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: 1) Knowledge-intensive learning ● Deductive methods ● Complete domain theories (Not based on “superficial” syntactic similarities or discrimination criteria) 2) Apprenticeship learning ● Observing and analysing the users' problem solving steps ● No particular learning method ● Sustained learning 3) 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: 1)Knowledge acquisition 2)Knowledge representation 3)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