Representing uncertainty is fundamental in the construction of intelligent systems. A standardized system of symbols and expressions is utilized to quantify likelihoods and relationships between events. For example, the probability of event A occurring is commonly denoted as P(A). The conditional probability of event A occurring given that event B has already occurred is represented as P(A|B). These notations form the bedrock for expressing probabilistic models.
This symbolic language offers a concise and unambiguous way to encode knowledge and reason under uncertainty. Its utilization facilitates the development of algorithms that can effectively handle incomplete or noisy data, characteristic of real-world applications. Its adoption has significantly impacted the trajectory of intelligent system development, allowing for more robust and adaptable solutions.
