@inproceedings{ec124fd9887d41c8b38d21b2b2f8f9d5,
title = "KASER: A qualitatively fuzzy object-oriented inference engine",
abstract = "This paper describes a shell that has been developed for the purpose of fuzzy qualitative reasoning. The relation among object predicates is defined by object trees that are fully capable of dynamic growth and maintenance. The qualitatively fuzzy inference engine and the developed expert system can then acquire a virtual-rule space that is exponentially (subject to machine implementation constants) larger than the actual, declared-rule space and with a decreasing non-zero likelihood of error. This capability is called knowledge amplification, and the methodology is named KASER. KASER is an acronym for Knowledge Amplification by Structured Expert Randomization. It can handle the knowledge-acquisition bottleneck in expert systems. KASER represents an intelligent, creative system that fails softly, learns over a network, and has enormous potential for automated decision making. KASERs compute with words and phrases and possess capabilities for metaphorical explanations.",
keywords = "Computer architecture, Costs, Decision making, Engines, Error correction, Expert systems, Intelligent networks, Intelligent systems, Production systems, User interfaces",
author = "Rubin, {S. H.} and Rush, {R. J.} and J. Murthy and Smith, {M. H.} and L. Trajkovi{\'c}",
note = "Publisher Copyright: {\textcopyright} 2002 IEEE.; null ; Conference date: 27-06-2002 Through 29-06-2002",
year = "2002",
doi = "10.1109/NAFIPS.2002.1018085",
language = "English",
series = "Annual Conference of the North American Fuzzy Information Processing Society - NAFIPS",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "354--359",
editor = "Olfa Nasraoui and Jim Keller",
booktitle = "2002 Annual Meeting of the North American Fuzzy Information Processing Society, Proceedings - NAFIPS-FLINT 2002",
}