Nest: re-engineering the compositional approach to rule-based inference

Title:

Nest: re-engineering the compositional approach to rule-based inference

Creator:

Berka, Petr, Laš, Vladimír, and Svátek, Vojtěch

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:c972b182-ed8c-4d1b-bc9a-2996ed33f3bf
uuid:c972b182-ed8c-4d1b-bc9a-2996ed33f3bf

Subject:

expert systems and rule-based reasoning

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

A compositional approach to rule-based inference is now often considered as overtaken by otlier approaches. We suggest that a few relatively straightforward extensions together with state-of-the-art implementation techniques should upgrade it to a level making it a useful part of today’s knowledge erigineering inventory. The ideas developed by the authors in mid-90s have recently been incorporated into a new expert systém called Nest. In addition to the traditional network of propositions and compositional rules, Nést also supports binary, nominal and numeric attributes used for derivation of proposition weights, logical (no uncertainty) and default (no antecedent) rules, context expressions and integrity constraints. The inference mechanism combines backward and forward chaining. Uncertainty processing (based on Hájek’s algebraic theory) allows interval weights interpreted as a union of hypothetical cases, and a novel set of combination functions inspired by neural networks has been added. The system is implemented in two versions: stand-alone and web-based client-server one. A user-friendly editor covering all mentioned features is included.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2004 Volume:14 | Number:5

Harvested from:

CDK

Metadata only:

false