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Stuart Russell

UC Berkeley computer scientist and AI theorist — co-author of the standard textbook Artificial Intelligence: A Modern Approach and a leading voice on long-term AI safety, who has also made two separate load-bearing theoretical contributions this vault now tracks independently.

Matters to this vault on two counts that predate any connection between them. First, Russell (with Eric Horvitz) is a co-originator of bounded optimality, a theory for designing optimal programs for agents with performance-limited hardware, which resource-rational analysis cites as its AI antecedent. Second, Russell co-authored (with Andrew Y. Ng and Daishi Harada, ICML 1999) the classical potential-based-reward-shaping invariance theorem — that a fixed, bounded potential with appropriate terminal-state handling preserves the policies optimal under the original task reward — which the 2026 robot-learning paper CoDA invokes as the theoretical basis for its "Text2potential" dense-reward mechanism (claim-coda-shared-memory-couples-data-engine-policy-optimality-preserving-reward-shaping). The two contributions sit in different vault clusters (rational-agent theory and RL reward design) twenty-seven years apart, connected only by Russell himself.

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