Decision Support and Artificial Intelligence

Topics: Artificial intelligence, Decision theory, Knowledge engineering Pages: 50 (12196 words) Published: August 13, 2013
DECISION SUPPORT AND ARTIFICIAL INTELLIGENCE

Brainpower for Your Business

CONTACT INFORMATION: Maeve Cummings is the primary author of this chapter. If you have any questions or comments, please direct them to her at cummings@pittstate.edu.

THE CHAPTER IN SHORT FORM…
This chapter introduces your students to the various types of decision support that are available. Computer-aided decision support has two major categories: systems that help you analyze and those that make the decision for you. The latter includes the various types of artificial intelligence systems.

The first section after the introduction discusses decision types and the process of decision making. It includes key terms such as structured decision, nonstructured decision, recurring decision, nonrecurring decision, intelligence, design, choice, and implementation.

The next three sections discuss three types of decision support that aid in the analysis of information: Decision support systems, collaboration systems, and geographic information systems. These sections include key terms such as model management, data management, collaboration system, and geographic information system.

The following five sections discuss various types of artificial intelligence. • Artificial intelligence (key term – robot)
• Expert systems (key terms – domain expertise, knowledge engineer, knowledge base, and rule-based system) • Neural networks (key terms – neural network, self-organizing neural network and back- propagation neural network) • Genetic algorithms (key terms – genetic algorithm, selection, crossover, and mutation) • Intelligent agents (key terms – shopping bot, user agent, monitoring-and-surveillance agent, data-mining agent, autonomy, adaptivity, and sociability)

STUDENT LEARNING OUTCOMES
1. Define decision support system, list its components, and identify the type of applications it’s suited to. 2. Define collaboration systems along with their features and uses. 3. Define geographic information systems and state how they differ from other decision support tools. 4. Define artificial intelligence and list the different types that are used in business. 5. Define expert systems and describe the type of problems to which they are applicable. 6. Define neural networks, their uses, and a major strength and weakness of these AI systems. 7. Define genetic algorithms and list the concepts on which they are based, and the types of problems they solve. 8. Define intelligent agents, list the four types, and identify the types of problems they solve.

LECTURE OUTLINE

INTRODUCTION (p. 134)

DECISIONS, DECISIONS, DECISIONS (p. 134)
1. How You Make a Decision
2. Types of Decisions You Face

DECISION SUPPORT SYSTEMS (p. 136)
1. Components of a Decision Support System

COLLABORATION SYSTEMS (p. 140)
1. Enterprisewide Collaboration
2. Supply-Chain Collaboration
3. Web-Based Collaboration

GEOGRAPHIC INFORMATION SYSTEMS (p. 143)

ARTIFICIAL INTELLIGENCE (p. 145)

EXPERT SYSTEMS (p. 147)
1. Components of an Expert System
2. What Expert Systems Can and Can’t Do

NEURAL NETWORKS (p. 152)
1. Types of Neural Networks
2. Inside a Neural Network

GENETIC ALGORITHMS (p. 156)

INTELLIGENT AGENTS (p. 158)
1. Buyer Agents
2. User Agents
3. Monitoring-and-Surveillance Agents
4. Data-Mining Agents
5. Components of an Intelligent Agent

END OF CHAPTER (p. 163)
1. Summary: Student Learning Outcomes Revisited
2. Closing Case Study One
3. Closing Case Study Two
4. Key Terms and Concepts
5. Short-Answer Questions
6. Assignments and Exercises
7. Discussion Questions
8. Real HOT Electronic Commerce

KEY TERMS AND CONCEPTS

|KEY TERMS AND CONCEPTS |TEXT PAGE | |Adaptive filtering |160 | |Adaptivity...
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