Semantical considerations on nonmonotonic logic
Artificial Intelligence
Artificial Intelligence
The statistical analysis of compositional data
The statistical analysis of compositional data
A first-order conditional logic for prototypical properties
Artificial Intelligence
Artificial intelligence (2nd ed.)
Artificial intelligence (2nd ed.)
An algebraic synthesis of the foundations of logic and probability
Information Sciences: an International Journal
Expert systems for experts
Nonmonotonic reasoning, preferential models and cumulative logics
Artificial Intelligence
What does a conditional knowledge base entail?
Artificial Intelligence
A mathematical treatment of defeasible reasoning and its implementation
Artificial Intelligence
Probability and conditionals
Qualitative probabilities for default reasoning, belief revision, and causal modeling
Artificial Intelligence
From statistical knowledge bases to degrees of belief
Artificial Intelligence
Nonmonotonic reasoning, conditional objects and possibility theory
Artificial Intelligence
Diverse confidence levels in a probabilistic semantics for conditional logics
Artificial Intelligence
Belief functions and default reasoning
Artificial Intelligence
Information Sciences: an International Journal
Probabilistic Reasoning Under Coherence in System P
Annals of Mathematics and Artificial Intelligence
Probabilistic Default Reasoning with Conditional Constraints
Annals of Mathematics and Artificial Intelligence
A Maximum Entropy Approach to Nonmonotonic Reasoning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Maximum Entropy and Variable Strength Defaults
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Probabilistic Default Logic Based on Irrelevance and Relevance Assumptions
ECSQARU/FAPR '97 Proceedings of the First International Joint Conference on Qualitative and Quantitative Practical Reasoning
Probabilistic Logic Programming under Inheritance with Overriding
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Probabilistic Justification of Default Reasoning
KI '94 Proceedings of the 18th Annual German Conference on Artificial Intelligence: Advances in Artificial Intelligence
Argument-based applications to knowledge engineering
The Knowledge Engineering Review
Deduction from conditional knowledge
Soft Computing - A Fusion of Foundations, Methodologies and Applications
System Z: a natural ordering of defaults with tractable applications to nonmonotonic reasoning
TARK '90 Proceedings of the 3rd conference on Theoretical aspects of reasoning about knowledge
The Uncertain Reasoner's Companion (Cambridge Tracts in Theoretical Computer Science)
The Uncertain Reasoner's Companion (Cambridge Tracts in Theoretical Computer Science)
Digraphs: Theory, Algorithms and Applications
Digraphs: Theory, Algorithms and Applications
A logic for reasoning about upper probabilities
Journal of Artificial Intelligence Research
Probabilistic deduction with conditional constraints over basic events
Journal of Artificial Intelligence Research
Towards Modelling Defeasible Reasoning with Imperfection in Production Rule Systems
RuleML '09 Proceedings of the 2009 International Symposium on Rule Interchange and Applications
Hi-index | 0.00 |
Rules having rare exceptions may be interpreted as assertions of high conditional probability. In other words, a rule If X then Y may be interpreted as meaning that Pr(Y驴X) 1. A general approach to reasoning with such rules, based on second-order probability, is advocated. Within this general approach, different reasoning methods are needed, with the selection of a specific method being dependent upon what knowledge is available about the relative sizes, across rules, of upper bounds on each rule's exception probabilities Pr(卢Y驴X). A method of reasoning, entailment with universal near surety, is formulated for the case when no information is available concerning the relative sizes of upper bounds on exception probabilities. Any conclusion attained under these conditions is robust in the sense that it will still be attained if information about the relative sizes of exception probability bounds becomes available. It is shown that reasoning via entailment with universal near surety is equivalent to reasoning in a particular type of argumentation system having the property that when two subsets of the rule base conflict with each other, the effectively more specific subset overrides the other. As stepping stones toward attaining this argumentation result, theorems are proved characterizing entailment with universal near surety in terms of upper envelopes of probability measures, upper envelopes of possibility measures, and directed graphs. In addition, various attributes of entailment with universal near surety, including property inheritance, are examined.