{"title":"Fast hypothetical reasoning system using inference-path network","authors":"M. Ishizuka, Fumiaki Ito","doi":"10.1109/TAI.1991.167115","DOIUrl":null,"url":null,"abstract":"A fast hypothetical reasoning system is described which is named KICK-SHOTGAN, and which avoids inefficient backtracking by the forward synthesis of necessary hypothesis combination along this network. The formation of inference-path network is based on a linear-time algorithm for the satisfiability testing of propositional Horn clauses. This system differs from ATMS mainly in its total problem solving nature. That is, it works for the logical problem-solving framework which yields a solution for a given goal, whereas the ATMS calculates possible data supported by hypotheses incrementally in response to the input of a justification (rule) from a problem solver existing outside the ATMS.<<ETX>>","PeriodicalId":371778,"journal":{"name":"[Proceedings] Third International Conference on Tools for Artificial Intelligence - TAI 91","volume":"20 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1991-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"[Proceedings] Third International Conference on Tools for Artificial Intelligence - TAI 91","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/TAI.1991.167115","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 10
Abstract
A fast hypothetical reasoning system is described which is named KICK-SHOTGAN, and which avoids inefficient backtracking by the forward synthesis of necessary hypothesis combination along this network. The formation of inference-path network is based on a linear-time algorithm for the satisfiability testing of propositional Horn clauses. This system differs from ATMS mainly in its total problem solving nature. That is, it works for the logical problem-solving framework which yields a solution for a given goal, whereas the ATMS calculates possible data supported by hypotheses incrementally in response to the input of a justification (rule) from a problem solver existing outside the ATMS.<>