AIQI is the first model-free universal AI agent proven asymptotically ε-optimal in general RL by inducing over distributional Q-functions instead of policies or environments.
A theory of universal artificial intelligence based on algorithmic complexity
4 Pith papers cite this work. Polarity classification is still indexing.
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abstract
Decision theory formally solves the problem of rational agents in uncertain worlds if the true environmental prior probability distribution is known. Solomonoff's theory of universal induction formally solves the problem of sequence prediction for unknown prior distribution. We combine both ideas and get a parameterless theory of universal Artificial Intelligence. We give strong arguments that the resulting AIXI model is the most intelligent unbiased agent possible. We outline for a number of problem classes, including sequence prediction, strategic games, function minimization, reinforcement and supervised learning, how the AIXI model can formally solve them. The major drawback of the AIXI model is that it is uncomputable. To overcome this problem, we construct a modified algorithm AIXI-tl, which is still effectively more intelligent than any other time t and space l bounded agent. The computation time of AIXI-tl is of the order tx2^l. Other discussed topics are formal definitions of intelligence order relations, the horizon problem and relations of the AIXI theory to other AI approaches.
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Intervention complexity provides a family of canonical rewards indexed by resource bias that completes the Legg-Hutter framework and enables a two-dimensional view of intelligence as competence plus learning efficiency.
A type system over finitely generated abelian groups enables design-time verification of AI model properties and links Hindley-Milner unification to a restriction of Solomonoff's universal prior.
The paper surveys the origins, frameworks, applications, and open challenges of AI agents built on large language models.
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A Model-Free Universal AI
AIQI is the first model-free universal AI agent proven asymptotically ε-optimal in general RL by inducing over distributional Q-functions instead of policies or environments.
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Intervention Complexity as a Canonical Reward and a Measure of Intelligence
Intervention complexity provides a family of canonical rewards indexed by resource bias that completes the Legg-Hutter framework and enables a two-dimensional view of intelligence as competence plus learning efficiency.
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Decidable By Construction: Design-Time Verification for Trustworthy AI
A type system over finitely generated abelian groups enables design-time verification of AI model properties and links Hindley-Milner unification to a restriction of Solomonoff's universal prior.
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The Rise and Potential of Large Language Model Based Agents: A Survey
The paper surveys the origins, frameworks, applications, and open challenges of AI agents built on large language models.