[PDF] Rapid Retrieval Algorithms for Case-Based Reasoning*

Introduction to Machine Learning Case-Based Reasoning

Instance-based learning also includes case-based reasoning methods that use more complex, symbolic representations for instances An overview of the topic can be found in [8] A survey of methods for locally weighted regression is given in [3] Chapter 2 of this syllabus provides a detailed discussion on case-based reasoning

PRINCIPLES OF CASE-BASED REASONING

Selecting the best similar case(s), it is usually performed in most Case-based reasoning agents by means of some evaluation heuristic functions or distances, possibly domain dependent They are usually named as nearest neighbour (NN or k-NN) algorithms [Watson, 1996]

An Introduction to Case-Based Reasoning*

Case-based reasoning means using old experiences to understand and solve new problems In case-based reasoning, a reasoner remembers a previous situation similar to the current one and uses that to solve the new problem Case- based reasoning can mean adapting old solutions to meet new demands; using old

Rapid Retrieval Algorithms for Case-Based Reasoning*

One of the major issues confronting case-based reasoning (CBR) is rapid retrieval of similar cases from a large case base This paper describes three algorithms which address this problem The first algorithm works with quantitative cases using a graphical paradigm where the hyperspace containing the cases is divided into smaller and smaller

Case-Based Reasoning for Evolutionary MEMS Design

bines a case-based reasoning (CBR) algorithm and a MEMS case library with paramet-ric optimization and a multi-objective genetic algorithm (MOGA) to synthesize new MEMS design topologies that meet or improve upon a designer’s specifications CBR is an artificial intelligence methodology that uses past design solutions and adapts them to

Using Case-based Reasoning in an Algorithm Portfolio for

Our approach uses case-based reasoning to inform the selec-tion process We build a case base of problem solving experience by solving a variety of typical problem instances with each solver in our algorithm portfolio We employ case retrieval methods in a number of increasingly sophisticated ways, giving better performance in each case

Problem Solving by Case-Based Reasoning

Dr Thomas Gabel --- Problem Solving by Case-Based Reasoning ---11 05 2010 Advantages of CBR (II) • High Quality of Solutions for Poorly Understood Domains – case-based systems can be made to retain only ``good‘‘ experience in memory – if only little adaptation is necessary for reuse, this will not impair the solution‘s quality too much

Improving Reinforcement Learning by using Case Based Heuristics

function This paper investigates the combination of Case Based Reasoning (CBR) and Heuristically Accelerated Reinforcement Learning (HARL) techniques, with the goal of speeding up RL algorithms by using previous domain knowledge, stored as a case base To do so, we propose a new algorithm, the Case Based Heuristically Accelerated

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Rapid Retrieval Algorithms for Case-Based Reasoning*

Richar

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Stottle

r an d Andre a L Henk e Jame s A Kin

g Stottler Associates NCR Corporation 2205 Hastings Drive, Suite 38 1700 South Patterson Boulevard Belmont, CA 94002 Dayton, OH 45479

Abstrac

t On e o f th e majo r issue s confrontin g case-base d reasonin g (CBR i s rapi d retrieva l o f simila r case s fro m a larg e cas e base Thi s pape r describe s thre e algorithm s whic h addres s thi s problem Th e firs t algorith m work s wit h quantitativ e case s usin g a graphica l paradig m wher e th e hyperspac e containin g th e case s i s divide d int o smalle r an d smalle r hypercubes Th e retrieva l tim e fo r thi s algorith m i s

0(Log(N))

wher e N i s th e numbe r o f cases Th e secon d algorith m work s o n qualitativ e dat a b y efficientl y retrievin g case s base d o n ever y necessar y combinatio n o f cas e attributes It s retrieva l tim e varie s onl y wit h respec t t o th e numbe r o f attributes Th e thir d algorith m i s a combinatio n o f th e previou s tw o an d allow s retrieva l o f case s consistin g o f bot h quantitativ e an d qualitativ e information Th e algorithm s describe d i n thi s pape r ar e th e firs t practica l algorithm s designe d fo r cas e base d retrieva l o n ver y larg e number s o f cases Th e algorithm s easil y handl e cas e base s containin g million s o f case s o r more 1

Introductio

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Statemen

t Rapi d developmen t o f exper t system s i s hindere d b y th e well-know n knowledg e acquisitio n bottleneck

Case-Base

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g (CBR overcome s thi s proble m b y representin g knowledg e a s cases wher e eac h cas e consist s o f a proble m an d it s solution A proble m ca n b e solve d b y rememberin g th e solutio n t o a simila r proble m an d adjustin g i t fo r th e curren t contexquotesdbs_dbs8.pdfusesText_14