Algorithmic Probability and Sequential Decision Theory

Title: Algorithmic Probability and Sequential Decision Theory

Research Question: How can we develop a universal and optimal model for artificial intelligence that can learn and make decisions effectively in any computable environment?

Methodology: The authors propose a unified theory that combines decision theory and universal induction. They call this model AI ξ. They claim that AI ξ behaves optimally in any computable environment. To make the model computationally feasible, they introduce a modified version called AI ξtl.

Results: The authors show that AI ξ can learn and make decisions optimally in any computable environment, even when the true environmental probability distribution is unknown. They also provide a modified version, AI ξtl, which is time and space-bounded and can be used in practice.

Implications: The AI ξ and AI ξtl models have significant implications for the field of artificial intelligence. They provide a framework for developing intelligent systems that can learn and make decisions effectively in any computable environment. The models also offer a new approach to understanding and solving problems related to rational agents, sequential decisions, and universal induction.

Link to Article: https://arxiv.org/abs/0012011v1 Authors: arXiv ID: 0012011v1