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Browsing College of Public Health by Subject "Attributional calculus"

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    An Application of Symbolic Learning to Intrusion Detection: Preliminary Results from the LUS Methodology
    (2003-06) Kaufman, Kenneth A.; Cervone, Guido; Michalski, Ryszard S.
    This paper describes briefly a method for applying AQ symbolic learning to problems of computer user modeling and intrusion detection. The method, called LUS (Learning User Signatures), learns models of users’ interaction in the form of sets of rules in attributional calculus, and signals a possible intrusion when a user interaction with a computer violates the model. An important characteristic of LUS is that the generated user signatures are easy to interpret and understand. We describe briefly the LUS method, the machine learning and inference tools developed to support it, and selected initial experimental results from its application to real-world data.
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    Attributional Calculus: A Logic and Representation Language for Natural Induction
    (2004-04) Michalski, Ryszard S.
    Attributional calculus (AC) is a typed logic system that combines elements of propositional logic, predicate calculus, and multiple-valued logic for the purpose of natural induction. By natural induction is meant a form of inductive learning that generates hypotheses in human-oriented forms, that is, forms that appear natural to people, and are easy to understand and relate to human knowledge. To serve this goal, AC includes non-conventional logic operators and forms that can make logic expressions simpler and more closely related to the equivalent natural language descriptions. AC has two forms, basic and extended, each of which can be bare or annotated. The extended form adds more operators to the basic form, and the annotated form includes parameters characterizing statistical properties of bare expressions. AC has two interpretation schemas, strict and flexible. The strict schema interprets AC expressions as true-false valued, and the flexible schema as continuously-valued. Conventional decision rules, association rules, decision trees, and n-of-m rules all can be viewed as special cases of attributional rules. Attributional rules can be directly translated to natural language, and visualized using concept association graphs and general logic diagrams. AC stems from Variable-Valued Logic 1 (VL1), and is intended to serve as a concept description language in advanced AQ inductive learning programs. To provide a motivation and background for AC the first part of the paper presents basic ideas and assumptions underlying concept learning.
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    Building Knowledge Scouts Using KGL Metalanguage
    (2000) Michalski, Ryszard S.; Kaufman, Kenneth A.
    Knowledge scouts are software agents that autonomously search for and synthesize user-oriented knowledge (target knowledge) in large local or distributed databases. A knowledge generation metalanguage, KGL, is used to creating scripts defining such knowledge scouts. Knowledge scouts operate in an inductive database, by which we mean a database system in which conventional data and knowledge management operators are integrated with a wide range of data mining and inductive inference operators. Discovered knowledge is represented in two forms: (1) attributional rules, which are rules in attributional calculus -- a logic-based language between propositional and predicate calculus, and (2) association graphs, which graphically and abstractly represent relations expressed by the rules. These graphs can depict multi-argument relationships among different concepts, with a visual indication of the relative strength of each dependency. Presented ideas are illustrated by two simple knowledge scouts, one that seeks relations among lifestyles, environmental conditions, symptoms and diseases in a large medical database, and another that searches for patterns of children's behavior in the National Youth Survey database. The preliminary results indicate a high potential utility of the presented methodology as a tool for deriving knowledge from databases.