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Artificial Intelligence

by: Miss Terry Reichel

Artificial Intelligence CSSE 413

Miss Terry Reichel
GPA 3.52


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This 15 page Class Notes was uploaded by Miss Terry Reichel on Monday October 19, 2015. The Class Notes belongs to CSSE 413 at Rose-Hulman Institute of Technology taught by Staff in Fall. Since its upload, it has received 47 views. For similar materials see /class/225102/csse-413-rose-hulman-institute-of-technology in Computer Science and Engineering at Rose-Hulman Institute of Technology.

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Date Created: 10/19/15
CaseBased Reasoning is 3 A methodology to model human reasoning and thinking i A methodology for building intelligent computer systems CBR in a nutshell store previous experience cases in memory to solve new problems retrieve similar experience about similar situations from the memory reuse the experience in the context of the new situation complete or partial reuse or adapt according to differences store new experience in memory learning A Simple Example Overview Technical Diagnosis of Car Faults Symptoms are observed eg engine doesn t start and values are measured eg battery voltage 63V Goal Find the cause forthe failure eg battery empty and a repair strategy eg charge battery CaseBased Diagnosis A case describes a diagnostic situation and contains description of the symptoms 39 description of the failure and the cause description of a repair strategy Store a collection of cases in a case base Find case similar to current problem and reuse repair strategy Problem Eyjm mm s 39 ProblemFrontlighldhesmtmrk 6 ocarvwiieomltepI 39 22199375 7 1 39 39 QP Problem Symptoms V F m nt light dpgsnflzwork V RETAIN G mem39 1 Knomedge Summary 4 CBR is ateohnique forsolvmgproblemsbasedenexperienee CBR problem solving involves four phases Retrieve Reuse Revise Retain CBR systems store knowledge in four containers Vocabulary Case Base Similarity Assessment Solution Adaptation Retain Learning from Problem Solving 4 What can be learned New experience new case Improved similarity assessment importance offeatUres Organizationlindexing ofthe case base to improve ef ciency Knowledge for solution adaptation Forgetting cases eg for e iciencgt or because outof date Methods Storing cases in the case base Deleting cases from the case base Explan ationbased learning induction eg of decision trees Neural net ster learning Less Effort Required for Maintenance What is the impact of changes of the environment Rule bases or models are difficult to maintain Many dependencies between rules Rules of KBS often dif cult to understand for non Al experts Effects of changes of the rule base are hard to predict Maintenance by the domain expert impossible Case bases are easier to maintain Cases are independent from each other Domain experts and novices understand cases quite easy Maintenance ofthe CBR system partially by addingfdeleting cases However changes in the vocabulary container require little more effort


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