An algebraic synthesis of the foundations of logic and probability
Information Sciences: an International Journal
Conditional logic and the principle of entropy
Artificial Intelligence
Mathematics of Data Fusion
Conditional Inference and Logic for Intelligent Systems: A Theory of Measure-Free Conditioning
Conditional Inference and Logic for Intelligent Systems: A Theory of Measure-Free Conditioning
Deduction with uncertain conditionals
Information Sciences—Informatics and Computer Science: An International Journal
Coherent knowledge processing at maximum entropy by spirit
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Recall and Reasoning-an information theoretical model of cognitive processes
Information Sciences: an International Journal
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Various controversies surrounding the meaning of conditionals like “A given B” or “if B then A” are discussed including that they can non-trivially carry the standard conditional probability, are truth functional but have three rather than two truth values, are logically and probabilistically non-monotonic, and can be combined with operations that extend the standard Boolean operations. A new theory of deduction with uncertain conditionals extends the familiar equations that define deduction between Boolean propositions. Several different deductive relations arise leading to different sets of implications. New methods to determine these implications for one or more conditionals are described. An absent-minded coffee drinker example containing two subjunctive (counter-factual) conditionals is solved. The use of information entropy to cut through complexity, and the question of the confidence to be attached to a maximum entropy probability distribution, are discussed including the results of E. Jaynes concerning the concentration of distributions at maximum entropy.