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 in making decisions andContinue Reading
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 networks. Dynamic Bayesian Networks were developedContinue Reading
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 model where the agent has someContinue Reading
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 is used to represent any full joint distribution. Bayesian networksContinue Reading
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 rely on the belief states (which represents allContinue Reading
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 what action it has to take. Learning: The actions taken byContinue Reading
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 algorithms. Let’s discuss both types one by one: Backward Chaining in Propositional LogicContinue Reading
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 data until the goal is notContinue Reading
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, we use Proof by Refutation technique to prove the given statement. TheContinue Reading
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 a ground term (a term without variables) for the variables. InContinue Reading
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