Maximum Entropy Applied to Inductive Logic and Reasoning
نویسندگان
چکیده
This editorial explains the scope of the special issue and provides a thematic introduction to the contributed papers.
منابع مشابه
On probabilistic inference in relational conditional logics
The principle of maximum entropy has proven to be a powerful approach for commonsense reasoning in probabilistic conditional logics on propositional languages. Due to this principle, reasoning is performed based on the unique model of a knowledge base that has maximum entropy. This kind of model-based inference fulfills many desirable properties for inductive inference mechanisms and is usually...
متن کاملOn the Emergence of Reasons in Inductive Logic
We apply methods of abduction derived from propositional probabilistic reasoning to predicate probabilistic reasoning, in particular inductive logic, by treating finite predicate knowledge bases as potentially infinite propositional knowledge bases. It is shown that for a range of predicate knowledge bases (such as those typically associated with inductive reasoning) and several key proposition...
متن کاملEvaluation and Comparison Criteria for Approaches to Probabilistic Relational Knowledge Representation
In the past ten years, the areas of probabilistic inductive logic programming and statistical relational learning put forth a large collection of approaches to combine relational representations of knowledge with probabilistic reasoning. Here, we develop a series of evaluation and comparison criteria for those approaches and focus on the point of view of knowledge representation and reasoning. ...
متن کاملJustifying Objective Bayesianism on Predicate Languages
Objective Bayesianism says that the strengths of one’s beliefs ought to be probabilities, calibrated to physical probabilities insofar as one has evidence of them, and otherwise sufficiently equivocal. These norms of belief are often explicated using the maximum entropy principle. In this paper we investigate the extent to which one can provide a unified justification of the objective Bayesian ...
متن کاملUniversität Dortmund an der Fakultät für Informatik Matthias Thimm
Reasoning with inaccurate information is a major topic within the fields of artificial intelligence in general and knowledge representation and reasoning in particular. This thesis deals with information that can be incomplete, uncertain, and contradictory. We employ probabilistic conditional logic as a foundation for our investigation. This framework allows for the representation of uncertain ...
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عنوان ژورنال:
- Entropy
دوره 17 شماره
صفحات -
تاریخ انتشار 2015