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Probabilistic Reasoning in AI - A way to deal with Uncertainty

In this tutorial, we will learn about the probability theory probabilistic reasoning while dealing with Uncertainty. We will study what probability theory is, how an agent implements probabilistic reasoning in its decision making and we will also study how this theory solves the problem of uncertainty in the environment of the agent. By Monika Sharma Last updated : April 15, 2023


As we know that there are many cases where the answer to the problem is neither completely true nor completely false. For example, the statement- "Student will pass in the board exams". We cannot say anything about a student's result before the results are declared. However, we can draw some predictions based on the student's past performances in academics.

Probabilistic Reasoning

In these types of situations, probabilistic theory can help us give an estimate of how much an event is likely to occur or happen? In this theory, we find the probabilities of all the alternatives that are possible in any experiment. The sum of all these probabilities for an experiment is always 1 because all these events/alternatives can happen only within this experiment.


As in the above example, the statement can either be true or false, not anything other than that. That means, the student will either pass in board exams or will fail. So, if we are given the following probability:

P (Student will pass in board exams) = 0.80
Therefore, P (Student will fail in board exams) = 0.20

Then this means that there are 80 percent chances that the student will pass and 20 percent chances that the student will fail. And as we can observe that, the probability that one of these events will occur is 100 percent.

Therefore, in all those cases where there is a fixed number of outcomes possible for any given experiment, the probabilistic theory is applicable.

Another example in this theory can be taken of picking a card from a deck (Excluding the Joker). If the stated events in this experiment are as follows:

    A: The chosen card is of Spade
    B: The chosen card is of Hearts
    C: The chosen card is of Clubs
    D: The chosen card is of Diamond

    Then the probability of each event is:
    P(A) = P(B) = P(C) = P(D) = 0.25

AS there are 13 cards of each of them in a deck. And the probability of the events to occur when the experiment is taking place successfully is:

    P(E)= P(A) + P(B) + P(C) + P(D)= 1

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