机器推理的进展与展望

机器学习算法的发展仍受到泛化能力较弱、鲁棒性较差、缺乏可解释性等问题的限制.文中介绍机器推理,说明推理对于机器学习人的知识和逻辑、理解和解释世界的重要作用.首先分析人类大脑推理机制,从认知地图、神经元和奖赏回路,扩展到受脑启发的直觉推理、神经网络和强化学习.进而总结机器推理的方式及其相互关联的现状、进展及挑战,具体包括直觉推理、常识推理、因果推理和关系推理等.最后展望机器推理的应用前景与未来的研究方向.

Progress and Prospect of Machine Reasoning

The development of machine learning algorithms are limited by the problems,such as weak generalization ability,poor robustness and lack of interpretability.In this paper,the important role of reasoning for machine learning human knowledge and logic,understanding and interpreting the world is illustrated.Firstly,the reasoning mechanism of the human brain is studied from cognitive maps,neurons and reward circuits,to brain-inspired intuitive reasoning,neural networks and reinforcement learning.Then,the current situation,progress and challenges of machine reasoning methods and their interrelationships are summarized,including intuitive reasoning,commonsense reasoning,causal reasoning and relational reasoning.Finally,the application prospects and future research directions of machine reasoning are analyzed.