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This paper provides a comprehensive historical and theoretical survey of computational belief change, tracing the evolution from early taxonomies through the AGM framework to modern implementation challenges. By synthesizing decades of research into robust blueprints with formal guarantees, it establishes a critical baseline for engineering-focused AI systems that must dynamically update their knowledge bases. The work is essential for researchers developing reliable reasoning engines and large language models capable of coherent belief revision.
From Doyle to AGM: A Survey and an Implementation Roadmap for Belief Change
arXiv:2608.14567v1 Announce Type: new Abstract: This paper presents a targeted narrative review establishing the historical and theoretical foundations for computational belief change implementation. Seeded by Doyle and London's foundational 1980 taxonomy, we trace the evolution of belief revision from computational origins through the theoretical transformation of the AGM framework to contemporary approaches. Our analysis demonstrates how pre-AGM computational pragmatism relates to AGM theoretical constructs, revealing both continuities and transformations across this evolution. We analyze how each taxonomical category evolved in the post-AGM era, identifying the theoretical foundations and historical precedents that inform contemporary implementation challenges. This foundation enables subsequent research into robust computational blueprints that synthesize historical insights with formal guarantees, providing the baseline for systematic implementation analysis and engineering-focused belief change research.
Abstract:This paper presents a targeted narrative review establishing the historical and theoretical foundations for computational belief change implementation. Seeded by Doyle and London's foundational 1980 taxonomy, we trace the evolution of belief revision from computational origins through the theoretical transformation of the AGM framework to contemporary approaches. Our analysis demonstrates how pre-AGM computational pragmatism relates to AGM theoretical constructs, revealing both continuities and transformations across this evolution. We analyze how each taxonomical category evolved in the post-AGM era, identifying the theoretical foundations and historical precedents that inform contemporary implementation challenges. This foundation enables subsequent research into robust computational blueprints that synthesize historical insights with formal guarantees, providing the baseline for systematic implementation analysis and engineering-focused belief change research.
Author's accepted manuscript of an article published in The European Journal on Artificial Intelligence 2026 (SAGE). 65 pages, 2 figures. Final published version available at this https URL
Artificial Intelligence (cs.AI); Logic in Computer Science (cs.LO)
arXiv:2608.14567 [cs.AI]
(or arXiv:2608.14567v1 [cs.AI] for this version)
https://doi.org/10.1177/30504554261426170 DOI(s) linking to related resources
Full text under the publisher's open licence · 168 words
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