{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/information-gain-computation","title":"Information-gain computation","arxiv_id":"1707.01550","date":"2017-07-05","proceeding":null,"authors":["Anthony Di Franco"],"abstract":"Despite large incentives, ecorrectness in software remains an elusive goal.\nDeclarative programming techniques, where algorithms are derived from a\nspecification of the desired behavior, offer hope to address this problem,\nsince there is a combinatorial reduction in complexity in programming in terms\nof specifications instead of algorithms, and arbitrary desired properties can\nbe expressed and enforced in specifications directly. However, limitations on\nperformance have prevented programming with declarative specifications from\nbecoming a mainstream technique for general-purpose programming. To address the\nperformance bottleneck in deriving an algorithm from a specification, I propose\ninformation-gain computation, a framework where an adaptive evaluation strategy\nis used to efficiently perform a search which derives algorithms that provide\ninformation about a query most directly. Within this framework, opportunities\nto compress the search space present themselves, which suggest that\ninformation-theoretic bounds on the performance of such a system might be\narticulated and a system designed to achieve them. In a preliminary empirical\nstudy of adaptive evaluation for a simple test program, the evaluation strategy\nadapts successfully to evaluate a query efficiently.","url_abs":"http://arxiv.org/abs/1707.01550v3","url_pdf":"http://arxiv.org/pdf/1707.01550v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"information-gain-computation","repo_url":"https://github.com/difranco/fifth","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}