À propos de cet article

Citez

Alexander, S. A., and Hutter, M. 2021. Reward-Punishment Symmetric Universal Intelligence. In CAGI.10.1007/978-3-030-93758-4_1 Search in Google Scholar

Alexander, S. A., and Pedersen, A. P. 2022. Pseudo-visibility: A Game Mechanic Involving Willful Ignorance. In FLAIRS.10.32473/flairs.v35i.130652 Search in Google Scholar

Alexander, S. A.; Castaneda, M.; Compher, K.; and Martinez, O. 2022. Extended Environments. https://github.com/semitrivial/ExtendedEnvironments. Search in Google Scholar

Alexander, S. A. 2022. Extended subdomains: a solution to a problem of Hernández-Orallo and Dowe. Preprint (accepted to CAGI-22).10.1007/978-3-031-19907-3_14 Search in Google Scholar

Bell, J. H.; Linsefors, L.; Oesterheld, C.; and Skalse, J. 2021. Reinforcement Learning in Newcomblike Environments. In NeurIPS. Search in Google Scholar

Bellemare, M. G.; Naddaf, Y.; Veness, J.; and Bowling, M. 2013. The arcade learning environment: An evaluation platform for general agents. Journal of Artificial Intelligence Research 47:253–279.10.1613/jair.3912 Search in Google Scholar

Beyret, B.; Hernández-Orallo, J.; Cheke, L.; Halina, M.; Shanahan, M.; and Crosby, M. 2019. The animal-AI environment: Training and testing animal-like artificial cognition. Preprint. Search in Google Scholar

Brockman, G.; Cheung, V.; Pettersson, L.; Schneider, J.; Schulman, J.; Tang, J.; and Zaremba, W. 2016. OpenAI gym. Preprint. Search in Google Scholar

Chaslot, G.; Bakkes, S.; Szita, I.; and Spronck, P. 2008. Monte-Carlo Tree Search: A New Framework for Game AI. AIIDE 8:216–217.10.1609/aiide.v4i1.18700 Search in Google Scholar

Chollet, F. 2019. On the measure of intelligence. Preprint. Search in Google Scholar

Cobbe, K.; Hesse, C.; Hilton, J.; and Schulman, J. 2020. Leveraging procedural generation to benchmark reinforcement learning. In International conference on machine learning, 2048–2056. PMLR. Search in Google Scholar

Gavane, V. 2013. A measure of real-time intelligence. Journal of Artificial General Intelligence 4(1):31–48.10.2478/jagi-2013-0003 Search in Google Scholar

Hendrycks, D., and Dietterich, T. 2019. Benchmarking neural network robustness to common corruptions and perturbations. In International Conference on Learning Representations. Search in Google Scholar

Hernández-Orallo, J., and Dowe, D. L. 2010. Measuring universal intelligence: Towards an anytime intelligence test. Artificial Intelligence 174(18):1508–1539.10.1016/j.artint.2010.09.006 Search in Google Scholar

Hibbard, B. 2008. Adversarial sequence prediction. In CAGI. Search in Google Scholar

Hubinger, E.; van Merwijk, C.; Mikulik, V.; Skalse, J.; and Garrabrant, S. 2019. Risks from learned optimization in advanced machine learning systems. Preprint. Search in Google Scholar

Hutter, M. 2004. Universal artificial intelligence: Sequential decisions based on algorithmic probability. Springer. Search in Google Scholar

Legg, S., and Hutter, M. 2007. Universal intelligence: A definition of machine intelligence. Minds and machines 17(4):391–444.10.1007/s11023-007-9079-x Search in Google Scholar

Legg, S., and Veness, J. 2013. An approximation of the universal intelligence measure. In Algorithmic Probability and Friends: Bayesian Prediction and Artificial Intelligence. Springer. 236–249.10.1007/978-3-642-44958-1_18 Search in Google Scholar

Leike, J., and Hutter, M. 2015. Bad universal priors and notions of optimality. In Conference on Learning Theory, 1244–1259. PMLR. Search in Google Scholar

Li, M., and Vitányi, P. 2008. An introduction to Kolmogorov complexity and its applications. Springer.10.1007/978-0-387-49820-1 Search in Google Scholar

Nichol, A.; Pfau, V.; Hesse, C.; Klimov, O.; and Schulman, J. 2018. Gotta Learn Fast: A New Benchmark for Generalization in RL. Preprint. Search in Google Scholar

Nozick, R. 1969. Newcomb’s problem and two principles of choice. In Rescher, N., ed., Essays in honor of Carl G. Hempel. Springer. 114–146.10.1007/978-94-017-1466-2_7 Search in Google Scholar

Raffn, A.; Hill, A.; Ernestus, M.; Gleave, A.; Kanervisto, A.; and Dormann, N. 2019. Stable Baselines3. https://github.com/DLR-RM/stable-baselines3. Search in Google Scholar

Sherstan, C.; White, A.; Machado, M. C.; and Pilarski, P. M. 2016. Introspective agents: Confidence measures for general value functions. In Conference on Artificial General Intelligence, 258–261. Springer.10.1007/978-3-319-41649-6_26 Search in Google Scholar

Yampolskiy, R. V. 2017. Detecting qualia in natural and artificial agents. Preprint. Search in Google Scholar

eISSN:
1946-0163
Langue:
Anglais
Périodicité:
2 fois par an
Sujets de la revue:
Computer Sciences, Artificial Intelligence