论文标题
机器常识
Machine Common Sense
论文作者
论文摘要
机器常识在人工智能（AI）中仍然是一个广泛的，潜在的无限问题。可以采用各种各样的策略来取得这一挑战。本文讨论了建模常识性推理的各个方面，重点是人际关系互动等领域。基本思想是，常识性推理有几种类型：一种体现在逻辑上的身体行动层面，另一个涉及对人类互动本质的理解。基于正式逻辑和人工神经网络的现有方法仅允许建模第一类常识。为了建模第二种类型，了解人类行为的动机和规则至关重要。该模型基于现实生活中的启发式方法，即通过不同世代的知识和经验而开发的经验法则。这样的知识基础允许开发具有推理和解释机制的专家系统（常识性推理算法和个人模型）。算法为情况分析提供了工具，而个人模型则可以识别人格特征。如此设计的系统应在包括人机在内的相互作用中执行放大智能的功能。
Machine common sense remains a broad, potentially unbounded problem in artificial intelligence (AI). There is a wide range of strategies that can be employed to make progress on this challenge. This article deals with the aspects of modeling commonsense reasoning focusing on such domain as interpersonal interactions. The basic idea is that there are several types of commonsense reasoning: one is manifested at the logical level of physical actions, the other deals with the understanding of the essence of human-human interactions. Existing approaches, based on formal logic and artificial neural networks, allow for modeling only the first type of common sense. To model the second type, it is vital to understand the motives and rules of human behavior. This model is based on real-life heuristics, i.e., the rules of thumb, developed through knowledge and experience of different generations. Such knowledge base allows for development of an expert system with inference and explanatory mechanisms (commonsense reasoning algorithms and personal models). Algorithms provide tools for a situation analysis, while personal models make it possible to identify personality traits. The system so designed should perform the function of amplified intelligence for interactions, including human-machine.
