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...

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 DBN is a type of Bayesian...

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 Utility assessments To help an agent...

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 problem-solving agents...

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: It decides...

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, it is a Markov...

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 inference rules to extract...

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 respective...

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 purposes of a dynamic routing...

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 quantifiersUniversal Instantiation (UI): In this, we can infer any sentence...

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