Intelligent Agent Foundations Forumhttp://agentfoundations.org/Intelligent Agent Foundations ForumTransition to the new AI Alignment Forumhttp://agentfoundations.org/item?id=1875Malo Bourgon

Now that the new AI Alignment Forum has launched, we’ll be shutting down the Intelligent Agent Foundation Forum (IAFF) this month.

All content from IAFF has already been imported to the AI Alignment Forum, and redirects for IAFF are setup and ready to go.

If you cannot access your account on AI Alignment Forum, you can contact support to regain access to your account through the Intercom chat in the bottom right corner of the new site.

In this essay I will try to explain the overall structure and motivation of my AI alignment research agenda. The discussion is informal and no new theorems are proved here. The main features of my research agenda, as I explain them here, are

• Viewing AI alignment theory as part of a general abstract theory of intelligence

• Using desiderata and axiomatic definitions as starting points, rather than specific algorithms and constructions

• Formulating alignment problems in the language of learning theory

• Evaluating solutions by their formal mathematical properties, ultimately aiming at a quantitative theory of risk assessment

• Relying on the mathematical intuition derived from learning theory to pave the way to solving philosophical questions

Logical Inductor Tiling and Why it's Hardhttp://agentfoundations.org/item?id=1808Alex Appel

(Tiling result due to Sam, exposition of obstacles due to me)

A Loophole for Self-Applicative Soundnesshttp://agentfoundations.org/item?id=1810Alex AppelLogical Inductors Converge to Correlated Equilibria (Kinda)http://agentfoundations.org/item?id=1804Alex Appel

Logical inductors of “similar strength”, playing against each other in a repeated game, will converge to correlated equilibria of the one-shot game, for the same reason that players that react to the past plays of their opponent converge to correlated equilibria. In fact, this proof is essentially just the proof from Calibrated Learning and Correlated Equilibrium by Forster (1997), adapted to a logical inductor setting.

Logical Inductor Lemmashttp://agentfoundations.org/item?id=1807Alex AppelTwo Notions of Best Responsehttp://agentfoundations.org/item?id=1806Alex Appel

In game theory, there are two different notions of “best response” at play. Causal best-response corresponds to standard game-theoretic reasoning, because it assumes that the joint probability distribution over everyone else’s moves remains unchanged if one player changes their move. The second one, Evidential best-response, can model cases where the actions of the various players are not subjectively independent, such as Death in Damascus, Twin Prisoner’s Dilemma, Troll Bridge, Newcomb, and Smoking Lesion, and will be useful to analyze the behavior of logical inductors in repeated games. This is just a quick rundown of the basic properties of these two notions of best response.

Doubts about Updatelessnesshttp://agentfoundations.org/item?id=1797Alex AppelComputing an exact quantilal policyhttp://agentfoundations.org/item?id=1794Vadim KosoyResource-Limited Reflective Oracleshttp://agentfoundations.org/item?id=1793Alex AppelNo Constant Distribution Can be a Logical Inductorhttp://agentfoundations.org/item?id=1792Alex AppelMusings on Explorationhttp://agentfoundations.org/item?id=1786Alex AppelQuantilal control for finite MDPshttp://agentfoundations.org/item?id=1785Vadim Kosoy

We introduce a variant of the concept of a “quantilizer” for the setting of choosing a policy for a finite Markov decision process (MDP), where the generic unknown cost is replaced by an unknown penalty term in the reward function. This is essentially a generalization of quantilization in repeated games with a cost independence assumption. We show that the “quantilal” policy shares some properties with the ordinary optimal policy, namely that (i) it can always be chosen to be Markov (ii) it can be chosen to be stationary when time discount is geometric (iii) the “quantilum” value of an MDP with geometric time discount is a continuous piecewise rational function of the parameters, and it converges when the discount parameter $$\lambda$$ approaches 1. Finally, we demonstrate a polynomial-time algorithm for computing the quantilal policy, showing that quantilization is not qualitatively harder than ordinary optimization.

A Difficulty With Density-Zero Explorationhttp://agentfoundations.org/item?id=1781Alex AppelDistributed Cooperationhttp://agentfoundations.org/item?id=1777Alex Appel

Reflective oracles can be approximated by computing Nash equilibria. But is there some procedure that produces a Pareto-optimal equilibrium in a game, aka, a point produced by a Cooperative oracle? It turns out there is. There are some interesting philosophical aspects to it, which will be typed up in the next post.

The result is not original to me, it’s been floating around MIRI for a while. I think Scott, Sam, and Abram worked on it, but there might have been others. All I did was formalize it a bit, and generalize from the 2-player 2-move case to the n-player n-move case. With the formalism here, it’s a bit hard to intuitively understand what’s going on, so I’ll indicate where to visualize an appropriate 3-dimensional object.

Using lying to detect human valueshttp://agentfoundations.org/item?id=1776Stuart ArmstrongIntuitive examples of reward function learning?http://agentfoundations.org/item?id=1775Stuart ArmstrongFunding for independent AI alignment researchhttp://agentfoundations.org/item?id=1774Paul ChristianoImproved regret bound for DRLhttp://agentfoundations.org/item?id=1773Vadim KosoynilBeyond algorithmic equivalence: self-modellinghttp://agentfoundations.org/item?id=1772Stuart ArmstrongBeyond algorithmic equivalence: algorithmic noisehttp://agentfoundations.org/item?id=1771Stuart ArmstrongUsing the universal prior for logical uncertaintyhttp://agentfoundations.org/item?id=1770Vladimir SlepnevnilPassing Troll Bridgehttp://agentfoundations.org/item?id=1769Alex Appel