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AI in Knowledge Management

AI in Knowledge Management. Professor Robin Burke CSC 594. Outline. Introduction to the class Overview Knowledge management AI Case-based reasoning. Objectives. Content Explore AI applications in knowledge management specifically case-based reasoning Skills

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AI in Knowledge Management

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  1. AI in Knowledge Management Professor Robin Burke CSC 594

  2. Outline • Introduction to the class • Overview • Knowledge management • AI • Case-based reasoning

  3. Objectives • Content • Explore AI applications in knowledge management • specifically case-based reasoning • Skills • Reading research literature • Building an informal knowledge base

  4. Course design • Seminar format • student presentations • in-class exercises • Attendance VERY IMPORTANT! • Reading VERY IMPORTANT!

  5. Reading • Two main readings each week • case study • research article • Admission ticket • 1-2 page reaction paper • what did you find interesting? • a discussion question

  6. Assessment • Presentations – 40% • two presentations / student • 1 case study • 1 research paper • Participation – 50% • course librarian • discussion • Final Project – 10% • more later

  7. Typical class session • Case study • 30 min. presentation • 15 min. discussion • Research paper • 30 min. presentation • 15 min. questions • Librarian’s reports • Group exercise

  8. Artificial intelligence • The subfield of computer science concerned with the concepts and methods of symbolic inference by computer and symbolic knowledge representation for use in making inferences. • AI can be seen as an attempt to model aspects of human thought on computers. It is also sometimes defined as trying to solve by computer any problem that a human can solve faster. -- FOLDOC

  9. Knowledge management • Knowledge management involves the acquisition, storage, retrieval, application, generation and review of the knowledge assets of an organization in a controlled way. -- I. Watson

  10. Example: oil industry • old model • own oil wells • pump oil • sell it • problem • how to grow when there’s no more wells to own? • volatility of oil market • low margins for commodity products • high costs

  11. Example: cont’d • solution: reconceptualize business • oilfield expertise • benefits • everyone needs know-how • expertise is always valuable

  12. Hierarchy of knowledge • Knowledge • expert analysis • synthesis • integration with experience • Information • reports on data • summarization • Data • recorded information • The world • stuff happens

  13. Knowledge assets • Usually intangible • in worker’s heads • How to make experience explicit? • not just what? • but also why, how, and why not?

  14. AI + Knowledge Management • Model aspects of human thought on computers • Which aspects? • the storage and use of experience • What sub-field of AI studies this? • case-based reasoning

  15. Problem-solving • One of the first two areas tackled by AI research • other is natural language • How do we solve problems? • researchers looked at logic puzzles and problems of robot control

  16. Rule-based reasoning • What are the steps to the solution? • problem situation • desired result • Forward-chaining • reason forward from the problem • Backward-chaining • reason backward from the desired state • Build up large rule bases • also control knowledge

  17. Case-based reasoning • An alternative to rule-based problem-solving • “A case-based reasoner solves new problems by adapting solutions used to solve old problems” -- Riesbeck & Schank 1987

  18. Paradox of the expert • Experts should have more rules • can solve more problems • can be much more precise • But experts are faster than novices • who presumably have fewer rules • What does experience provide if it isn’t just “more rules”?

  19. Problems we solve this way • Medicine • doctor remembers previous patients especially for rare combinations of symptoms • Law • English/US law depends on precedence • case histories are consulted • Management • decisions are based on past experience • Financial • performance is predicted by past results

  20. Solution CBR Solving Problems Review Retain Database Adapt Retrieve Similar New Problem

  21. CBR System Components • Case-base • database of previous cases (experience) • episodic memory • Retrieval of relevant cases • index for cases in library • matching most similar case(s) • retrieving the solution(s) from these case(s) • Adaptation of solution • alter the retrieved solution(s) to reflect differences between new case and retrieved case(s)

  22. R4 Cycle RETRIEVE find similar problems RETAIN integrate in case-base CBR REUSE propose solutions from retrieved cases REVISE adapt and repair proposed solution

  23. P P P P P P P P P S S S S S S S S S CBR Assumption • New problem can be solved by • retrieving similar problems • adapting retrieved solutions • Similar problems have similar solutions ? X

  24. AI in Knowledge Management • Apply the CBR model to the organization rather than the individual • Retain the experience of the firm • Apply it in new situations • Do this in a consistent, automated way

  25. How to do this? • Very situation-specific • What is a case? • What counts as similar? • What do you need to know to adapt old solutions? • How do you find and remove obsolete cases?

  26. CBR Knowledge Containers • Cases • Case representation language • Retrieval knowledge • Adaptation knowledge

  27. Cases • Contents • lesson to be learned • context in which lesson applies • Issues • case boundaries • time, space

  28. Case representation language • Contents • features and values of problem/solution • Issues • more detail / structure = flexible reuse • less detail / structure = ease of encoding new cases

  29. Retrieval knowledge • Contents • features used to index cases • relative importance of features • what counts as “similar” • Issues • “surface” vs “deep” similarity

  30. Nearest Neighbour Retrieval • Retrieve most similar • k-nearest neighbour • k-NN • Example • 1-NN • 5-NN

  31. How do we measure similarity? • Can be strictly numeric • weighted sum of similarities of features • “local similarities” • May involve inference • reasoning about the similarity of items

  32. Adaptation knowledge • Contents • circumstances in which adaptation is needed • how to modify • Issues • role of causal knowledge • “why the case works”

  33. Learning • Case-base • inserting new cases into case-base • updating contents of case-base to avoid mistakes • Retrieval Knowledge • indexing knowledge • features used • new indexing knowledge • similarity knowledge • weighting • new similarity knowledge • Adaptation knowledge

  34. What this class is about • We will study examples of KM-related CBR applications • We will study CBR technology and research

  35. Next week • Case study • R. Burke & A. Kass (1994) "Tailoring Retrieval to Support Case-Based Teaching." Proceedings of the 12th Annual Conference on Artificial Intelligence. • Research • A. Aamodt & E. Plaza (1994) "Case-based reasoning: Foundational issues, methodological variations, and system approaches." AI Communications, 7:39-59

  36. Administrativa • Sign up for presentations • Sign up for librarian slots

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