Temporal Reasoning in AI Systems: Studying temporal reasoning techniques and their applications in AI systems for modeling dynamic environments

Abstract

Temporal reasoning is crucial for AI systems to understand and model dynamic environments where events occur and evolve over time. This paper provides an overview of temporal reasoning techniques in AI systems, highlighting their importance and applications. We discuss various temporal reasoning models, including interval-based, point-based, and qualitative reasoning approaches. Additionally, we explore how these techniques are applied in AI systems for tasks such as planning, scheduling, and understanding natural language. The paper concludes with a discussion on future directions and challenges in temporal reasoning for AI systems.