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AIXI /ˈaɪksi/ is a theoretical mathematical formalism for artificial general intelligence. It combines Solomonoff induction with sequential decision theory. AIXI was first proposed by Marcus Hutter in 2000 and several results regarding AIXI are proved in Hutter's 2005 book Universal Artificial Intelligence.
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| Subject | Predicate | Object | Confidence | Src |
|---|---|---|---|---|
| AIXI | is a | reinforcement learning | 0.90 | text |
| partially observable Pac-Man | instance of | which has had some success playing simple games | 0.80 | text |
| AIXI | related to Computational aspects | Like Solomonoff | 0.60 | section |
| AIXI | related to Computational aspects | However | 0.60 | section |
| AIXI | related to Computational aspects | One | 0.60 | section |
| AIXI | related to Computational aspects | AIXItl | 0.60 | section |
| AIXI | related to Computational aspects | Another | 0.60 | section |
| AIXI | related to Computational aspects | MC-AIXI | 0.60 | section |
| AIXI | related to Computational aspects | FAC-CTW | 0.60 | section |
| AIXI | related to Computational aspects | Monte Carlo AIXI FAC-Context-Tree | 0.60 | section |
| AIXI | related to Computational aspects | Weighting | 0.60 | section |
| AIXI | related to Computational aspects | Pac-Man | 0.60 | section |
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