tailieunhanh - Data Mining and Knowledge Discovery Handbook, 2 Edition part 44

Data Mining and Knowledge Discovery Handbook, 2 Edition part 44. Knowledge Discovery demonstrates intelligent computing at its best, and is the most desirable and interesting end-product of Information Technology. To be able to discover and to extract knowledge from data is a task that many researchers and practitioners are endeavoring to accomplish. There is a lot of hidden knowledge waiting to be discovered – this is the challenge created by today’s abundance of data. Data Mining and Knowledge Discovery Handbook, 2nd Edition organizes the most current concepts, theories, standards, methodologies, trends, challenges and applications of data mining (DM) and knowledge discovery. | 410 Oded Maimón and Shahar Cohen In order to achieve good behavior the agent must explore its environment. Exploration means trying different sort of actions in various situations. While exploring some of the choices may be poor ones which may lead to severe costs. In such cases it is more appropriate to train the agent on a computer-simulated model of the environment. It is sometimes possible to simulate an environment without explicitly understanding it. RL methods have been used to solve a variety of problems in a number of domains. Pednault et al. 2002 solved targeted marketing problems. Tesauro 1994 1995 planned an artificial backgammon player with RL. Hong and Prabhu 2004 and Zhang and Dietterich 1996 used RL to solve manufacturing problems. Littman and Boyan 1993 have used RL for the solution of a networking routing problem. Using RL Crites and Barto 1996 trained an elevator dispatching controller. Reinforcement-Learning and Data-Mining This chapter presents an overview of some of the ideas and computation methods in RL. In this section the relation and relevance of RL to DM is discussed. Most DM learning methods are taken from ML. It is popular to distinguish between three categories of learning methods - Supervised Learning SL Unsupervised Learning and Reinforcement Learning. In SL the learner is programmed to extract a model from a set of observations where each observation consists of explaining variables and corresponding responses. In unsupervised learning there is a set of observations but no response and the learner is expected to extract a helpful representation of the domain from which the observations were drawn. RL requires the learner to extract a model of response based on experience observations that include states responses and the corresponding reinforcements. SL methods are central in DM and a correlation may be established between SL and RL in the following manner. Consider a learner that needs to extract a model of response for .