让AI学会推断事件持续时间，提升理解语言和规划的能力
Temporal Reasoning in AI systems
- 用离散生存函数推断事实的持续时间
- 在约束条件下实现更准确的时序推理
- 适合需要时间逻辑的智能系统开发者
大规模常识性时序推理是认知系统的核心挑战。许多任务，如自然语言理解与规划，都需要正确推断谓词持续的时间。当前多数AI系统因缺乏演绎闭包，无法准确外推已有谓词与事件的信息。本文探讨了在Cyc知识库中实现鲁棒时序预测所需的知识表示与推理机制。通过分析事件如何启动或结束谓词的风险期，采用离散生存函数来外推给定谓词的持续区间，并可被时间约束与其他常识知识截断。实验表明，该方法显著提升了问答任务的表现。
原文摘要 · Abstract (English)
Commonsense temporal reasoning at scale is a core problem for cognitive systems. The correct inference of the duration for which fluents hold is required by many tasks, including natural language understanding and planning. Many AI systems have limited deductive closure because they cannot extrapolate information correctly regarding existing fluents and events. In this study, we discuss the knowledge representation and reasoning schemes required for robust temporal projection in the Cyc Knowledge Base. We discuss how events can start and end risk periods for fluents. We then use discrete survival functions, which represent knowledge of the persistence of facts, to extrapolate a given fluent. The extrapolated intervals can be truncated by temporal constraints and other types of commonsense knowledge. Finally, we present the results of experiments to demonstrate that these methods obtain significant improvements in terms of Q/A performance.
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