Intelligent Systems for Decision Support

Intelligent Systems for Decision Support

Intelligent Systems for Decision Support Intelligent Systems for Decision Support Intelligent techniques for enhancing decision making Many based on artificial intelligence (AI) Computer-based systems (hardware and software) that attempt to emulate human behavior and thought patterns Include: Expert systems Case-based reasoning

Neural networks Genetic algorithms Intelligent agents ESS (Executive Support) Visualization Software AI and Expert Systems VIDEO Intelligent Systems for Decision Support Expert systems Model human knowledge as a set of rules that are collectively called the knowledge base 200 to 10,000 rules, depending on complexity The systems inference engine searches through the rules and fires those rules that are triggered by facts gathered

and entered by the user. Useful for dealing with problems of classification in which there are relatively few alternative outcomes and in which these possible outcomes are all known in advance Should be used only in highly structured decision making situations Intelligent Systems for Decision Support Dog Breed Advisor (If/then) Rules in an Expert System An expert system contains a set of rules to be followed when used. The rules are

interconnected; the number of outcomes is known in advance and is limited; there are multiple paths to the same outcome; and the system can consider multiple rules at a single time. The rules illustrated are for a simple credit-granting expert system. Decision Trees Decision tree Hierarchical arrangement of criteria that predict a classification or value Basic idea of a decision tree Select attributes most useful for classifying

something on some criteria that create disparate groups More different or pure the groups, the better the classification Copyright 2011 Pearson Education, Inc. Publishing as Prentice Hall 9-5 Problems of Expert Systems 1. Difficult and expensive to develop. They require many labor hours from both experts in the domain under study and designers of expert systems. High opportunity cost of tying up domain experts. 2. Difficult to maintain. Nature of rule-based systems creates

unexpected consequences when adding a new rule in middle of hundreds of others. A small change can cause very different outcomes. 3. No expert system has the same diagnostic ability as knowledgeable, skilled, and experienced doctors. Rules/actions change frequently. Copyright 2011 Pearson Education, Inc. Publishing as Prentice Hall 9-7 Intelligent Systems for Decision Support Case-based reasoning Knowledge and past experiences of human specialists are represented as cases and stored in a database for

later retrieval. System searches for stored cases with problem characteristics similar to new one, finds closest fit, and applies solutions of old case to new case. Successful and unsuccessful applications are tagged and linked in database. Used in medical diagnostic systems, customer support. Intelligent Systems for Decision Support How Case-Based Reasoning Works Case-based reasoning represents knowledge as a database of past cases

and their solutions. The system uses a six-step process to generate solutions to new problems encountered by the user. Examples: Appliance Call Center automation at General Electric SMART: Support management automated reasoning technology for Compaq customer service Neural networks

Use hardware and software that parallel the processing patterns of a biological brain. One of the data mining tools Learn patterns from large quantities of data by searching for relationships, building models, and correcting over and over again the models own mistakes.

Humans train the network by feeding it data for which the inputs produce a known set of outputs or conclusions. Machine learning Useful for solving complex, poorly understood problems for which large amounts of data have been collected. Used to predict values and make classifications such as good prospect or poor prospect customers Complicated set of nonlinear equations To some extent, data-mining techniques have replaced neural networks in situations

requiring pattern recognition. credit card companies sift through mountains of transaction data to identify which charging patterns are unlikely for each and every charge card customer. Intelligent Systems for Decision Support How a Neural Network Works A neural network uses rules it learns from patterns in data to construct a hidden layer of logic. The hidden layer then processes inputs, classifying them based on the experience of the model. In this example, the neural network has been trained to distinguish between valid and fraudulent credit card purchases.

Intelligent Systems for Decision Support Genetic algorithms (Solver is an example) Find the optimal solution for a specific problem by examining very large number of alternative solutions for that problem. Based on techniques inspired by evolutionary biology: inheritance, mutation, selection, and so on. Work by representing a solution as a string of 0s and 1s, then searching randomly generated strings of binary digits to identify best possible solution. Used to solve complex problems that are very dynamic and complex, involving hundreds or thousands of variables or formulas.

Intelligent Systems for Decision Support Intelligent agents Video Programs that work in the background without direct human intervention to carry out specific, repetitive, and predictable tasks for user, business process, or software application Shopping bots Procter & Gamble (P&G) programmed group of semiautonomous agents to emulate behavior of supplychain components, such as trucks, production facilities, distributors, and retail stores and created simulations to determine how to make supply chain more efficient Intelligent Agents in P&Gs Supply Chain Network

to improve coordination among supply-chain members Intelligent agents are helping Procter & Gamble shorten the replenishment cycles for products, such as a box of Tide. Executive Support and KPIs Executive support systems (ESS) help managers and executives focus on performance information that maximizes resources within the organization to improve the profitability and success of the company. There are two parts to developing an ESS: understand exactly what the most important performance information(KPIs Key Performance Indicators) is and develop systems capable of delivering that information to the right people in an easy-to-use format.

What are some key performance indicators (KPI) for Furman? enrollment numbers and the number of students in each academic discipline are obvious Less obvious KPI might be drop-out rates or the number of students switching majors. Before you can develop an ESS, you need to understand exactly what data you should track. The Balanced Score Card A balanced scorecard focuses on measurable outcomes on four dimensions of a businesss performance: financial, business process, customer, and learning and growth.

Each dimension uses key performance indicators (KPIs) to understand how well an organization is performing on any of the dimensions at any time. The framework of a balanced scorecard requires managers to focus on more than just financial performance. They must focus on things they are able to influence at the present time like customer satisfaction, business process efficiency, or employee training. The KPIs are developed by senior executives and are automatically provided to users through an executive support systems Visualization Software

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