Markov Decision Processes in Artificial intelligence

A reinforcement learning task that satisfies the Markov property is called a Markov decision process, or MDP. If the state and action spaces are finite, then it is called a finite Markov decision process (finite MDP). Finite MDPs are particularly important to the theory of reinforcement learning. We treat them extensively throughout this book; they … Read more

The Agent–Environment Interface

The reinforcement learning problem is meant to be a straightforward framing of the problem of learning from interaction to achieve a goal. The learner and decision-maker is called the agent. The thing it interacts with, comprising everything outside the agent, is called the environment. These interact continually, the agent selecting actions and the environment responding … Read more

An n-Armed Bandit Problem in Artificial intelligence

Consider the following learning problem. You are faced repeatedly with a choice among n different options, or actions. After each choice you receive a numerical reward chosen from a stationary probability distribution that depends on the action you selected. Your objective is to maximize the expected total reward over some time period, for example, over … Read more

Elements of Reinforcement Learning in Artificial intelligence

Elements of Reinforcement Learning Beyond the agent and the environment, one can identify four main sub elements of a reinforcement learning system: a policy, a reward signal, a value function, and, optionally, a model of the environment. A policy defines the learning agent’s way of behaving at a given time. Roughly speaking, a policy is … Read more

K – means clustering in Artificial intelligence

What is clustering ? The organization of unlabeled data into similarity groups called clusters A cluster is a collection of data items which are “similar” between them, and “dissimilar” to data items in other clusters K-Means clustering K-means (MacQueen, 1967) is a partitional clustering algorithm Let the set of data points D be {x1 , … Read more

Support Vector Machine in Artificial intelligence

Support Vector Machine (SVM) was first heard in 1992, introduced by Boser, Guyon, and Vapnik in COLT-92. Support vector machines (SVMs) are a set of related supervised learning methods used for classification and regression They belong to a family of generalized linear classifiers. In another terms, Support Vector Machine (SVM) is a classification and regression … Read more

Naive Bayes Classification Algorithm

The Bayesian Classification represents a supervised learning method as well as a statistical method for classification. Assumes an underlying probabilistic model and it allows us to capture uncertainty about the model in a principled way by determining probabilities of the outcomes. It can solve diagnostic and predictive problems This Classification is named after Thomas Bayes … Read more