Abstract
We propose a new model to quantitatively estimate the accuracy of artificial agents over cognitive tasks of approximable complexities. The model is derived by introducing notions from algorithmic information theory into a well-known (psychometric) measurement paradigm called Item Response Theory (IRT). A lower bound on accuracy can be guaranteed with respect to task complexity and the breadth of its solution space using our model. This in turn permits formulating the relationship between agent selection cost, task difficulty and accuracy as optimisation problems. Further results indicate some of the settings over which a group of cooperative agents can be more or less accurate than individual agents or other groups.
Original language | English |
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Title of host publication | Artificial General Intelligence |
Subtitle of host publication | 10th International Conference, AGI 2017, Melbourne, VIC, Australia, August 15–18, 2017, Proceedings |
Editors | Tom Everitt, Alexey Potapov, Ben Goertzel |
Place of Publication | Cham, Switzerland |
Publisher | Springer |
Pages | 225-236 |
Number of pages | 12 |
ISBN (Electronic) | 9783319637037 |
ISBN (Print) | 9783319637020 |
DOIs | |
Publication status | Published - 2017 |
Event | Conference on Artificial General Intelligence (AGI) 2017 - Melbourne, Australia Duration: 15 Aug 2017 → 18 Aug 2017 Conference number: 10th https://link.springer.com/book/10.1007/978-3-319-63703-7 (Proceedings) |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Publisher | Springer |
Volume | 10414 |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | Conference on Artificial General Intelligence (AGI) 2017 |
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Abbreviated title | AGI 2017 |
Country | Australia |
City | Melbourne |
Period | 15/08/17 → 18/08/17 |
Internet address |
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