Williamson, Jon
(2001)
*
Bayesian Networks for Logical Reasoning.
*
In:
Proceedings of the AAAI Fall Symposium on using Uncertainty within Computation.
Association for the Advancement of Artificial Intelligence, pp. 136-143.
(The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)
(KAR id:7396)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. | |

Official URL: http://www.aaai.org/Papers/Symposia/Fall/2001/FS-0... |

## Abstract

By identifying and pursuing analogies between causal and logical in uence I show how the Bayesian network formalism can be applied to reasoning about logical deductions. Despite the fact that logic itself is about certainty, logical reasoning takes place in a context of very little certainty. In fact the very search for a proof of a proposition is usually a search for certainty: we are unsure about the proposition and want to become sure by nding a proof or a refutation. Even the search for a better proof takes place under uncertainty: we are sure of the conclusion but not of the alternative premises or lemmas. Uncertainty is rife in mathematics, for instance. A good mathematician is one who can assess which conjectures are likely to be true, and from where a proof of a conjecture is likely to emerge | which hypotheses, intermediary steps and proof techniques are likely to be required and are most plausible in themselves. Mathematics is not a list of theorems but a web of beliefs, and mathematical propositions are constantly being evaluated on the basis of the mathematical and physical evidence available at the time. 1 Of course logical reasoning has many other applications, notably throughout the eld of articial intelligence. Planning a decision, parsing a sentence, checking a computer program, maintaining consistency of a knowledge base and deriving predictions from a model are only few of the tasks that can be considered theorem-proving problems. Finding a proof is rarely an easy matter, thus automated theorem proving and automated proof planning are important areas of active research. 2 However, current systems do not tackle uncertainty in any fundamental way. I will argue in this paper that Bayesian networks are particularly suited as a formalism for logical reasoning under uncertainty, just as they are for causal reasoning

Item Type: | Book section |
---|---|

Subjects: |
Q Science > QA Mathematics (inc Computing science) > QA273 Probabilities B Philosophy. Psychology. Religion > BC Logic Q Science > QA Mathematics (inc Computing science) > QA 75 Electronic computers. Computer science |

Divisions: | Divisions > Division of Arts and Humanities > School of Culture and Languages |

Depositing User: | Jon Williamson |

Date Deposited: | 19 Mar 2009 17:58 UTC |

Last Modified: | 10 Oct 2023 11:59 UTC |

Resource URI: | https://kar.kent.ac.uk/id/eprint/7396 (The current URI for this page, for reference purposes) |

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