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]: Bottom-up logic programming can be used to declaratively specify many algorithms in a succinct and natural way, and McAllester and Ganzinger have shown that it is possible to define a cost semantics that enables reasoning about the running time of algorithms written as inference rules. Previous work with the programming language Lollimon demonstrates the expressive power of logic programming with linear logic in describing algorithms that have imperative elements or that must repeatedly make mutually exclusive choices. In this paper, we identify a bottom-up logic programming language based on linear logic that is amenable to efficient execution and describe a novel cost semantics that can be used for complexity analysis of algorithms expressed in linear logic.

[2]: This paper investigates the relationship between the Logical Algorithms (LA) language of Ganzinger and McAllester and Constraint Handling Rules (CHR). We present a translation schema from LA to CHRrp: CHR with rule priorities, and show that the meta-complexity theorem for LA can be applied to a subset of CHRrp via inverse translation. Inspired by the high-level implementation proposal for Logical Algorithm by Ganzinger and McAllester and based on a new scheduling algorithm, we propose an alternative implementation for CHRrp that gives strong complexity guarantees and results in a new and accurate meta-complexity theorem for CHRrp. It is furthermore shown that the translation from Logical Algorithms to CHRrp combined with the new CHRrp implementation satisfies the required complexity for the Logical Algorithms meta-complexity result to hold.

[3]:
Linear logical algorithms [1] (LLA) is a programming language based on bottom-up reasoning in linear logic, inspired by logical algorithms [3] . The first implementation of logical algorithms was realized in CHR with rule priorities [2] .