Knowledge Acquisition and Learning by Experience – The Role of Case-Specific Knowledge

1. Knowledge Acquisition and Learning by Experience –The Role of Case-Specific Knowledge • Knowledge modeling and acquisition • Learning by experience • Framework for knowledge modeling • Knowledge modeling problem areas • The CBR cycle • CBR applications • Bridging knowledge level and symbol level • Iterative cycle • CREEK • Modeling the knowledge content of CBR systems

2. Knowledge modeling and acquisition • Cooperation between domain experts and knowledge engineers • Constructing a body of knowledge and a KBS which can be viewed as a qualitative model describing parts of the real world • Largely manual – knowledge acquisition • Largely automatic – machine learning

3. Learning by experience • The ability to adapt to an evolving environment • Three main solutions: • Knowledge-intensive learning • Deductive methods • Complete domain theories(Not based on “superficial” syntactic similarities or discrimination criteria) • Apprenticeship learning • Observing and analysing the users' problem solving steps • No particular learning method • Sustained learning • Case-based reasoning • Learning specific knowledge in terms of past cases • Using past cases to solve new ones • Learning: • Extract relevant information • Index in the system's knowledge structure • Considerable growth during the last few years

4. Domain and task • Open problem domain: • Frequent changes • Incomplete • Weak theory domain: • Uncertain relationships between concepts • Typical open and weak theory domains: • Medical diagnosis • Geological interpretation • Investment planning • Most engineering domains

5. Domain and task (2) • Strong theory domains: • More certain relationships • Mathematical domains • Closed technical domains • Some games • May still lack efficient algorithms • Open and weak domains may still have significant domain knowledge available

6. Framework for knowledge modeling • Initial knowledge modeling (realization) • Analyse the domain and task in question • Create the conceptual models necessary for communication • Implement the initial operational and fielded version of the system • Knowledge maintenance (continues throughout the system's lifetime) • Updating and refining the knowledge model • Regular use improves the system • Correcting errors • Improving knowledge quality • Improving performance efficiency • Adjust system behaviour according to changes in the environment

7. Framework for knowledge modeling

8. Knowledge modeling problems • Capture the knowledge content • Make it efficient to use (availability) • Easy to understand (close to a human interpretable language) • Three problem areas: • Knowledge acquisition • Knowledge representation • Learning • These problem areas apply to both the conceptual model and the internal model

9. The knowledge aquisition problem • Knowledge groups: • Tasks: defined by the goal the system tries to achieve • Problem solving methods: used to accomplish the tasks • Domain knowledge: needed by the methods • Knowledge level modeling • Data structures and programs • A “symbol level” was suggested by Newell as a distinct level above the data structures and programs • Lately: from intentional, purpose-orientation described by Newell, to a more structured and useful type

10. The knowledge representation problem • Applies to both knowledge level and symbol level • Increased focus on capturing knowledge content • Bridging the gap between the two levels • Work within intelligent system architectures has lead to a better understanding of the symbol level • Better understanding of the symbol level has lead to the realization of KBS dealing with real-world open application domains • Real-world open application domains has lead to more research on knowledge level modeling

11. The learning problem • Trend: case-based approach to sustained learning • Learning as a natural sub-process of problem solving • Evaluating the solution by applying it in the real world • Extract useful information from the problem solving experience and integrate it into the knowledge base • Generalizing • Instance generalization is done during problem solving • Generalizing knowledge during case retrieval (the partial matching when finding similar cases is kind of a generalization) • Generalization is implicit in the similarity assessment made during case retrieval

12. Case-based problem solving and learning • Similar to human problem solving • Main types: • Exemplar-based reasoning (using one past case as the solution) • Instance-based reasoning (specialized Examplar-based) • Memory-based reasoning (memory organization) • Main stream case-based reasoning • Analogy based reasoning

13. The CBR cycle • Retrieve • Reuse • Revise • Retain

14. CBR applications • PROTOS • Diagnosing hearing disorders • Using feature sets for matching • Indexed by remindings from features • Learning apprentice that relies on the user (the user will have to tell PROTOS whether the suggested solution is good or not) • Does not adapt previous solutions to new problem • CASEY • Combining case-based and model-based reasoning • Diagnosing heart diseases • Adapting past solutions to new problems • Interacts with a model-based reasoning system instead of relying on user feedback

15. KA+ML integration in an iterative modeling cycle • More iterative, less top-down driven modeling process

16. KA+ML integration in an iterative modeling cycle (2) • Incorporate backtracking of decisions as part of the knowledge acquisition and learning strategies • Stronger emphasis on bottom-up learning • Previous approaches to bridging the KL-SL gap • Automatic operationalization (top-down) • Pre-defined links (bottom-up)

17. KA+ML integration in an iterative modeling cycle (3) • Balance top-down, knowledge-driven and bottom-up, symbol-level driven modeling • KA methods used for developing an initial knowledge level model • Continuous evolution of models by sustained learning from experience using ML methods • Active user involvement • A modeling language with specified semantics

18. CREEK • An architecture for knowledge-intensive case-based problem solving and learning • Four modules integrated within a common conceptual knowledge fundament: • Object-level domain knowledge model • Application strategy level model • Combined reasoning model • Sustained learning model • All types of knowledge and information are captured in CreekL, a frame-based representation language

19. CREEK representation • Semantic network with nodes corresponding to concepts and links corresponding to relations between concepts • Supports user-defined relations (has-color) as well as symbolic values (red)

20. CREEK problem solving • Explanation engine: • Activate relevant parts of the semantic network • Generating and explaining derived information • Focusing towards a conclusion that conforms with the goal • Activate-explain-focus cycle specialized for each of the four CBR tasks (retrieve, reuse, revise, retain) • Determine relevant features • Retrieve most likely case • Modify solution of retrieved case • Asses relevance and justify validity of solution • Learn from the experience

21. CREEK sustained learning • Learns from every problem solving experience • Main target for learning process is the case base • Continually improves through the solving of problems • Shift to adaptive and continually evolving systems

22. Modeling the knowledge contents of CBR systems • Knowledge containers • Vocabulary • Set of cases • Similarity assessment knowledge • Solution transformation knowledge • Focus on knowledge contents

23. Conclusions • From general problem solving to an explicit knowledge model • Rule-based systems • Knowledge-intensive models • Knowledge to control the problem solving and learning processes • Knowledge level modeling • Sustained learning • Iterative knowledge modeling cycle • CREEK