2 Definition:Artificial Intelligence is the study of how to make computers do things at which, at the moment, people are better.
3 The Turing TestAccording to this test, a computer could be considered to bethinking only when a human interviewer, conversing with bothan unseen human being and an unseen computer, could notdetermine which is which.
4 More on AI Artificial Real Items Airplanes Birds Silk Flowers Flowers Artificial Snow Snow
5 AI Major Areas - Expert Systems - Natural Language Processor - Speech Recognition- Robotics- Computer Vision- Intelligent Computer-Aided Instruction- Data Mining- Genetic Algorithms
7 AI Advantages 1. AI is permanent 2. AI offers ease of duplication 3. AI can be less expensive than natural intelligenc4. AI is consistent5. AI can be documented
8 Natural Intelligence Advantages 1. Natural intelligence is creative.2. Natural intelligence uses sensory experience directly,whereas most AI systems must work with symbolicinput.3. Human reasoning is able to make use at all times of avery wide context experience and bring that to bear onindividual problems, where as AI systems typicallygain their power by having a very narrow domain.
9 Characteristics of a Human Experts - Recognize and formulate the problem- Solve the problem fairly quickly- Explain the solution- Learn from experience- Restructure knowledge- Break rules- Determine relevance- Degrade gracefully
10 What Do Experts Know?It is estimated that a world-class expert, such as a chessgrandmaster, has 50,000 to 100,000 chunks of heuristicinformation about his/her specialty. On the average, ittakes at least 10 years to acquire 50,000 rules.
12 Expert Systems Components 1. Knowledge Acquisition2. Knowledge Base3. Inference Engine4. User Interface5. Explanation Facility6. Knowledge Refining System
13 Different Categories of Expert Systems Category Problem AddressedInterpretation Inferring situation description from observationsPrediction Inferring likely consequences of given situationsDiagnosis Inferring systems malfunctions from observationsDesign Configuring objects under constraintsPlanning Developing plans to achieve goalsMonitoring Comparing observations to plan vulnerabilitiesDebugging Prescribing remedies for malfunctionsRepair Executing a plan to administer a prescribed remedyControl Interpreting, predicting, repairing, and monitoringsystem behavior
14 What Tasks Are ES Right For? - Payroll, Inventory- Simple Tax Returns- Database Management- Mortgage Computation- Regression Analysis- Facts are Known- Expertise is CheapToo Easy - Use Conventional Software
15 What Tasks Are ES Right For? - Diagnosing and Troubleshooting- Analyzing Diverse Data- Production Scheduling- Equipment Layout- Advise on Tax Shelter- Facts are known but not precisely- Expertise is expensive but availableJust Right
16 What Tasks Are ES Right For? - Designing New Tools- Stock Market Forecast- Discovering New Principles- Common Sense Problems- Requires Innovation or Discovery- Expertise is not availableToo Hard - Requires Human Intelligence
17 Problems and Limitations of Expert Systems - Knowledge is not always readily available.- Expertise is hard to extract from humans.- ES work well only in a narrow domain.- The approach of each expert to problem underconsideration may be different, yet correct.
18 Necessary Requirements for ES Development - The task does not require common sense.- The task requires only cognitive, not physical, skills.- There is an expert who is willing to cooperate.- The experts involved can articulate their methodsof problem solving.- The task is not too difficult.- The task is well understood, and is defined clearly.- The task definition is fairly stable.- Problem must be well bounded and narrow.
19 Justification for ES Development - The solution to the problem has a high payoff.- The ES can capture scarce human expertise so itwill not be lost.- The expertise is needed in many locations.- The expertise is needed in hostile or hazardousenvironment.- The system can be used for training.- The ES is more dependable and consistent thanhuman expert.
20 Feasibility Study A. Financial Feasibility Cost of system development Cost of maintenancePayback periodCash flow analysisB. Technical Feasibility Interface requirementsNetwork issuesAvailability of data and knowledgeSecurity of confidential knowledgeKnowledge representation schemeHardware/software availabilityHardware/software compatibility
21 More on Feasibility Study C. Operational Feasibility Availability of human resourcesPriority compare to other projectsImplementation issuesManagement and user supportAvailability of expertsAvailability of knowledgeengineers