You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
We observe that the various formulations of the operational semantics of Constraint Handling Rules proposed over the years fall into a spectrum ranging from the analytical to the pragmatic. While existing analytical formulations facilitate program analysis and formal proofs of program properties, they cannot be implemented as is. We propose a novel operational semantics ω!, which has a strong analytical foundation, while featuring a terminating execution model. We prove its soundness and completeness with respect to existing analytical formulations and we provide an implementation in the form of a source-to-source transformation to CHR with rule priorities.

Cited Abstract(s):
[1]: Tabled resolution, which alleviates some of Prolog's termination problems, makes it possible to create practical applications from high-level declarative specifications. Constraint Handling Rules (CHR) is an elegant framework for implementing constraint solvers from high-level specifications, and is available in many Prolog systems. However, applications combining the power of these two declarative paradigms have been impractical since traditional CHR implementations interact poorly with tabling. In this paper we present a new (set-based) semantics for CHR which enables efficient integration with tabling. The new semantics coincides with the traditional (multi-set-based) semantics for a large class of CHR programs. We describe CHRd, an implementation based on the new semantics. CHRd uses a distributed constraint store that can be directly represented in tables. Although motivated by tabling, CHRd works well also on non-tabled platforms. We present experimental results which show that, relative to traditional implementations, CHRd performs significantly better on tabled programs, and yet shows comparable results on non-tabled benchmarks.
The authors of [1] justify their set-based approach by the following statement: Our approach can be understood as a compromise since we avoid a propagation history by imposing an implicit set semantics on persistent constraints. The distinction between linear and persistent constraints, however, allows us to restrict the set behavior to those constraints, whereas the multiset semantics is preserved for linear constraints.