Year: 2011
Type: preprint
Abstract: Artificial general intelligence aims to create agents capable of learning to solve arbitrary interesting problems. We define two versions of asymptotic optimality and prove that no agent can satisfy the strong version while in some cases, depending on discounting, there does exist a non-computable weak asymptotically optimal agent.
Source: arXiv (Cornell University)
Authors: Lattimore, Tor, Hutter, Marcus
Institutions: Australian National University, ETH Zurich
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2952363498