论文标题
最大算法口径和算法因果网络推断：现实世界一般智能的一般原理？
Maximal Algorithmic Caliber and Algorithmic Causal Network Inference: General Principles of Real-World General Intelligence?
论文作者
论文摘要
远距离平衡热力学的思想和形式主义通过遵循和扩展Tadaki的算法热力学来移植到随机计算过程的背景下。提出了最大算法才能的原则，为指导哪些计算过程进行指导，应该假设是否提供了在其中工作的约束。据推测，在适当的假设下，遵守算法马尔可夫条件的计算过程将最大化算法口径。有人提出，根据此，现实世界的认知系统可能会通过对其环境进行建模并选择其动作为（近似和紧凑）算法Markov网络，从而在很大程度上发挥作用。这些想法被认为是朝着务实的一般智能系统运作的一般理论的潜在早期步骤。
Ideas and formalisms from far-from-equilibrium thermodynamics are ported to the context of stochastic computational processes, via following and extending Tadaki's algorithmic thermodynamics. A Principle of Maximum Algorithmic Caliber is proposed, providing guidance as to what computational processes one should hypothesize if one is provided constraints to work within. It is conjectured that, under suitable assumptions, computational processes obeying algorithmic Markov conditions will maximize algorithmic caliber. It is proposed that in accordance with this, real-world cognitive systems may operate in substantial part by modeling their environments and choosing their actions to be (approximate and compactly represented) algorithmic Markov networks. These ideas are suggested as potential early steps toward a general theory of the operation of pragmatic generally intelligent systems.
