Agents from First Principles
The question underneath all five: what would an agent look like if you derived its behaviour from a few fundamentals rather than assembling it from components?
It starts with a creature that has to learn which things are safe to eat, and ends with a claim that the entire apparatus is three types and four axioms. Along the way it plays Zork badly, then less badly, and the reason it improves turns out to have nothing to do with intelligence and everything to do with how it represents where it is.
Building a Bayesian Learning Agent That Teaches Itself to Eat
A visual demonstration of Bayesian inference and the exploration-exploitation tradeoff. An agent navigates a grid world, learning which foods are safe through exact conjugate updates.
Evolution Discovers How to Think: A Philosophical Journey in Code
Part 2 of the Bayesian agent series. We confront the question of what should be designed versus what should be allowed to emerge, and discover that it's agents all the way up and all the way down.
Teaching Zork to a Bayesian
A Bayesian agent plays text adventures with four information sources and a VOI gate on every query. The LLM recommends actions. The mathematics decides whether to listen.
The Loop Problem
Every RL agent that has played a text adventure has tried to take the lantern fifty times in a row. The fix is not better exploration heuristics. The fix is representing state properly.
Three Types and a Funeral for Your Inference Library
What would it take to build an agent whose behaviour is derived from a few fundamentals the way physics is derived from conservation laws? Three types, four axioms, and a refusal to add anything else.