# Probabilistic reasoning in intelligent systems - networks of plausible inference

@inproceedings{Pearl1989ProbabilisticRI, title={Probabilistic reasoning in intelligent systems - networks of plausible inference}, author={Judea Pearl}, booktitle={Morgan Kaufmann series in representation and reasoning}, year={1989} }

From the Publisher:
Probabilistic Reasoning in Intelligent Systems is a complete andaccessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty. [... ] Key Method Specifically, network-propagation techniques serve as a mechanism for combining the theoretical coherence of probability theory with modern demands of reasoning-systems technology: modular declarative inputs, conceptually meaningful inferences, and parallel distributed computation… Expand

## 18,270 Citations

Logical and Probabilistic Reasoning to Support Information Analysis in Uncertain Domains

- Computer Science
- 2007

This work defines a formalism for the conversion of automatically generated natural deduction proof trees into Bayesian networks and shows that hard evidential updates force the conclusions of the proof to be true with probability one, regardless of any dependencies and prior probability values assumed for the causal model.

Knowledge-Based Probabilistic Reasoning from Expert Systems to Graphical Models

- Computer Science
- 2006

This chapter presents the technology supporting the traditional knowledge-based expert system, including the production system for reasoning with rules, and discusses Bayesian inference, and the adoption of simplifying techniques such as the Stanford Certainty Factor Algebra.

Decision making by intelligent agents: logical argument, probabilistic inference and the maintenance of beliefs and acts

- Computer Science, PhilosophyNMR
- 2002

How argumentation can provide a framework for integrating many different forms of uncertain reasoning as special cases into a unified agent model is discussed.

Five Useful Properties of Probabilistic Knowledge Representations From the Point of View of Intelligent Systems

- Computer ScienceFundam. Informaticae
- 1997

Five properties of probabilistic knowledge representations that are particularly useful in intelligent systems research are described.

On probabilistic inference in relational conditional logics

- Computer Science, PhilosophyLog. J. IGPL
- 2012

This work proposes two different semantics and model theories for interpreting first-order probabilistic conditional logic, addresses the problems of ambiguity that are raised by the difference between subjective and statistical views, and develops a comprehensive list of desirable properties for inductive model-based probabilism inference in relational frameworks.

Probabilistic Reasoning with Maximum Entropy - The System PIT (system description)

- Computer ScienceWLP
- 1997

A system for common sense reasoning based on propositional logic, the probability calculus and the concept of model-quantiication to deliver decisions under incomplete knowledge but to keep the necessary additional assumptions as minimal as possible.

Explanation in Probabilistic Systems: Is It Feasible? Will It Work?

- Computer Science
- 2001

It is argued that the observed discrepancies between human and probabilistic reasoning and the anticipated difficulties in building user interfaces are not a good reason for rejecting probability theory, and provide motivation for a normative treatment of uncertainty.

An axiomatic framework for propagating uncertainty in directed acyclic networks

- Computer ScienceInt. J. Approx. Reason.
- 1993

Knowledge and Data Fusion in Probabilistic Networks

- Computer Science
- 2003

Modifications of standard Bayesian learning methods are described to handle the repeated structures that occur in the Multi-Entity Bayesian Network probabilistic logic.

## References

SHOWING 1-10 OF 230 REFERENCES

Default Reasoning, Nonmonotonic Logics, and the Frame Problem

- PhilosophyAAAI
- 1986

This work provides axioms for a simple problem in temporal reasoning which has long been identified as a case of default reasoning, thus presumably amenable to representation in nonmonotonic logic, and investigates the failure of the logics considered and discusses two recent proposals for solving this problem.

Inferno: A Cautious Approach To Uncertain Inference

- Computer ScienceComput. J.
- 1983

A new system call INFERNO is introduced, which is probabilistic but makes no assumptions whatsoever about the joint probability distributions of pieces of knowledge, so the correctness of inferences can be guaranteed.

Semantic Networks and Neural Nets

- Computer Science
- 1984

This report describes a direct way of realizing semantic networks with neuron-like computing units that obviates the need for a centralized knowledge base interpreter and embodies an evidential semantics for knowledge that provides a natural treatment of defaults, exceptions and inconsistent or conflicting information.

RUM: A Layered Architecture for Reasoning with Uncertainty

- Computer ScienceIJCAI
- 1987

New reasoning techniques for dealing with uncertainty in expert systems have been embedded in RUM, a Reasoning with Uncertainty Module. RUM is an integrated software tool based on a frame system…

A Method for Managing Evidential Reasoning in a Hierarchical Hypothesis Space

- Computer Science, MathematicsArtif. Intell.
- 1985

An inquiry into computer understanding

- Computer ScienceComput. Intell.
- 1988

This paper shows that the difficulties McDermott described are a result of insisting on using logic as the language of commonsense reasoning, and if (Bayesian) probability is used, none of the technical difficulties found in using logic arise.

SOME PHILOSOPHICAL PROBLEMS FROM THE STANDPOINT OF ARTI CIAL INTELLIGENCE

- Philosophy, Computer Science
- 1981

A Synthetic View of Approximate Reasoning Techniques

- Computer ScienceIJCAI
- 1983

This paper presents a review of different approximate reasoning techniques which have been proposed for dealing with uncertain or imprecise knowledge, especially in expert systems based on production…

A Framework for Evidential-Reasoning Systems

- Computer ScienceAAAI
- 1986

The formal basis and a framework for the implementation of automated reasoning systems based upon these techniques are presented, based upon the Dempster-Shafer theory of belief functions.