Artificial intelligence

# Probabilistic Reasoning and Uncertainty in AI ## Introduction

It is one of the most important statistical tools when it comes to finding uncertainty in the data set, model, or system. Probabilistic reasoning works with the help of logic and probability to find the uncertainty. It is the representation of knowledge in a system where one can apply probability in order to find out the uncertainty in the knowledge. This technique can be important for those models with the help of which the user wants to measure the real-world data. This data is mostly temperature, experimental errors, incredible news, and climate change. With the help of this technique, the model refined the data and helps to improve the situation.

While comparing this technique with a simple mathematical technique it is important to know some points. In mathematical techniques which have been used widely, one can get the exact values in some real numbers but in this technique, we can get the uncertainty of the data with the help of which the model can take the decision on further changes in the environment. Let us say there is a model with the help of which we can measure the temperature, if this model is trained on the basis of probabilistic reasoning then the model can manipulate its finding by ignoring garbage values all by itself and cater only to those values which are valuable for our data.

While comparing this probabilistic technique with the conventional logic technique, the research shows that in probabilistic reasoning there is a factor of logic involved with the probability as well, whereas in traditional logic, it works only on logic and there is no uncertainty available here.

## How Probabilistic Reasoning works in AI

While working in machine learning, artificial intelligence, and deep learning there are many possibilities that we can’t be sure of the possible outcomes which the model can face in the future. In other words when the number of possibilities of the results is too large that we cannot list them down then probabilistic reasoning comes into play. Bayes’ theorem’s acyclic graph helps to achieve the probabilistic graph for the model so that the model can work with the full flow. So, we can say that probabilistic reasoning basically works on the principle of Bayes’ theorem. In artificial intelligence or similar fields, probabilistic reasoning can help us to get estimate whether what will be the possibility of the event which can occur. With the help of probabilistic reasoning, the model can behave correctly and helps to achieve better results as compared to those models which work only on the traditional logic.

To explain better there are some examples of probabilistic reasoning from our daily lives. With the help of these, the reader can get a better overview of the probabilistic reasoning and concepts can become clearer as we further proceed. Following are some examples of probabilistic reasoning.

• While tossing a coin, it is not possible for us to find the outcome whether it will be heads or tail but with the help of probabilistic reasoning, we can give some weights to each of the possibilities and somehow estimate the results.
• During the rolling of a dice, it is never predictable which number will occur, only we can get an idea from the probability approach by rolling the dice a number of times and maintaining a data set.
• While picking a card from a deck of 52 cards we aren’t able to tell which card we will pick. Magicians often use this probabilistic reasoning technique to confuse people and show their magic.
• Similarly, the most common example while explaining probabilistic reasoning is basically the temperature. What will be the temperature tomorrow? No one exactly knows this answer, but we can estimate, predict, and learn from the weather behavior about the situation of the weather.

## Important Factors for Probabilistic Reasoning

While working on probabilistic reasoning there are some of the most important factors on which probability reasoning depends. Each of these factors with their brief explanation is given as follows.

• Estimation is always an important point, not only in probabilistic reasoning but also in the whole of artificial intelligence. With the help of estimation, we can list down the possible outcomes from certain experiments, also the results obtained from the probabilistic reasoning are also estimated not the exact ones, but they are far better as compared to the exact values which we can predict in the early stages.
• The likelihood is again another important point in probabilistic reasoning. As we can compile all the related results, in similar conditions and preload the model so the model can better estimate the values while measuring certain values or taking decisions.
• Observation is the least important factor in probabilistic reasoning. With the help of observation, the model finds the change in the environment by taking some early decisions, and on the basis of those results the model keeps on improving with the help of which the estimation gets better, and hence we can obtain far better results.

There are many advantages of this technique. Some of them are listed below.

• With the help of this technique, possible outcomes can be determined.
• By using probabilistic reasoning, we can estimate the outcome of those factors or models or systems in which the exact values can’t be obtained.
• The system gets better and better by observing the nature of the external climate and helps to improve the estimation.
• The chance of error becomes very small as the model is trained progressively.
• The system can get the data from the previous experiments as well and with the help of those results, the system learns better, and hence the user gets a better output or better-estimated

## Final Thoughts

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