Characterizations of Hemirings Based on Probability Spaces

The notion of falling fuzzy h-ideals of a hemiring is introduced on the basis of the theory of falling shadows and fuzzy sets. Then the relations between fuzzy h-ideals and falling fuzzy h-ideals are described. In particular, by means of falling fuzzy h-ideals, the charac-terizations of h-hemiregular hemirings are investigated based on independent (prefect positive correlation) probability spaces

Doubt fuzzy BCI-algebras

The aim of this note is to introduce the notion of doubt fuzzy p-ideals in BCI-algebras and to study their properties. We also solve the problem of classifying doubt fuzzy p-ideals and study fuzzy relations on BCI-algebras

Soft p-ideals of soft BCI-algebras

AbstractMolodtsov [D. Molodtsov, Soft set theory–First results, Comput. Math. Appl. 37 (1999) 19–31] introduced the concept of soft set as a new mathematical tool for dealing with uncertainties that is free from the difficulties that have troubled the usual theoretical approaches. Jun [Y. B. Jun, Soft BCK/BCI-algebras, Comput. Math. Appl. 56 (2008) 1408–1413] applied first the notion of soft sets by Molodtsov to the theory of BCK/BCI-algebras. In this paper we introduce the notion of soft p-ideals and p-idealistic soft BCI-algebras, and then investigate their basic properties. Using soft sets, we give characterizations of (fuzzy) p-ideals in BCI-algebras. We provide relations between fuzzy p-ideals and p-idealistic soft BCI-algebras

Fuzzy hh-ideals of hemirings

A characterization of an $h$-hemiregular hemiring in terms of a fuzzy $h$-ideal is provided. Some properties of prime fuzzy $h$-ideals of $h$-hemiregular hemirings are investigated. It is proved that a fuzzy subset $\zeta$ of a hemiring $S$ is a prime fuzzy left (right) $h$-ideal of $S$ if and only if $\zeta$ is two-valued, $\zeta(0) = 1$, and the set of all $x$ in $S$ such that $\zeta(x) = 1$ is a prime (left) right $h$-ideal of $S$. Finally, the similar properties for maximal fuzzy left (right) $h$-ideals of hemirings are considered

On f

The notion of left-right (resp., right-left) f-derivation of a BCI-algebra is introduced, and some related properties are investigated. Using the idea of regular f-derivation, we give characterizations of a p-semisimple BCI-algerba

On Rough Hyperideals in Hyperlattices

We introduce and study rough hyperideals in hyperlattices. First, we give some interesting examples of hyperlattices and introduce hyperideals of hyperlattices. Then, applying the notion of rough sets to hyperlattices, we introduce rough hyperideals in hyperlattices, which are extended notions of hyperideals of hyperlattices. In addition, we consider rough hyperideals in Cartesian products and quotients of hyperlattices. Finally, we investigate some properties about homomorphic images of rough hyperideals in hyperlattices

A New Extended Soft Intersection Set to M

Molodtsov’s soft set theory provides a general mathematical framework for dealing with uncertainty. The concepts of (M,N)-SI implicative (Boolean) filters of BL-algebras are introduced. Some good examples are explored. The relationships between (M,N)-SI filters and (M,N)-SI implicative filters are discussed. Some properties of (M,N)-SI implicative (Boolean) filters are investigated. In particular, we show that (M,N)-SI implicative filters and (M,N)-SI Boolean filters are equivalent

H2O+: An Improved Framework for Hybrid Offline-and-Online RL with Dynamics Gaps

Solving real-world complex tasks using reinforcement learning (RL) without high-fidelity simulation environments or large amounts of offline data can be quite challenging. Online RL agents trained in imperfect simulation environments can suffer from severe sim-to-real issues. Offline RL approaches although bypass the need for simulators, often pose demanding requirements on the size and quality of the offline datasets. The recently emerged hybrid offline-and-online RL provides an attractive framework that enables joint use of limited offline data and imperfect simulator for transferable policy learning. In this paper, we develop a new algorithm, called H2O+, which offers great flexibility to bridge various choices of offline and online learning methods, while also accounting for dynamics gaps between the real and simulation environment. Through extensive simulation and real-world robotics experiments, we demonstrate superior performance and flexibility over advanced cross-domain online and offline RL algorithms