时态结构中的恒常联系因果学习

人工智能研究中，行动这一概念通常在理论框架中有完全的定义。然而，现实中的行动有时难以完全刻画。智能体需要从过去的经验观察中习得行动的后果。本文提出一种基于时态结构的行动一结果学习理论。在自然数时间结构中，智能体通过观察过去的恒常联系建立因果关系。智能体依据己建立的因果关系指导将来的行动。同时，我们给出关于该理论的一个完全的逻辑演绎系统，并给出基于该逻辑的智能体行动的有效算法。

Effect Learning by Constant Conjunction in a Temporal Structure

Actions in Artificial Intelligence, such as in planning and situation calculus, are well defined. Effects of actions are given in the design. In reality, however, not all actions＇ effects are known by the agent. She must learn and attribute the causation between actions and effects through her observations. This paper presents a logic for learning effects of actions based on a simple temporal model. Time points are modeled by natural numbers, at which the agent observes constant conjunctions in the past. By simple induction on these data, the agent attributes and revises the causation between actions and effects. Then she uses it to arrange her future actions for some given goal. A complete deductive system and a tractable algorithm of decision making by the logic are given in the paper.