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dc.contributor.authorÖzkural, Erayen_US
dc.contributor.authorAykanat, Cevdeten_US
dc.date.accessioned2016-02-08T12:24:12Z
dc.date.available2016-02-08T12:24:12Z
dc.date.issued2010en_US
dc.identifier.urihttp://hdl.handle.net/11693/28575
dc.descriptionConference name: Proceedings of the 3d Conference on Artificial General Intelligence AGI 2010
dc.description.abstractGigamachine is our initial implementation of an Artificial General Intelligence (AGI system) in the O'Caml language with the goal of building Solomonoff's Phase 1 machine that he proposed as the basis of a quite powerful incremental machine learning system (Sol02). While a lot of work remains to implement the full system, the present algorithms and implementation demonstrate the issues in building a realistic system. Thus, we report on our ongoing research to share our experience in designing such a system. In this extended abstract, we give an overview of our present implementation, summarize our contributions, discuss the results obtained, the limitations of our system, our plans to overcome those limitations, potential applications, and future work. The reader is referred to (Sch04; Sol02; Sol09) for a background on general-purpose incremental machine learning. The precise technical details of our ongoing work may be found in (Ozk09), which focuses on our algorithmic contributions. The discussion here is not as technical but assumes basic knowledge of universal problem solvers.en_US
dc.description.abstractGigamachine is our initial implementation of an Artificial General Intelligence (AGI system) in the O'Caml language with the goal of building Solomonoff's Phase 1 machine that he proposed as the basis of a quite powerful incremental machine learning system (Sol02). While a lot of work remains to implement the full system, the present algorithms and implementation demonstrate the issues in building a realistic system. Thus, we report on our ongoing research to share our experience in designing such a system. In this extended abstract, we give an overview of our present implementation, summarize our contributions, discuss the results obtained, the limitations of our system, our plans to overcome those limitations, potential applications, and future work. The reader is referred to (Sch04; Sol02; Sol09) for a background on general-purpose incremental machine learning. The precise technical details of our ongoing work may be found in (Ozk09), which focuses on our algorithmic contributions. The discussion here is not as technical but assumes basic knowledge of universal problem solvers.
dc.language.isoEnglishen_US
dc.source.titleProceedings of the 3d Conference on Artificial General Intelligence AGI 2010en_US
dc.relation.isversionofhttps://doi.org/10.2991/agi.2010.27en_US
dc.titleStochastic grammar based incremental machine learning using schemeen_US
dc.typeConference Paperen_US
dc.departmentDepartment of Computer Engineeringen_US
dc.citation.spage190en_US
dc.citation.epage191en_US
dc.identifier.doi10.2991/agi.2010.27en_US
dc.publisherAtlantis Press


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