Business Intelligence and Decision Support Systems (9th Ed., Prentice Hall) Chapter 13: Advanced Intelligent Systems Learning Objectives  Understand the basic concepts and definitions of machine-learning       13-2 Learn the commonalities and differences between machine learning and human learning Know popular machine-learning methods Know the concepts and definitions of casebased reasoning systems (CBR) Be aware of the MSS applications of CBR Know the concepts behind and applications of genetic algorithms Understand fuzzy logic and its application in designing intelligent systems Copyright © 2011 Pearson Education, Inc Publishing as Prentice Hall Learning Objectives     13-3 Understand the concepts behind support vector machines and their applications in developing advanced intelligent systems Know the commonalities and differences between artificial neural networks and support vector machines Understand the concepts behind intelligent software agents and their use, capabilities, and limitations in developing advanced intelligent systems Explore integrated intelligent support systems Copyright © 2011 Pearson Education, Inc Publishing as Prentice Hall Opening Vignette: “Machine Learning Helps Develop an Automated Reading Tutoring Tool” 13-4  Background on literacy  Problem description  Proposed solution  Results  Answer and discuss the case questions Copyright © 2011 Pearson Education, Inc Publishing as Prentice Hall Machine Learning Concepts and Definitions  Machine learning (ML) is a family of artificial intelligence technologies that is primarily concerned with the design and development of algorithms that allow computers to “learn” from historical data    13-5 ML is the process by which a computer learns from experience It differs from knowledge acquisition in ES: instead of relying on experts (and their willingness) ML relies on historical facts ML helps in discovering patterns in data Copyright © 2011 Pearson Education, Inc Publishing as Prentice Hall Machine Learning Concepts and Definitions   Learning is the process of selfimprovement, which is an critical feature of intelligent behavior Human learning is a combination of many complicated cognitive processes, including:     13-6 Induction Deduction Analogy Other special procedures related to observing and/or analyzing examples Copyright © 2011 Pearson Education, Inc Publishing as Prentice Hall Machine Learning Concepts and Definitions  Machine Learning versus Human Learning      13-7 Some ML behavior can challenge the performance of human experts (e.g., playing chess) Although ML sometimes matches human learning capabilities, it is not able to learn as well as humans or in the same way that humans There is no claim that machine learning can be applied in a truly creative way ML systems are not anchored in any formal theories (why they succeed or fail is not clear) ML success is often attributed to manipulation of symbols (rather than mere numeric information) Copyright © 2011 Pearson Education, Inc Publishing as Prentice Hall Machine Learning Methods 13-8 Copyright © 2011 Pearson Education, Inc Publishing as Prentice Hall Case-Based Reasoning (CBR)  Case-based reasoning (CBR) A methodology in which knowledge and/or inferences are derived directly from historical cases/examples  Analogical reasoning (= CBR) Determining the outcome of a problem with the use of analogies A procedure for drawing conclusions about a problem by using past experience directly (no intermediate model?)  Inductive learning A machine learning approach in which rules (or models) are inferred from the historic data 13-9 Copyright © 2011 Pearson Education, Inc Publishing as Prentice Hall CBR vs Rule-Based Reasoning 13-10 Copyright © 2011 Pearson Education, Inc Publishing as Prentice Hall Support Vector Machines (SVM)  A hyperplane is a geometric concept used to describe the separation surface between different classes of things   A kernel function in SVM uses the kernel trick (a method for using a linear classifier algorithm to solve a nonlinear problem)  13-35 In SVM, two parallel hyperplanes are constructed on each side of the separation space with the aim of maximizing the distance between them The most commonly used kernel function is the radial basis function (RBF) Copyright © 2011 Pearson Education, Inc Publishing as Prentice Hall Support Vector Machines (SVM)  Many linear classifiers (hyperplanes) may separate the data Copyright © 2011 Pearson Education, Inc Publishing as Prentice Hall 13-36 How Does a SVM Works?   Following a machine-learning process, a SVM learns from the historic cases The Process of Building SVM Preprocess the data  Scrub and transform the data Develop the model    Select the kernel type (RBF is often a natural choice) Determine the kernel parameters for the selected kernel type If the results are satisfactory, finalize the model, otherwise change the kernel type and/or kernel parameters to achieve the desired accuracy level Extract and deploy the model 13-37 Copyright © 2011 Pearson Education, Inc Publishing as Prentice Hall The Process of Building a SVM 13-38 Copyright © 2011 Pearson Education, Inc Publishing as Prentice Hall SVM Applications      13-39 SVM are the most widely used kernel-learning algorithms for wide range of classification and regression problems SVM represent the state-of-the-art by virtue of their excellent generalization performance, superior prediction power, ease of use, and rigorous theoretical foundation Most comparative studies show its superiority in both regression and classification type prediction problems See recent literature and examples in the book SVM versus ANN? Copyright © 2011 Pearson Education, Inc Publishing as Prentice Hall Intelligent Software Agents    Intelligent Agent (IA): is an autonomous computer program that observes and acts upon an environment and directs its activity toward achieving specific goals Relatively new technology Other names include       13-40 Software agents Wizards Knowbots Intelligent software robots (Softbots) Bots Agent - Someone employed to act on one’s behalf Copyright © 2011 Pearson Education, Inc Publishing as Prentice Hall Definitions of Intelligent Agents 13-41  Intelligent agents are software entities that carry out some set of operations on behalf of a user or another program, with some degree of independence or autonomy and in so doing, employ some knowledge or representation of the user’s goals or desires.” (“The IBM Agent”)  Autonomous agents are computational systems that inhabit some complex dynamic environment, sense and act autonomously in this environment and by doing so realize a set of goals or tasks for which they are designed (Maes, 1995, p 108) Copyright © 2011 Pearson Education, Inc Publishing as Prentice Hall Characteristics of Intelligent Agents  Autonomy (empowerment)          13-42 Agent takes initiative, exercises control over its actions They are Goal-oriented, Collaborative, Flexible, Selfstarting Operates in the background Communication (interactivity) Automates repetitive tasks Proactive (persistence) Temporal continuity Personality Mobile agents Intelligence and learning Copyright © 2011 Pearson Education, Inc Publishing as Prentice Hall A Taxonomy for Autonomous Agents 13-43 Copyright © 2011 Pearson Education, Inc Publishing as Prentice Hall Classification for Intelligent Agents by Characteristics  Agents can be classified in terms of these three important characteristics dimensions Agency  Degree of autonomy and authority vested in the agent  More advanced agents can interact with other agents/entities Intelligence  Degree of reasoning and learned behavior  Tradeoff between size of an agent and its learning modules Mobility  Degree to which agents travel through the network  13-44 Mobility requires approval for residence at a foreign locations Copyright © 2011 Pearson Education, Inc Publishing as Prentice Hall Intelligent Agents’ Scope in Three Dimensions 13-45 Copyright © 2011 Pearson Education, Inc Publishing as Prentice Hall Internet-Based Software Agents   Software Robots or Softbots Major Categories       E-mail agents (mailbots) Web browsing assisting agents Frequently asked questions (FAQ) agents Intelligent search (or Indexing) agents Internet softbot for finding information Network Management and Monitoring   13-46 Security agents (virus detectors) Electronic Commerce Agents (negotiators) Copyright © 2011 Pearson Education, Inc Publishing as Prentice Hall Leading Intelligent Agents Programs          13-47 IBM [research.ibm.com/iagents] Carnegie Mellon [cs.cmu.edu/~softagents] MIT [agents.media.mit.edu] University of Maryland, Baltimore County [agents.umbc.edu] University of Massachusetts [dis.cs.umass.edu] University of Liverpool [csc.liv.ac.uk/research/agents] University of Melbourne (agentlab.unimelb.edu.au) Multi-agent Systems [multiagent.com] Commercial Agents/Bots [botspot.com] Copyright © 2011 Pearson Education, Inc Publishing as Prentice Hall End of the Chapter  13-48 Questions / comments… Copyright © 2011 Pearson Education, Inc Publishing as Prentice Hall All rights reserved No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior written permission of the publisher Printed in the United States of America Copyright © 2011 Pearson Education, Inc   Publishing as Prentice Hall 13-49 Copyright © 2011 Pearson Education, Inc Publishing as Prentice Hall ... intelligent systems Know the commonalities and differences between artificial neural networks and support vector machines Understand the concepts behind intelligent software agents and their use, capabilities,... concepts and definitions of casebased reasoning systems (CBR) Be aware of the MSS applications of CBR Know the concepts behind and applications of genetic algorithms Understand fuzzy logic and its... understand - Frees the imagination Provides flexibility More forgiving Shortens system development time Increases the system's maintainability Uses less expensive hardware Handles control or decision- making