Learning situation-specific knowledge for solving situation-specific problems. A. Aamodt, NTNU,

Transcription

1 Learning situation-specific knowledge for solving situation-specific problems A. Aamodt, NTNU,

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5 The CBR Cycle Problem New Case Learned Case RETAIN Past Cases General Knowledge Retrieved Case New Case Tested/ Repaired Case REVISE Solved Case Confirmed Solution Suggested Solution

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15 (H. Kitano et. al. 93) A. Aamodt, NTNU,

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18 (S. Kedar-Cabelli 86) A. Aamodt, NTNU,

19 83) A. Aamodt, NTNU,

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23 excerpt A. Aamodt, NTNU,

24 Transformational and Derivational analogy (J. Carbonell 83) - Transformational A. Aamodt, NTNU,

25 - Derivational A. Aamodt, NTNU,

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28 Dynamic Memory (Scank & Kolodner 83) A. Aamodt, NTNU,

29 Example A. Aamodt, NTNU,

30 Category Structure (Porter & Bareiss 87) A. Aamodt, NTNU,

31 The CBR Cycle Problem New Case Learned Case RETAIN Past Cases General Knowledge Retrieved Case New Case Tested/ Repaired Case REVISE Solved Case Confirmed Solution Suggested Solution

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41 Some early example systems: PROTOS CHEF CASEY CREEK A. Aamodt, NTNU,

42 PROTOS Case-based classification in weak-theory domains. Domain: Auditory diseases Learning of (polymorph) concepts Problem description is set of findings. Solution is a diagnostic class. Knowledge types are - case memory of findings sets associated with diagnostic classes - general concepts and relations, as well as diagnoses exemplars (cases) interconnected in a category structure User has strong role in the classification and learning methods Also an approach to 'bottom-up' knowledge acquisition - acquiring necessary knowledge to explain case matching and reuse ICML-99 (A. Aamodt) 42

43 ICML-99 (A. Aamodt) 43

44 PROTOS Retrieve Selecect candidate cases based on remindings, direct to cases or via categories Use 'explanation-based pattern matching' to explain improve the match Reuse Copy class of best matched case, and suggest to user If rejected, ask for additional info from user, and attempt to retrieve a better match ICML-99 (A. Aamodt) 44

45 PROTOS Revise Use evaluate solution If failure, user supplies correct solution Retain Learn new case or only update reminding (index) strengths Attempt to explain feature relevance, based on category structure Heavy user-interaction ICML-99 (A. Aamodt) 45

46 CHEF Case-based planning. Domain: Cooking Learning of plans (recipes) Problem description (planning goal) is desired dish Solution is a cooking recipe Knowledge types are - case memory of plans - links from goals to anticipated failures - plan modification rules - memory of repair strategies for failed plans Strong model assumption, no user involvement Special feature: Repair of a solution after it has been 'applied' and evaluated ICML-99 (A. Aamodt) 46

47 ICML-99 (A. Aamodt) 47

48 CHEF Retrieve Anticipate failures - follow pointers from goals Search index structure for best plan - satisfy most goals and failures Reuse If not all goals satisfied: Modify plan - apply modification rules - apply critics ICML-99 (A. Aamodt) 48

49 CHEF Revise Evaluate solution - simulate in internal world model If plan failures: Repair - search for suitable TOP - apply all repair strategies under it - test and select the best one - include doman-dependent criteria Retain Learn new plan - plain memory indexing Learn features that predict failure - generalize to most general (explanation-based learning) Learn repairs to faulty plans - retain critics associated with ingredients ICML-99 (A. Aamodt) 49

50 CASEY Case-based diagnosis in medicine (appl.: heart failue diseases) Learning of performance speed Problem description is list of symptoms Solution is a diagnosis, and an explanation for it Knowledge types are - case memory of symtpom sets to diagnoses, indexed by causal states and observable symptoms, and explained by causal relationships - general domain model is pre-existing causal reasoning system Closed system, no user-interaction, relies heavily on causal model ICML-99 (A. Aamodt) 50

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52 CASEY Retrieve - symptoms are linked to general causal states which is primary index to case base - observable symptoms is secondary index Reuse - attempt to copy diagnosis of retrieved case - adaptation based on modifying explanation structure of retrieved case (- solves problem by causal model is CBR fails) Revise (is not explicitly specified) Retain - updating importance of features - storing new case, including explanation ICML-99 (A. Aamodt) 52

53 CREEK Case-based reasoning in open and weak theory domains; diagnosis problems (appl.: oil-well drilling, medicine) Problem description is problem solving goal, solution constraints, and list of findings Solution is (one or more) diagnoses and repairs Knowledge types are - case memory of findings to solutions, indexed by relevant findings; cross-case indexes to neighbouring cases and between diagnosis and treatments - general domain knowledge as deep relationships or heuristiv rules - all knowledge integrated into a single semantic network of concepts and relations - each concept and each relation explicitly represented as frames ICML-99 (A. Aamodt) 53

54 CreekL Knowledge Types t h i n g g e n e r i c c o n c e p t s g e n e r a l d o m a i n c o n c e p t s c a s e c a s e 7 6 c a s e c a s e s ICML-99 (A. Aamodt) 54

55 The Creek Explanation Engine Goal - Appl. task is defined Situation - Findings are listed - Constraints are specified - Solution asked for Goal - Appl. task accomplished Situation - Findings explained - Constraints confirmed - Solution found ACTIVATE FOCUS EXPLAIN ICML-99 (A. Aamodt) 55

56 CREEK Retrieve - context focusing by spreading activation in the semantic netowrk, followed by - index retrieval of possible cases, followed by - explanation-driven selection of best match Reuse - attempts to copy solution from matched case - explanation-driven adaptation, by combining explanantion of retrieved case with general domain model Revise - user evaluates and gives feedback - case status info kept and used in case selection and reuse Retain - attempts to merge the two cases - stores relevant findings, sucessful and failed solutions, and their explanations - updating the strength of indexes ICML-99 (A. Aamodt) 56

57 CBR methods The Data-- Knowledge Dimension Data intensive - Knowledge poor - A case is a data record - Similarity asessment based on simple metric Knowledge intensive - Data Poor - A case is a user experience - Similarity asessment is an explanation process Both knowledge and data intensive - Multiple case contents - Multiple similarity asessment methods A. Aamodt, NTNU,

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63 Some more recent applications PSA Peugeot Citroën diagnosis of modern cars DaimlerChrysler technical assistance to dealership Ford squeak, rattle & vibration prevention CFM international diagnosis of Boeing 737 engines Sextant Avionique diagnosis of on-board electronics for Airbus airplanes Centre Spatial Guyanais reliability of Ariane IV Aérospatiale-Matra Design or Airbus parts MET-Ericsson System tests for producing telecom boards Legrand cost estimation for plastic parts production Analog Devices sales support for electronic devices Siemens technical support for industrial automation devices Unilever process control for producing bleach 63

64 Spin-off firma fra vår gruppe: Real-world application: A CBR system for advice giving in data-intensive environments

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66 Drilling challenge Unwanted down2me One day of unwanted downtime on this rig means increased cost of 1,6 MNOK for the ongoing drilling operation. Providing the relevant experience and getting the right information precisely when needed will reduce unwanted operational downtime. The result is a more reliable drilling process, reduced drilling costs, and increased productivity.

67 Approach Human experiences are gathered as cases from earlier incidents The case base is linked to a model of general domain knowledge An ongoing drilling opera2on is con2nuously supervised, collec2ng data from a large number of parameter readings The system interprets these readings retrieves one or more past cases that match the current state of the drilling process warns the user if the past case suggests that an unwanted event is building up gives advice on how to avoid a possible unwanted event.

68 Improved decision support through experience capture and reuse

69 The contents of a case Administrative data Geological data Planned data Operation activity Observed data Interpretations of observed data Predicted problem Cause of problem Remedy to avoid problem Lesson learned

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73 Recent mostly used now (2010): MyCBR (DFKI, Kaiserslautern) jcolibri (Univ. Complytense, Madrid A. Aamodt, NTNU,

74 Useful URLs mnemosyne.itc.it:1024/avesani/html/cbr.html... (my homepg.) A. Aamodt, NTNU,

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