Uncertain linear constraints

Nic Wilson

Uncertain linear constraints

Nic Wilson

Research output: Chapter in Book/Report/Conference proceedings › Chapter › peer-review

Abstract

Linear constraints occur naturally in many reasoning problems and the information that they represent is often uncertain. There is a difficulty in applying many AI uncertainty formalisms to this situation, as their representation of the underlying logic, either as a mutually exclusive and exhaustive set of possibilities, or with a propositional or a predicate logic, is inappropriate (or at least unhelpful). To overcome this, we express reasoning with linear constraints as a logic, and develop the formalisms based on this different underlying logic. We focus in particular on a possibilistic logic representation of uncertain linear constraints, a lattice-valued possibilistic logic, and a Dempster-Shafer representation.

Original language	English
Title of host publication	ECAI 2004 - 16th European Conference on Artificial Intelligence, including Prestigious Applications of Intelligent Systems, PAIS 2004 - Proceedings
Editors	Ramon Lopez de Mantaras, Lorenza Saitta
Publisher	IOS Press BV
Pages	231-235
Number of pages	5
ISBN (Electronic)	9781586034528
Publication status	Published - 2004
Event	16th European Conference on Artificial Intelligence, ECAI 2004 - Valencia, Spain Duration: 22 Aug 2004 → 27 Aug 2004

Publication series

Name	Frontiers in Artificial Intelligence and Applications
Volume	110
ISSN (Print)	0922-6389
ISSN (Electronic)	1879-8314

Conference

Conference	16th European Conference on Artificial Intelligence, ECAI 2004
Country/Territory	Spain
City	Valencia
Period	22/08/04 → 27/08/04

Cite this

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title = "Uncertain linear constraints",

abstract = "Linear constraints occur naturally in many reasoning problems and the information that they represent is often uncertain. There is a difficulty in applying many AI uncertainty formalisms to this situation, as their representation of the underlying logic, either as a mutually exclusive and exhaustive set of possibilities, or with a propositional or a predicate logic, is inappropriate (or at least unhelpful). To overcome this, we express reasoning with linear constraints as a logic, and develop the formalisms based on this different underlying logic. We focus in particular on a possibilistic logic representation of uncertain linear constraints, a lattice-valued possibilistic logic, and a Dempster-Shafer representation.",

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booktitle = "ECAI 2004 - 16th European Conference on Artificial Intelligence, including Prestigious Applications of Intelligent Systems, PAIS 2004 - Proceedings",

address = "Netherlands",

note = "16th European Conference on Artificial Intelligence, ECAI 2004 ; Conference date: 22-08-2004 Through 27-08-2004",

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Wilson, N 2004, Uncertain linear constraints. in RL de Mantaras & L Saitta (eds), ECAI 2004 - 16th European Conference on Artificial Intelligence, including Prestigious Applications of Intelligent Systems, PAIS 2004 - Proceedings. Frontiers in Artificial Intelligence and Applications, vol. 110, IOS Press BV, pp. 231-235, 16th European Conference on Artificial Intelligence, ECAI 2004, Valencia, Spain, 22/08/04.

Uncertain linear constraints. / Wilson, Nic.
ECAI 2004 - 16th European Conference on Artificial Intelligence, including Prestigious Applications of Intelligent Systems, PAIS 2004 - Proceedings. ed. / Ramon Lopez de Mantaras; Lorenza Saitta. IOS Press BV, 2004. p. 231-235 (Frontiers in Artificial Intelligence and Applications; Vol. 110).

Research output: Chapter in Book/Report/Conference proceedings › Chapter › peer-review

TY - CHAP

T1 - Uncertain linear constraints

AU - Wilson, Nic

PY - 2004

Y1 - 2004

N2 - Linear constraints occur naturally in many reasoning problems and the information that they represent is often uncertain. There is a difficulty in applying many AI uncertainty formalisms to this situation, as their representation of the underlying logic, either as a mutually exclusive and exhaustive set of possibilities, or with a propositional or a predicate logic, is inappropriate (or at least unhelpful). To overcome this, we express reasoning with linear constraints as a logic, and develop the formalisms based on this different underlying logic. We focus in particular on a possibilistic logic representation of uncertain linear constraints, a lattice-valued possibilistic logic, and a Dempster-Shafer representation.

AB - Linear constraints occur naturally in many reasoning problems and the information that they represent is often uncertain. There is a difficulty in applying many AI uncertainty formalisms to this situation, as their representation of the underlying logic, either as a mutually exclusive and exhaustive set of possibilities, or with a propositional or a predicate logic, is inappropriate (or at least unhelpful). To overcome this, we express reasoning with linear constraints as a logic, and develop the formalisms based on this different underlying logic. We focus in particular on a possibilistic logic representation of uncertain linear constraints, a lattice-valued possibilistic logic, and a Dempster-Shafer representation.

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M3 - Chapter

AN - SCOPUS:85017338128

T3 - Frontiers in Artificial Intelligence and Applications

SP - 231

EP - 235

BT - ECAI 2004 - 16th European Conference on Artificial Intelligence, including Prestigious Applications of Intelligent Systems, PAIS 2004 - Proceedings

A2 - de Mantaras, Ramon Lopez

A2 - Saitta, Lorenza

PB - IOS Press BV

T2 - 16th European Conference on Artificial Intelligence, ECAI 2004

Y2 - 22 August 2004 through 27 August 2004

ER -

Uncertain linear constraints

Abstract

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