On uncertainty handling in plausible reasoning with conceptual graphs

1993 ◽  
pp. 137-147 ◽  
Author(s):  
Sung H. Myaeng ◽  
Christopher Khoo
Keyword(s):  
Conceptual Graphs ◽  
Uncertainty Handling ◽  
Plausible Reasoning

Inference

2018 ◽  
Author(s):  
Mark Sainsbury
Keyword(s):  
Common Basis ◽  
Plausible Reasoning ◽  
Potential Sources ◽  
Semantic Properties

Display theory predicts that no inferential relations among attitude attributions are based on the logical or semantic properties of the expressions in attribution complements. This chapter shows various ways in which there may be an illusion that such relations obtain. One common basis for the illusion is that we implicitly appeal to psychological facts. Since there is no reason to think these are necessary, the inferences are not truth preserving of necessity, even if they generally have true conclusions when they have true premises. They are examples of “plausible reasoning”. Wanting and fearing are discussed in detail as potential sources of the apparently inferential phenomena.

A FORMALISM FOR REPRESENTING AND REASONING WITH LINGUISTIC INFORMATION

2002 ◽  
Vol 10 (03) ◽  
pp. 281-307 ◽  
Author(s):  
TRU H. CAO
Keyword(s):  
Artificial Intelligence ◽  
Fuzzy Logic ◽  
Natural Language ◽  
Functional Relation ◽  
Computing With Words ◽  
Conceptual Graphs ◽  
Graph Language ◽  
Conceptual Graph ◽  
Knowledge Representation Language ◽  
Representation Language

Conceptual graphs and fuzzy logic are two logical formalisms that emphasize the target of natural language, where conceptual graphs provide a structure of formulas close to that of natural language sentences while fuzzy logic provides a methodology for computing with words. This paper proposes fuzzy conceptual graphs as a knowledge representation language that combines the advantages of both the two formalisms for artificial intelligence approaching human expression and reasoning. Firstly, the conceptual graph language is extended with functional relation types for representing functional dependency, and conjunctive types for joining concepts and relations. Then fuzzy conceptual graphs are formulated as a generalization of conceptual graphs where fuzzy types and fuzzy attribute-values are used in place of crisp types and crisp attribute-values. Projection and join as basic operations for reasoning on fuzzy conceptual graphs are defined, taking into account the semantics of fuzzy set-based values.

Teaching Political Philosophy as Plausible Reasoning

1985 ◽  
Vol 45 ◽  
pp. 8-8
Author(s):  
Larry Arnhart
Keyword(s):  
Political Philosophy ◽  
Political Thought ◽  
State University ◽  
University Of Chicago ◽  
Distinctive Character ◽  
Political Issues ◽  
Idaho State University ◽  
Plausible Reasoning ◽  
Northern Illinois University ◽  
The University

I have taught courses on political philosophy at four schools — the University of Chicago, Rosary College, Idaho State University, and Northern Illinois University. I have had to adjust the style of my teaching to conform to the distinctive character of each school. But I have found that the most fundamental obstacles to winning the attention of students have been the same.Many students have begun my courses with four unfavorable preconceptions. They believe that political philosophy is too abstract. And for that reason they also believe that it has no application to contemporary political issues. Moreover, many students assume that since the classic texts of political thought are old, the ideas they contain must therefore be obsolete. And finally they think that political philosophy is ultimately subjective because no philosopher can prove his ideas to be absolutely true.

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