Vol. 2 No. 2 (2022): Journal of AI-Assisted Scientific Discovery
Articles

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

Srihari Maruthi
University of New Haven, West Haven, CT, United States
Sarath Babu Dodda
Central Michigan University, MI, United States
Ramswaroop Reddy Yellu
Independent Researcher, USA
Praveen Thuniki
Independent Researcher & Program Analyst, Georgia, United States
Surendranadha Reddy Byrapu Reddy
Sr. Data Architect at Lincoln Financial Group, Greensboro, NC, United States
Cover

Published 30-12-2022

Keywords

  • Temporal reasoning,
  • AI systems,
  • dynamic environments,
  • interval-based reasoning

How to Cite

[1]
S. Maruthi, S. Babu Dodda, R. Reddy Yellu, P. Thuniki, and S. Reddy Byrapu Reddy, “Temporal Reasoning in AI Systems: Studying temporal reasoning techniques and their applications in AI systems for modeling dynamic environments”, Journal of AI-Assisted Scientific Discovery, vol. 2, no. 2, pp. 22–28, Dec. 2022, Accessed: Nov. 10, 2024. [Online]. Available: https://scienceacadpress.com/index.php/jaasd/article/view/16

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.

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