20. september 2006TDT55 - Case-based reasoning1 Retrieval, reuse, revision, and retention in case-based reasoning.

Slides:



Advertisements
Similar presentations
Case Based Reasoning Lecture 7: CBR Competence of Case-Bases.
Advertisements

Modelling with expert systems. Expert systems Modelling with expert systems Coaching modelling with expert systems Advantages and limitations of modelling.
CPSC 502, Lecture 15Slide 1 Introduction to Artificial Intelligence (AI) Computer Science cpsc502, Lecture 15 Nov, 1, 2011 Slide credit: C. Conati, S.
Optimizing search engines using clickthrough data
Expert System Shells - Examples
Does our organization take the actions possible for utilization of knowledge? Aban 1389 Self-assessment tool KTE-TUMS Group.
Lazy vs. Eager Learning Lazy vs. eager learning
Application of Inductive Learning and Case-Based Reasoning for Troubleshooting Industrial Machines - Michel Manago and Eric Auriol 컴퓨터공학과 신수용.
CS540 Software Design Lecture 1 1 Lecture 1: Introduction to Software Design Anita S. Malik Adapted from Budgen (2003) Chapters 1.
Explorations in Tag Suggestion and Query Expansion Jian Wang and Brian D. Davison Lehigh University, USA SSM 2008 (Workshop on Search in Social Media)
Artificial Intelligence MEI 2008/2009 Bruno Paulette.
Case-Based Reasoning, 1993, Ch11 Kolodner Adaptation method and Strategies Teacher : Dr. C.S. Ho Student : L.W. Pan No. : M Date : 1/7/2000.
5/12/1999 Li-we Pan1 指導老師 : 何正信教授 學生:潘立偉 學號: M 日期: 5/12/1999 Wolfgang Wilke, Barry Smyth, Pádraig Cunningham “Case-Based Reasoning Technology From.
Case Based Reasoning Melanie Hanson Engr 315. What is Case-Based Reasoning? Storing information from previous experiences Using previously gained knowledge.
Retrieval Evaluation. Brief Review Evaluation of implementations in computer science often is in terms of time and space complexity. With large document.
Orna Muller Tel-Aviv University, Israel ICER - Seattle, Oct 1-2, 2005.
Case-based Reasoning System (CBR)
Learning from Experience: Case Injected Genetic Algorithm Design of Combinational Logic Circuits Sushil J. Louis Genetic Algorithm Systems Lab(gaslab)
Models of Human Performance Dr. Chris Baber. 2 Objectives Introduce theory-based models for predicting human performance Introduce competence-based models.
NTUST Ailab Li-we Pan The MEDIATOR : Analysis of an Early Case-Based Problem Solver Jenet L. Kolodner & Robert L. Simpson Cognitive Science 13,
Case-Based Reasoning Ramon López de Mántaras Badia IIIA - CSIC
Factors in B2B Buying Behavior Process Stages; see diagram below Players: roles in “Buying Center” gatekeepers, users, influencers, deciders, purchasers.
Principles of High Quality Assessment
Software Architecture Quality. Outline Importance of assessing software architecture Better predict the quality of the system to be built How to improve.
Data Mining – Intro.
Nature of Science.
CBR in Medicine Jen Bayzick CSE435 – Intelligent Decision Support Systems.
Becerra-Fernandez, et al. -- Knowledge Management 1/e -- © 2004 Prentice Hall Chapter 16 Knowledge Application Systems: Systems that Utilize Knowledge.
Data Mining Chun-Hung Chou
Thinking Actively in a Social Context T A S C.
AugusBoth checks were cut the was cut on1/16 and the other one for was cut yesterday, both went out yesterday Marybeth Tahar Interaction.
Case-Based Recommendation Presented by Chul-Hwan Lee Barry Smyth.
Case-Based Solution Diversity Alexandra Coman Héctor Muñoz-Avila Dept. of Computer Science & Engineering Lehigh University Sources: cbrwiki.fdi.ucm.es/
Chapter 10 Marketing communication and personal selling
CBR for Design Upmanyu Misra CSE 495. Design Research Develop tools to aid human designers Automate design tasks Better understanding of design Increase.
Case Adaptation Sources: –Chapter 8 – –
Case Base Maintenance(CBM) Fabiana Prabhakar CSE 435 November 6, 2006.
University of Dublin Trinity College Localisation and Personalisation: Dynamic Retrieval & Adaptation of Multi-lingual Multimedia Content Prof Vincent.
Class Starter Please list the first five words or phrases that come to your mind when you hear the word : CHEMISTRY.
Copyright © 2007 The McGraw-Hill Companies, Inc.
Second Generation ES1 Second Generation Expert Systems Ahme Rafea CS Dept., AUC.
Data Mining Chapter 1 Introduction -- Basic Data Mining Tasks -- Related Concepts -- Data Mining Techniques.
CONSUMER BEHAVIOUR.
Case study of Several Case Based Reasoners Sandesh.
Data Mining – Intro. Course Overview Spatial Databases Temporal and Spatio-Temporal Databases Multimedia Databases Data Mining.
Unpacking the Elements of Scientific Reasoning Keisha Varma, Patricia Ross, Frances Lawrenz, Gill Roehrig, Douglas Huffman, Leah McGuire, Ying-Chih Chen,
Facilitating Document Annotation using Content and Querying Value.
CS 682, AI:Case-Based Reasoning, Prof. Cindy Marling1 Chapter 11: Adaptation Methods and Strategies Adaptation is the process of modifying a close, but.
Knowledge Learning by Using Case Based Reasoning (CBR)
Strategies for Distributed CBR Santi Ontañón IIIA-CSIC.
Of 33 lecture 1: introduction. of 33 the semantic web vision today’s web (1) web content – for human consumption (no structural information) people search.
1 Knowledge Acquisition and Learning by Experience – The Role of Case-Specific Knowledge Knowledge modeling and acquisition Learning by experience Framework.
B. Trousse, R. Kanawati - JTE : Advanced Services on the Web, Paris 7 may 1999 Broadway: a recommendation computation approach based on user behaviour.
Le parc japonais est beau et calme La fille japonaise est belle mais bavarde Ritsurin Park, Takamatsu.
Does GridGIS require more intelligence than GIS? Claire Jarvis Department of Geography GEOGRAPHY.
Getting to Know Webb’s. Webb’s Depth of Knowledge Level One (recall) requires simple recall of such information as fact, definition, term, or simple procedure.
Artificial Intelligence
CS Machine Learning Instance Based Learning (Adapted from various sources)
Jianping Fan Department of Computer Science University of North Carolina at Charlotte Charlotte, NC Relevance Feedback for Image Retrieval.
Copyright © 2011 Wolters Kluwer Health | Lippincott Williams & Wilkins Chapter 1 Research: An Overview.
Developing a diagnostic system through integration of fuzzy case-based reasoning and fuzzy ant colony system Expert Systems with Applications 28(2005)
Investigate Plan Design Create Evaluate (Test it to objective evaluation at each stage of the design cycle) state – describe - explain the problem some.
Planning for Instruction and Assessments. Matching Levels Ensure that your level of teaching matches your students’ levels of knowledge and thinking.
Architecture Components
Title: Suggestion Strategies for Constraint- Based Matchmaker Agents
Case-Based Reasoning.
Job Analysis CHAPTER FOUR Screen graphics created by:
Concept, Statement, & Theory Development
Case-Based Reasoning BY: Jessica Jones CSCI 446.
Presentation transcript:

20. september 2006TDT55 - Case-based reasoning1 Retrieval, reuse, revision, and retention in case-based reasoning

20. september 2006TDT55 - Case-based reasoning2 Introduction CBR Influenced by cognitive science Usage of remindings (”This reminds me of something I’ve seen before”) An important issue is how closely CBR systems should mirror how humans think

20. september 2006TDT55 - Case-based reasoning3 Introduction The steps of the CBR cycle Retrieval in CBR → Fetches previous cases that are assumed to be able to contribute to solve the target problem Reuse → Suggests a solution for the target-case from the solutions of the retrieved cases, possibly with an adaption process to fit the target-case better Revision → Evaluates the chosen solution with respect to degree of success Retention → The product of the most recent problem-solving episode is incorporated into the system’s knowledge

20. september 2006TDT55 - Case-based reasoning4 Retrieval in CBR Similarity assessment Surface features: the features given as a part of the case description Similarity-based retrieval is retrieval based on similarity of the surface features Ineffective to scan all cases in the base → Foot-print based retrieval → Validation

20. september 2006TDT55 - Case-based reasoning5 Retrieval in CBR Retrieval performance The solution quality is as important as the retrieval speed Problems that may influence the quality: → inadequate similarity measures → noise → missing values in cases → unknown values in the description of the target problem → the heterogenity problem – different attributes are used to describe different cases Work on how to solve this problem: → making the similarity measure be the subject of an adaptive learning process → guiding by domain knowledge

20. september 2006TDT55 - Case-based reasoning6 Retrieval in CBR Alternatives to similarity-retrieval: Adaption-guided retrieval → Retrieval of the cases which are easiest to adapt Diversity-conscious retrieval → Combines similarity and diversity measures to distinguish between cases of great similarity. Compromise-driven retrieval → A case is more acceptable than another if it is closer to the user’s query and it involves a subset of the compromises that the other case involves.

20. september 2006TDT55 - Case-based reasoning7 Retrieval in CBR Alternatives to similarity-retrieval: Order-based retrieval → Combine preferred values with preference information such as max and min values, and values that the user would prefer not to consider. Explanation-oriented retrieval → The goal is to explain how the system reached its conclusions. The easiest way of doing this is to use the explanation of the most similar case.

20. september 2006TDT55 - Case-based reasoning8 Reuse and revision in CBR Reuse can be as simple as returning the most similar case, but significant differences in target problem vs. retrieved case → need for adaption Adaption methods: Substitution adaption → exchanges parts of the retrieved solution Transformation adaption → changes the structure of the retrieved solution Generative adaption → derives the new solution by repeating the method used to derive the solution of the retrieved case

20. september 2006TDT55 - Case-based reasoning9 Retention in CBR The simplest form of retention is to just save the problem case and its solution as a new case The utility problem: As the case-base grows, every new case will not lead to a lot of new information (overlaps other cases), but will increase the searching time just as much Solution in general: → Delete harmful cases from the case base Solution in CBR: → Use a competence-model to decide each case’s contribution to the total problem solving competence

20. september 2006TDT55 - Case-based reasoning10 Retention in CBR Case-base maintenance Insert two new steps into the CBR cycle: Review – checks the quality of the system knowledge Restore – chooses and executes maintenance operations Categorization of maintenance policies: → how they gather data relevant to maintenance decisions → how they determine when to trigger maintenance operations → the types of maintenance operations available → how the maintenance operations are executed

20. september 2006TDT55 - Case-based reasoning11 Conclusions There is a significant amount of ongoing research on this subject A lot of the research is motivated by awareness of the limitations of the traditional approach