Probabilistic reasoning is a form of knowledge representation in which the concept of probability is used to indicate the degree of uncertainty in knowledge. In AI, probabilistic models are used to examine data using statistical codes. It was one of the first machine learning methods. To this day, it's still widely used. The Naive Bayes algorithm is one of the most well-known algorithms in this group.

How does it work?

Probabilistic modelling provides a framework for accepting the concept of learning. The probabilistic framework specifies how to express and deploy model reservations. In scientific data analysis, predictions play a significant role. Machine learning, automation, cognitive computing, and artificial intelligence all rely heavily on them.

AI makes use of probabilistic reasoning:

• When we are uncertain about the premises
• When the number of possible predicates becomes unmanageable
• When it is known that an experiment contains an error

How does knowledge support reasoning?

Simplifying any realistic domain requires some simplifications. The act of preparing knowledge to support reasoning necessitates the omission of numerous facts, their omission or their crude summarization. Rather than ignoring or enumerating exceptions, an alternative is to summarise them, i.e., to provide some warning signs indicating which areas of the minefield are more dangerous than others. Summarization is critical for striking a reasonable balance between safety and movement speed.

One way to summarise exceptions is to assign a numerical measure of uncertainty to each proposition and then combine these measures using uniform syntactic principles, similar to how truth values are combined in logic. AI is a computational representation of intelligent behaviour and common sense reasoning. Probability theory provides a logical explanation for how belief should change in the presence of incomplete or uncertain information. AI systems are not unfamiliar with network representations. Thus, we need uncertain reasoning or probabilistic reasoning to represent uncertain knowledge.

Causes of uncertainty:

The following are some of the most common sources of uncertainty in the real world:

• The information came from unreliable sources.
• Errors in Experimental Design
• Equipment failure 
• Temperature variations
• Climate change

Why Is Probabilistic Reasoning Necessary in AI?

• when the outcome is unpredictable.
• when the specification or set of possible predicates becomes unmanageable, and
• when an experiment encounters an unknown error.

In probabilistic reasoning, there are two ways to solve problems involving uncertain knowledge:

• Bayes' rule
• Bayesian Statistics

Conclusion

The objective of this article was to acquaint the reader with the concept of probabilistic reasoning in artificial intelligence. Numerous problems in AI (reasoning, planning, learning, perception, and robotics) necessitate that the agent operates with incomplete or uncertain information. Using methods from probability theory and economics, AI researchers have developed a number of powerful tools for resolving these problems. Bayesian networks are a very versatile tool that can be used to solve a variety of problems, including reasoning (via the Bayesian inference algorithm), learning (via the expectation maximisation algorithm), planning (via decision networks), and perception (using dynamic Bayesian networks). Probabilistic algorithms can also be used to filter, predict, smooth, and find explanations for data streams, assisting perception systems in analysing time-dependent processes (e.g., hidden Markov models or Kalman filters). We hope this article helped you better understand the concept of uncertainty in AI systems.