## Probabilistic Reasoning

Probabilistic Reasoning Probabilistic Reasoning is the study of building network models which can reason under uncertainty, following the principles of probability theory. Bayesian Networks Bayesian network is a data structure which is used to represent the dependencies among variables. It …

## Dynamic Bayesian Networks

DBN is a temporary network model that is used to relate variables to each other for adjacent time steps. Each part of a Dynamic Bayesian Network can have any number of Xivariables for states representation, and evidence variables Et. A …

## Utility Functions in Artificial Intelligence

The agents use the utility theory for making decisions. It is the mapping from lotteries to the real numbers. An agent is supposed to have various preferences and can choose the one which best fits his necessity. Utility scales and …

## Quantifying Uncertainty

The concept of quantifying uncertainty relies on how an agent can keep away uncertainty with a degree of belief. The term uncertainty refers to that situation or information which is either unknown or imperfect. Earlier, we have seen that the …

## Classical Planning

Classical Planning is the planning where an agent takes advantage of the problem structure to construct complex plans of an action. The agent performs three tasks in classical planning: Planning: The agent plans after knowing what is the problem. Acting: …

## Hidden Markov Models

Hidden Markov Model is a partially observable model, where the agent partially observes the states. This model is based on the statistical Markov model, where a system being modeled follows the Markov process with some hidden states. In simple words, …

## Forward Chaining

Forward Chaining is the process which works on the basis of available data to make certain decisions. Forward chaining is the process of chaining data in the forward direction. In forward chaining, we start with the available data and use …

## Backward Chaining

Backward Chaining is a backward approach which works in the backward direction. It begins its journey from the back of the goal. Like, forward chaining, we have backward chaining for Propositional logic as well as Predicate logic followed by their …

## Dynamic Routing

Dynamic Routing Dynamic routing is used to update the routing table and find networks on the routers. It is easier than static routing and default routing, but it is more expensive in terms of bandwidth and CPU utilization. The main …

## Inference in First-order Logic

Inference in First-order Logic While defining inference, we mean to define effective procedures for answering questions in FOPL. FOPL offers the following inference rules: Inference rules for quantifiers Universal Instantiation (UI): In this, we can infer any sentence by substituting …

## Resolution Method in AI

Resolution Method in AI Resolution method is an inference rule which is used in both Propositional as well as First-order Predicate Logic in different ways. This method is basically used for proving the satisfiability of a sentence. In resolution method, …

## Theory of First-order Logic

Theory of First-order Logic First-order logic is also called Predicate logic and First-order predicate calculus (FOPL). It is a formal representation of logic in the form of quantifiers. In predicate logic, the input is taken as an entity, and the …

## Inference Rules in Proposition Logic

Inference rules are those rules which are used to describe certain conclusions. The inferred conclusions lead to the desired goal state. In propositional logic, there are various inference rules which can be applied to prove the given statements and conclude …

## Propositional Logic

It is a branch of logic which is also known as statement logic, sentential logic, zeroth-order logic, and many more. It works with the propositions and its logical connectivities. It deals with the propositions or statements whose values are true, …

## The Wumpus World

The Wumpus world is a game playing which provides an environment to the knowledge-based agent to showcase its stored knowledge. It was developed by Gregory Yob in 1973. About the game:  It is a single-player game. It is a cave …

## Knowledge Based Agents in AI

Knowledge is the basic element for a human brain to know and understand the things logically. When a person becomes knowledgeable about something, he is able to do that thing in a better way. In AI, the agents which copy …

## Knowledge Representation in AI

In this section, we will understand how to represent the knowledge in the form which could be understood by the knowledge-based agents. The knowledge that is stored in the system is related to the world and its environment. It is …

## Cryptarithmetic Problem

Cryptarithmetic Problem Cryptarithmetic Problem is a type of constraint satisfaction problem where the game is about digits and its unique replacement either with alphabets or other symbols. In cryptarithmetic problem, the digits  (0-9) get substituted by some possible alphabets or …

## Constraint Satisfaction Problems in Artificial Intelligence

Constraint Satisfaction Problems in Artificial Intelligence We have seen so many techniques like Local search, Adversarial search to solve different problems. The objective of every problem-solving technique is one, i.e., to find a solution to reach the goal. Although, in …

## Alpha-beta Pruning | Artificial Intelligence

Alpha-beta pruning is an advance version of MINIMAX algorithm. The drawback of minimax strategy is that it explores each node in the tree deeply to provide the best path among all the paths. This increases its time complexity. But as …