$μλεδ$-Calculus: A Self Optimizing Language that Seems to Exhibit Paradoxical Transfinite Cognitive Capabilities

Ronie Salgado
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Abstract

Formal mathematics and computer science proofs are formalized using Hilbert-Russell-style logical systems which are designed to not admit paradoxes and self-refencing reasoning. These logical systems are natural way to describe and reason syntactic about tree-like data structures. We found that Wittgenstein-style logic is an alternate system whose propositional elements are directed graphs (points and arrows) capable of performing paraconsistent self-referencing reasoning without exploding. Imperative programming language are typically compiled and optimized with SSA-based graphs whose most general representation is the Sea of Node. By restricting the Sea of Nodes to only the data dependencies nodes, we attempted to stablish syntactic-semantic correspondences with the Lambda-calculus optimization. Surprisingly, when we tested our optimizer of the lambda calculus we performed a natural extension onto the $\mu\lambda$ which is always terminating. This always terminating algorithm is an actual paradox whose resulting graphs are geometrical fractals, which seem to be isomorphic to original source program. These fractal structures looks like a perfect compressor of a program, which seem to resemble an actual physical black-hole with a naked singularity. In addition to these surprising results, we propose two additional extensions to the calculus to model the cognitive process of self-aware beings: 1) $\epsilon$-expressions to model syntactic to semantic expansion as a general model of macros; 2) $\delta$-functional expressions as a minimal model of input and output. We provide detailed step-by-step construction of our language interpreter, compiler and optimizer.
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μλεδ$-微积分:一种似乎具有自相矛盾的无限认知能力的自我优化语言
形式数学和计算机科学的证明是通过希尔伯特-鲁塞尔式逻辑系统形式化的,这些逻辑系统的设计不允许悖论和自反推理。这些逻辑系统是描述树状数据结构并进行语法推理的自然方法。我们发现维特根斯坦式逻辑是另一种系统,它的命题元素是有向图(点和箭头),能够执行准一致自引用推理而不会爆炸。命令式编程语言通常使用基于 SSA 的图进行编译和优化,而 SSA 的最一般表示形式是节点海。通过将 "节点之海 "限制为只有数据依赖关系节点,我们试图建立与 Lambda 计算优化的语法语义对应关系。令人惊讶的是,当我们对λ演算法的优化器进行测试时,我们对总是终止的$\mu\lambda$进行了自然扩展。这个总是终止的算法是一个实际的悖论,它产生的图是几何分形,似乎与原始源程序同构。这些分形结构看起来就像一个程序的完美压缩器,似乎类似于一个具有赤裸裸奇点的真实物理黑洞。除了这些令人惊讶的结果之外,我们还提出了两个对微积分的额外扩展,以模拟具有自我意识的生物的认知过程:1)$epsilon$表达式,作为宏的一般模型来模拟句法到语义的扩展;2)$elta$函数表达式,作为输入和输出的最小模型。我们将逐步详细地构建我们的语言解释器、编译器和优化器。
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