligence in the broadest reasonable sense. | en_AU |
| dc.identifier.uri | http://hdl.handle.net/1885/15032 | |
| dc.publisher | Belgian-Dutch Conference on Machine Learning (Benelearn) | en_AU |
| dc.relation.ispartof | Proceedings of the 15th Annual Machine Learning Conference of Belgium and The Netherlands Benelearn'06 | en_AU |
| dc.rights | © The Author(s) | en_AU |
| dc.title | A formal measure of machine intelligence | en_AU |
| dc.type | Conference paper | en_AU |
| local.bibliographicCitation.lastpage | 80 | en_AU |
| local.bibliographicCitation.startpage | 73 | en_AU |
| local.contributor.affiliation | Hutter, M., Research School of Computer Science, The Australian National University | en_AU |
| local.contributor.authoruid | u4350841 | en_AU |
| local.type.status | Published Version | en_AU |
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