TLDR: Psychology, economics, and other disciplines describe agents as systems driven by beliefs and desires. This post argues that the belief-desire view can be derived from classic theorems from optimal control and reinforcement learning. This suggests seeing beliefs and desires as properties of optimal policies rather than as assumptions from...
TLDR: In recent work, Roy Fox proposes to understand an agent's capabilities in terms of the set of environment dynamics it can bring about.[1] This leads to an intriguing duality between probabilities and utilities via the Legendre-Fenchel transform. Introduction Some agents are more powerful than others. Indeed, some can yield...
Drawing inspiration from the ‘brain in a vat’ thought experiment, this blogpost investigates methods to simplify world models that remain agnostic to the agent under evaluation. This work was done together with Alec Boyd and Manuel Baltieri, with support from the UK ARIA Safeguarded AI programme and the PIBBSS Affiliateship...