论文标题
使用对象因认知程序的快速概念推断模型
A Model of Fast Concept Inference with Object-Factorized Cognitive Programs
论文作者
论文摘要
事实证明，人类从少数图像中快速识别一般概念的能力很难用机器人模仿。最近，开发了一个计算机架构，该架构允许机器人通过使用一组原始认知功能的指令将概念作为认知程序进行建模，从而模仿人类能力的某些方面。这使一个机器人可以通过模拟世界模型中的候选程序来模仿人类的想象力，然后再将其推广到物理世界。但是，该模型使用了一种幼稚的搜索算法，该算法需要30分钟才能发现一个概念，并且对具有20多个说明的程序变得棘手。为了避免这种瓶颈，我们提出了一种算法，该算法模仿了对象分解和子目标的人类认知启发式，从而允许人类水平的推断速度，提高准确性并使产出更具解释性。
The ability of humans to quickly identify general concepts from a handful of images has proven difficult to emulate with robots. Recently, a computer architecture was developed that allows robots to mimic some aspects of this human ability by modeling concepts as cognitive programs using an instruction set of primitive cognitive functions. This allowed a robot to emulate human imagination by simulating candidate programs in a world model before generalizing to the physical world. However, this model used a naive search algorithm that required 30 minutes to discover a single concept, and became intractable for programs with more than 20 instructions. To circumvent this bottleneck, we present an algorithm that emulates the human cognitive heuristics of object factorization and sub-goaling, allowing human-level inference speed, improving accuracy, and making the output more explainable.
