English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Pro

Presents the zaia usage as premium feature

ZAIA

Presents AI Discovery search usage as a premium feature

AI Discovery

Presents AI Discovery explain and summary usage as a premium feature

Content Analysis

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Presents the pdf analysis as premium feature

PDF Analysis

1500 Top-up

3000 Top-up

Zendy Basic

2500 Top-up

Zendy Unlimited

Presents the access of premium content as premium feature

Premium Content

Rule based languages focussed in last decade on the use of term rewriting as a modeling tool [5]. To extend modeling capabilities of these languages, we explored recently the possibility of making the rule applications subject to probabilistic choices [2]. Dealing with rewriting with probabilistic firing of rules leads to numerous problems about the understanding of the underlying theoretical notions and results. In [2], we started to discuss what could be the generalizations of the classical notions in rewriting community for abstract reduction systems [1]. The next natural step is to understand if a generalization of equational proof theory for probabilistic systems exists. Our first attempt to do so yielded the theory which is presented in this abstract and which is actually closer to fuzzy logic than probabilities [3].

A Generalization of Equational Proof Theory?