Probabilistic Reasoning Models in Artificial Intelligence: Exploring Probabilistic Reasoning Models and Their Applications in Solving Complex AI Problems

Authors

  • Bhuman Vyas Senior Software Developer, Credit Acceptance Corporation, Canton, Michigan, USA

Keywords:

Probabilistic reasoning, Artificial Intelligence, Bayesian networks, Markov networks, Probabilistic graphical models, Uncertainty modeling, Decision making, Applications of probabilistic reasoning

Abstract

Probabilistic reasoning models play a pivotal role in artificial intelligence (AI), enabling machines to make decisions under uncertainty. This paper provides an in-depth exploration of probabilistic reasoning models and their applications in solving complex AI problems. We begin by elucidating the fundamental principles of probabilistic reasoning, including Bayesian networks, Markov networks, and probabilistic graphical models. Subsequently, we delve into the diverse applications of these models across various domains, such as healthcare, finance, and robotics. Through a comprehensive review of existing literature, we highlight the strengths and limitations of probabilistic reasoning models, paving the way for future research directions. This paper aims to provide researchers and practitioners with a thorough understanding of probabilistic reasoning models and inspire further advancements in AI.

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Published

10-04-2023

How to Cite

[1]
B. Vyas, “Probabilistic Reasoning Models in Artificial Intelligence: Exploring Probabilistic Reasoning Models and Their Applications in Solving Complex AI Problems”, J. of Art. Int. Research, vol. 3, no. 1, pp. 14–24, Apr. 2023.

Issue

Vol. 3 No. 1 (2023): Journal of Artificial Intelligence Research

Section

Articles

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