12 citations · 66 across the 25 of their papers we have counts for
7 papers · 1 filter
Universal Agent Mixtures and the Geometry of Intelligence
Samuel Allen Alexander, David Quarel, Len Du +1
Inspired by recent progress in multi-agent Reinforcement Learning (RL), in this work we examine the collective intelligent behaviour of theoretical universal agents by introducing…
Representation and Invariance in Reinforcement Learning
Samuel Alexander, Arthur Paul Pedersen
Researchers have formalized reinforcement learning (RL) in different ways. If an agent in one RL framework is to run within another RL framework's environments, the agent must firs…
Reward-Punishment Symmetric Universal Intelligence
Samuel Allen Alexander, Marcus Hutter
Can an agent's intelligence level be negative? We extend the Legg-Hutter agent-environment framework to include punishments and argue for an affirmative answer to that question. We…
Extending Environments To Measure Self-Reflection In Reinforcement Learning
Samuel Allen Alexander, Michael Castaneda, Kevin Compher +1
We consider an extended notion of reinforcement learning in which the environment can simulate the agent and base its outputs on the agent's hypothetical behavior. Since good perfo…
Measuring Intelligence and Growth Rate: Variations on Hibbard's Intelligence Measure
Samuel Alexander, Bill Hibbard
In 2011, Hibbard suggested an intelligence measure for agents who compete in an adversarial sequence prediction game. We argue that Hibbard's idea should actually be considered as…
Measuring the intelligence of an idealized mechanical knowing agent
Samuel Allen Alexander
We define a notion of the intelligence level of an idealized mechanical knowing agent. This is motivated by efforts within artificial intelligence research to define real-number in…