Anurag RANA

An expert system is a computer program that is designed to solve complex problems and to provide decision-making ability like a human expert. It performs this by extracting knowledge from its knowledge base using the reasoning and inference rules according to the user queries. The expert system is a part of AI, and the first ES was developed in the year 1970, which was the first successful approach of artificial intelligence. It solves the most complex issue as an expert by extracting the knowledge stored in its knowledge base. The system helps in decision making for compsex problems using  both facts and heuristics like a human expert . It is called so because it contains the expert knowledge of a specific domain and can solve any complex problem of that particular domain. These systems are designed for a specific domain, such as  medicine, science,  etc. The performance of an expert system is based on the expert's knowledge stored in its knowledge base. The more knowled...

Following are the most common subsets of AI: Machine Learning Deep Learning Natural Language processing Expert System Robotics Machine Vision Speech Recognition Note: Among all of the above, Machine learning plays a crucial role in AI. Machine learning and deep learning are the ways of achieving AI in real life. Machine Learning Machine learning is a part of AI which provides intelligence to machines with the ability to automatically learn with experiences without being explicitly programmed. It is primarily concerned with the design and development of algorithms that allow the system to learn from historical data. Machine Learning is based on the idea that machines can learn from past data, identify patterns, and make decisions using algorithms. Machine learning algorithms are designed in such a way that they can learn and improve their performance automatically. Machine learning helps in discovering patterns in data. Types of Machine Learning Machine learning can be subdivided intoth...

Bayes' theorem: Bayes' theorem is also known as  Bayes' rule, Bayes' law , or  Bayesian reasoning , which determines the probability of an event with uncertain knowledge. In probability theory, it relates the conditional probability and marginal probabilities of two random events. Bayes' theorem was named after the British mathematician  Thomas Bayes . The  Bayesian inference  is an application of Bayes' theorem, which is fundamental to Bayesian statistics. It is a way to calculate the value of P(B|A) with the knowledge of P(A|B). Bayes' theorem allows updating the probability prediction of an event by observing new information of the real world. Example : If cancer corresponds to one's age then by using Bayes' theorem, we can determine the probability of cancer more accurately with the help of age. Bayes' theorem can be derived using product rule and conditional probability of event A with known event B: As from product rule we can write: P(A ⋀ ...

Uncertainty: Till now, we have learned knowledge representation using first-order logic and propositional logic with certainty, which means we were sure about the predicates. With this knowledge representation, we might write A→B, which means if A is true then B is true, but consider a situation where we are not sure about whether A is true or not then we cannot express this statement, this situation is called uncertainty. So to represent uncertain knowledge, where we are not sure about the predicates, we need uncertain reasoning or probabilistic reasoning. Causes of uncertainty: Following are some leading causes of uncertainty to occur in the real world. Information occurred from unreliable sources. Experimental Errors Equipment fault Temperature variation Climate change. Probabilistic reasoning: Probabilistic reasoning is a way of knowledge representation where we apply the concept of probability to indicate the uncertainty in knowledge. In probabilistic reasoning, we combine probabi...