Death and suicide in universal artificial intelligence
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Martin, Jarryd; Everitt, Tom; Hutter, Marcus
Description
Reinforcement learning (RL) is a general paradigm for studying intelligent behaviour, with applications ranging from artificial intelligence to psychology and economics. AIXI is a universal solution to the RL problem; it can learn any computable environment. A technical subtlety of AIXI is that it is defined using a mixture over semimeasures that need not sum to 1, rather than over proper probability measures. In this work we argue that the shortfall of a semimeasure can naturally be...[Show more]
dc.contributor.author | Martin, Jarryd | |
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dc.contributor.author | Everitt, Tom | |
dc.contributor.author | Hutter, Marcus | |
dc.date.accessioned | 2016-12-21T00:53:45Z | |
dc.date.available | 2016-12-21T00:53:45Z | |
dc.identifier.isbn | 978-3-319-41648-9 | |
dc.identifier.issn | 0302-9743 | |
dc.identifier.uri | http://hdl.handle.net/1885/111443 | |
dc.description.abstract | Reinforcement learning (RL) is a general paradigm for studying intelligent behaviour, with applications ranging from artificial intelligence to psychology and economics. AIXI is a universal solution to the RL problem; it can learn any computable environment. A technical subtlety of AIXI is that it is defined using a mixture over semimeasures that need not sum to 1, rather than over proper probability measures. In this work we argue that the shortfall of a semimeasure can naturally be interpreted as the agent’s estimate of the probability of its death. We formally define death for generally intelligent agents like AIXI, and prove a number of related theorems about their behaviour. Notable discoveries include that agent behaviour can change radically under positive linear transformations of the reward signal (from suicidal to dogmatically self-preserving), and that the agent’s posterior belief that it will survive increases over time. | |
dc.format | 23 pages | |
dc.format.mimetype | application/pdf | |
dc.publisher | Springer Verlag (Germany) | |
dc.rights | © Springer International Publishing Switzerland 2016 | |
dc.source | Lecture Notes in Computer Science | |
dc.subject | reinforcement learning (RL) | |
dc.subject | semimeasure | |
dc.subject | intelligent | |
dc.subject | behaviour | |
dc.subject | artificial | |
dc.subject | intelligence | |
dc.subject | AIXI | |
dc.subject | death | |
dc.subject | probability | |
dc.title | Death and suicide in universal artificial intelligence | |
dc.type | Journal article | |
local.description.notes | The article appears as part of a monographic series in Steunebrink B., Wang P., Goertzel B. (eds) Artificial General Intelligence. AGI 2016. Lecture Notes in Computer Science, vol 9782. Springer, Cham. | |
local.identifier.citationvolume | 9782 | |
dc.date.issued | 2016-06-25 | |
local.publisher.url | http://link.springer.com/ | |
local.type.status | Published Version | |
local.contributor.affiliation | Hutter, Marcus, Research School of Computer Science, College of Engineering and Computer Science, The Australian National University | |
local.bibliographicCitation.startpage | 23 | |
local.bibliographicCitation.lastpage | 32 | |
local.identifier.doi | 10.1007/978-3-319-41649-6_3 | |
dcterms.accessRights | Open Access | |
Collections | ANU Research Publications |
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