Probabilistic Logic

  title={Probabilistic Logic},
  author={Nils J. Nilsson},
  journal={Artif. Intell.},
  • N. Nilsson
  • Published 1 February 1986
  • Philosophy, Computer Science
  • Artif. Intell.

Figures from this paper


A semantical generalization of logic in which the truth values of sentences are probability values between 0 and 1, which applies to any logical system for which the consistency of a finite set of sentences can be established.

Probabilistic Logic for Intelligent Systems

It is shown how the PSAT problem can be expressed and solved as a set of nonlinear equations derived from the knowledge base sentences and standard probability of logical sentences.

Incidence calculus: A mechanism for probabilistic reasoning

  • A. Bundy
  • Computer Science
    Journal of Automated Reasoning
  • 2004
It is argued that a purely numeric mechanism, like those proposed so far, cannot provide a probabilistic logic with truth functional connectives, and an alternative mechanism, Incidence Calculus, which is based on a representation of uncertainty using sets of points, which might represent situations models or possible worlds.

Evidential logic and Dempster-Shafer theory

Nilsson's probabilistic logic is extended, a semantic generalization of logic, in which the truth value of a sentence is a probability value between Ø and 1 to evidential logic in the framework of Dempster-Shafer theory.

Anytime Deduction for Probabilistic Logic

Foundations of Probabilistic Logic

It is proved that a complete theory of probabilistic logic requires the a priori assignment of probabilities for a system with k basic propositions and a proposal due to Cheeseman, namely, to regard measures of confidence in knowledge systems as expectations that are conditioned on unknown distributions does not work in general.

Some extensions of probabilistic logic

Probabilistic Reasoning in the Description Logic ALCP with the Principle of Maximum Entropy

This work introduces the probabilistic description logic ALCP, a logic designed for representing context-dependent knowledge, where the actual context taking place is uncertain, and provides reasoning algorithms for this logic, which satisfies several desirable properties of Probabilistic logics.



A logic to reason about likelihood

A logic LL is presented which uses a modal operator L to help capture the notion of likely and a complete axiomatization is given and it is shown that satisfiability of LL formulas can be decided in exponential time.

Subjective bayesian methods for rule-based inference systems

A subjective Bayesian inference method that realizes some of the advantages of both formal and informal approaches, and modifications needed to deal with the inconsistencies usually found in collections of subjective statements are described.

Evidential reasoning: a developing concept

This approach, based on a relatively new mathematical theory of evidence, is contrasted with those approaches based on Bayesian probability models and has some significant advantages, particularly its ability to represent and reason from bounded ignorance.

A Method of Computing Generalized Bayesian Probability Values for Expert Systems

A new method for calculating the conditional probability of any multi-valued predicate given particular information about the individual case is presented, based on the principle of Maximum Entropy (ME), and gives the most unbiased probability estimate given the available evidence.

Probabilistic Inference and the Concept of Total Evidence

My purpose is to examine a cluster of issues centering around the socalled statistical syllogism and the concept of total evidence. The kind of paradox that is alleged to arise from uninhibited use

Evidential Reasoning: An Implementation for Multisensor Integration

This paper characterizes evidence as information that is uncertain, incomplete, and sometimes inaccurate, and concludes that evidential reasoning requires both a method for pooling multiple bodies of evidence to arrive at a consensus and some means of drawing the appropriate conclusions from that consensus.

Efficient Minimum Information Updating for Bayesian Inferencing in Expert Systems

A new algorithm for minimun information Bayesian Inferencing within Expert Systems is presented and it is proved that it does indeed satisfy minimum information criteria.

Studies in the logic of confirmation, in: Aspects of Scientific Explanation and other Essays in the Philosophy of

  • Science
  • 1965

Computer-based medical consultations

  • Computer-based medical consultations
  • 1976