The Abstraction and Reasoning Corpus (ARC) aims at benchmarking the
performance of general artificial intelligence algorithms. The ARC's focus on
broad generalization and few-shot learning has made it difficult to solve using
pure machine learning. A more promising approach has been to perform program
synthesis within an appropriately designed Domain Specific Language (DSL).
However, these too have seen limited success. We propose Abstract Reasoning
with Graph Abstractions (ARGA), a new object-centric framework that first
represents images using graphs and then performs a search for a correct program
in a DSL that is based on the abstracted graph space. The complexity of this
combinatorial search is tamed through the use of constraint acquisition, state
hashing, and Tabu search. An extensive set of experiments demonstrates the
promise of ARGA in tackling some of the complicated object-centric tasks of the
ARC rather efficiently, producing programs that are correct and easy to
understand.

本文提出一种基于图形抽象的对象中心框架，使用 DSL 进行程序综合，引入了约束获取、状态哈希和禁忌搜索，成功的解决了 ARC 的复杂对象中心任务，并生成了正确而易于理解的程序。

抽象与推理语料库的图形、约束和搜索

Graphs, Constraints, and Search for the Abstraction and Reasoning Corpus

Most business process automation is still developed using traditional
automation technologies such as workflow engines. These systems provide domain
specific languages that require both business knowledge and programming skills
to effectively use. As such, business users often lack adequate programming
skills to fully leverage these code oriented environments. We propose a
paradigm for the construction of business automations using natural language.
The approach applies a large language model to translate business rules and
automations described in natural language, into a domain specific language
interpretable by a business rule engine. We compare the performance of various
language model configurations, across various target domains, and explore the
use of constrained decoding to ensure syntactically correct generation of
output.

本研究提出了一种基于自然语言处理的业务自动化构建范例，通过应用大型自然语言模型将业务规则和自动化描述转换为业务规则引擎可解释的领域特定语言，以解决业务用户缺乏足够的编程能力的问题。实验比较了不同语言模型配置在不同目标领域下的性能，并探索了应用约束解码以确保正确生成输出的可能性。

自然语言编写商业自动化的无码低码范式

A No-Code Low-Code Paradigm for Authoring Business Automations Using Natural Language

Today's conversational agents are restricted to simple standalone commands.
In this paper, we present Iris, an agent that draws on human conversational
strategies to combine commands, allowing it to perform more complex tasks that
it has not been explicitly designed to support: for example, composing one
command to "plot a histogram" with another to first "log-transform the data".
To enable this complexity, we introduce a domain specific language that
transforms commands into automata that Iris can compose, sequence, and execute
dynamically by interacting with a user through natural language, as well as a
conversational type system that manages what kinds of commands can be combined.
We have designed Iris to help users with data science tasks, a domain that
requires support for command combination. In evaluation, we find that data
scientists complete a predictive modeling task significantly faster (2.6 times
speedup) with Iris than a modern non-conversational programming environment.
Iris supports the same kinds of commands as today's agents, but empowers users
to weave together these commands to accomplish complex goals.

本文介绍了 Iris，一个利用人类交谈策略将命令组合起来以执行更复杂任务的代理系统，并且引入了一种领域特定的语言，用于将命令转化为 Iris 可以动态地组合、排序和执行的自动机，在评估中，我们发现数据科学家使用 Iris 完成预测建模任务的速度显著提高（2.6 倍加速）， Iris 支持与今天的代理系统相同的命令类型，但赋予用户将这些命令编织在一起以实现复杂目标的能力。