哪种程序设计语言最适合集成式AGI？
What Kind of Programming Language Best Suits Integrative AGI?
摘要：
什么样的程序设计语言最适合于服务于AGI的集成、多范式、多软件系统方法的需求？这个问题是通过探索如何创建一个更可扩展和可用的“Atomese”程序设计语言版本（“Atomese 2.0”）的更具体的问题来提出的，该语言是OpenCog AGI设计的关键组件。初步建议Atomese 2.0的核心应该是一个非常灵活的重写元数据库规则的框架（其中规则本身在同一元数据库中表示，并且在规则解释过程中创建和使用的一些中间数据可以在同一元数据库中表示）。这个框架应该支持并发重写的元数据，根据标记有各种各样的不确定性量化的规则，并与各种类型系统相关联的各种类型标记。应该使用渐进的类型化方法来实现与各种类型系统相关联的规则和其他元数据库节点/链接的混合，以及与任何类型系统都不关联的无类型元数据库节点/链接。这必须以允许合理的效率和可伸缩性的方式来完成，包括在并发和分布式处理上下文中，在其中大部分处理时间被占用以评估对大型元数据集的特定子图的静态模式匹配查询（包括丰富多样的查询，诸如针对表示变量的节点的匹配以及针对整个子图的匹配等）的情况下。
英文摘要：
What kind of programming language would be most appropriate to serve the needs of integrative, multi-paradigm, multi-software-system approaches to AGI? This question is broached via exploring the more particular question of how to create a more scalable and usable version of the "Atomese" programming language that forms a key component of the OpenCog AGI design (an "Atomese 2.0") . It is tentatively proposed that the core of Atomese 2.0 should be a very flexible framework of rewriting rules for rewriting a metagraph (where the rules themselves are represented within the same metagraph, and some of the intermediate data created and used during the rule-interpretation process may be represented in the same metagraph). This framework should support concurrent rewriting of the metagraph according to rules that are labeled with various sorts of uncertainty-quantifications, and that are labeled with various sorts of types associated with various type systems. A gradual typing approach should be used to enable mixture of rules and other metagraph nodes/links associated with various type systems, and untyped metagraph nodes/links not associated with any type system. This must be done in a way that allows reasonable efficiency and scalability, including in concurrent and distributed processing contexts, in the case where a large percentage of of processing time is occupied with evaluating static pattern-matching queries on specific subgraphs of a large metagraph (including a rich variety of queries such as matches against nodes representing variables, and matches against whole subgraphs, etc.).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
