Abstract:
Developing models and structures to efficiently capture and organize information for reasoning and problem-solving, a crucial aspect known as knowledge representation, holds significant importance in the realms of artificial intelligence and cognitive science. In recent times, the utilization of statistical and probabilistic reasoning has emerged as powerful technique for effective knowledge representation. This article employs these methods to delve into diverse facets of knowledge representation, encompassing fundamental principles, methodologies, applications, and challenges. As real-world problems grow in complexity, knowledge representation has undergone a transformation to encompass more nuanced and robust decision-making processes. This study explores the paradigm shift towards knowledge representation utilizing statistical and probabilistic reasoning, presenting a more adaptable approach by integrating uncertainty into knowledge models. Unlike conventional methods like symbolic logic, which grapple with challenges in handling incomplete information and uncertainty, the newer approaches prove more adept. Theoretical foundations and practical applications of statistical and probabilistic reasoning, including but not limited to Bayesian networks, Markov networks, and influence diagrams, are thoroughly examined across various domains such as healthcare, finance, and natural language processing. These models facilitate probabilistic knowledge representation and enhance decision-making processes, rendering them invaluable for sound reasoning under conditions of uncertainty.
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