AIMar 20, 2013
A Reason Maintenace System Dealing with Vague Data
arXiv:1303.5717v16 citations
Originality Synthesis-oriented
AI Analysis
This work addresses the challenge of handling uncertainty in problem-solving systems, though it appears incremental as it builds on existing ATMS and fuzzy logic frameworks.
The paper tackles the problem of reasoning under incomplete information and vague data by extending an ATMS with Mukaidono's fuzzy logic, enabling nonmonotonic inferences and revision of conclusions upon detecting contradictions.
A reason maintenance system which extends an ATMS through Mukaidono's fuzzy logic is described. It supports a problem solver in situations affected by incomplete information and vague data, by allowing nonmonotonic inferences and the revision of previous conclusions when contradictions are detected.