Author: misamaliraza94
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What is an Expert System?
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…
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Subsets of Artificial Intelligence
Till now, we have learned about what is AI, and now we will learn in this topic about various subsets of AI. 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…
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Bayesian Belief Network in AI
Bayesian belief network is key computer technology for dealing with probabilistic events and to solve a problem which has uncertainty. We can define a Bayesian network as: “A Bayesian network is a probabilistic graphical model which represents a set of variables and their conditional dependencies using a directed acyclic graph.” It is also called a Bayes…
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Bayes theorem in Artificial intelligence
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…
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Probabilistic reasoning in AI
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…
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Inductive vs Deductive reasoning
Reasoning in artificial intelligence has two important forms, Inductive reasoning, and Deductive reasoning. Both reasoning forms have premises and conclusions, but both reasoning are contradictory to each other. Following is a list for comparison between inductive and deductive reasoning: Deductive reasoning uses available facts, information, or knowledge to deduce a valid conclusion, whereas inductive reasoning…
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Reasoning in Artificial intelligence
In previous topics, we have learned various ways of knowledge representation in artificial intelligence. Now we will learn the various ways to reason on this knowledge using different logical schemes. Reasoning: The reasoning is the mental process of deriving logical conclusion and making predictions from available knowledge, facts, and beliefs. Or we can say, “Reasoning…
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Difference b/w backward chaining and forward chaining
Following is the difference between the forward chaining and backward chaining: Forward chaining as the name suggests, start from the known facts and move forward by applying inference rules to extract more data, and it continues until it reaches to the goal, whereas backward chaining starts from the goal, move backward by using inference rules…
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Forward Chaining and backward chaining in AI
In artificial intelligence, forward and backward chaining is one of the important topics, but before understanding forward and backward chaining lets first understand that from where these two terms came. Inference engine: The inference engine is the component of the intelligent system in artificial intelligence, which applies logical rules to the knowledge base to infer…
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Resolution in FOL
Resolution Resolution is a theorem proving technique that proceeds by building refutation proofs, i.e., proofs by contradictions. It was invented by a Mathematician John Alan Robinson in the year 1965. Resolution is used, if there are various statements are given, and we need to prove a conclusion of those statements. Unification is a key concept…