1. Intelligent Systems Lecture 8 Acquisition of knowledge and learning, tools of knowledge engineering

2. What is knowledge acquisition? knowledge acquisition without computer –Methodic methods of acquisition of knowledge from experts during interview, dialog, questionnaire design and others knowledge acquisition with computer –Data mining or intelligent data analyzing –learning

3. Learning systems Knowledge level learning (KLL) Symbol level learning (SLL) AnalyticalEmpirical Deep Brief Non-deductive knowledge level learning (NKLL) (induction) By set of examples By single example (by explanation) Conceptualization (clusterization, classification ) By analogy Knowledge driven Data driven By examples By discovery Main paradigms of learning and knowledge acquisition in knowledge engineering

4. What is induction? Let the following facts: Titmouse is a bird, Sparrow is a bird, Crane is a bird, Titmouse is able to fly, Sparrow is able to fly, Crane is able to fly, Then the system having inductive capabilities may be to make conclusion that A bird is able to fly.

5. Example of method of induction – DSM-method Let a i – potential reason b j – potential consequence M + - matrix of confirming of hypothesis of causal dependence between a i and b j b 1 b 2 ….b j …b m a 1 h 11 h 12 …h 1j …h 1m a 2 h 21 h 22 …h 2j …h 2m M + = a i h i1 h i2 …h ij …h im a n h n1 h n2 …h nj …h nm h ij = k ij + / k ij, k ij + - number of facts confirming of hypothesis, k ij – number of all facts (experiments)

6. What is needed define to define of induction Method and form of presentation of facts Method of inductive inference (algorithm of processing of facts) What and in which form we want to obtain Conditions of finishing of inductive inference Method (algorithm) of extraction of desired rules from results of inductive inference

7. Classifications of tools of knowledge engineering Universal algorithmic programming languages Object-oriented programming languages (C++, Object Pascal, Java) Logical programming languages (Prolog, Lisp, Smalltalk) Toolboxes for AI developers (e.g. components for programming of expert systems or neural networks) (OPS 5, NeuralBase) Universal shells (or development environment) (e.g. Expert Shells) (ESWin, ExSys, G2, CExpert, CLIPS, Kee, Leonardo, NExpert Object, ART*Enterprise, Kappaб, Flex) problem-specific or domain-specific shells (or development environment), in particular, empty expert systems (EMYCIN)

8. Expert shell ESWin (Insycom Ltd.) Consist of: –Language for description of knowledge base –Expert shell for developer –Expert shell for end user –Two kinds of editors of Knowledge Base (KB) –Program utility for view and diagnostics of KB –Program utility for improvement of structure of KB Solving of task by backward fuzzy inference Aims to using for development of expert systems for diagnostics, identification, support of making of decisions

9. Sources of facts Dialog with user Databases by SQL-query forming automatically during dialog External special programs been developed in case that possibilities of ESWin is not enough for solving of task For example, as external program may be any neural network

10. Knowledge base Consist of: –TITLE = ‹name of Expert System› –COMPANY = ‹name of organization - owner of ES› –Frame named as Goal with names of tasks solved by expert system –Other frames describing of domain –Rules for solving of tasks –Descriptions of linguistic variables (if ones are used in expert system)

11. Structure of frame FRAME (‹ type of the frame ›) = ‹ a name of the frame › PARENT: ‹ a name of the frame - parent › OWNER: ‹ A name of slot 1 › ….. …. ‹ A name of slot i › (‹ type of slot ›) [‹ a question of slot ›?] {‹ the comment of slot ›}: (‹ value 1 ›; ‹ value 2 ›; …; ‹ value m ›) … ‹ A name of slot n › ….. ENDF Types of frames: Class Instance Template Types of slots: Symbol Number LV – linguistic variable

12. Structure of rules RULE ‹number› ‹condition 1› ‹condition 2› … ‹condition m› DO ‹conclusion 1› ‹conclusion 2› … ‹conclusion n› ENDR Relations in conditions can be: EQ or = Equally; GT or > It is more; LT or < It is less; NE or <> Not equally; IN Two frames are connected by the relation "part of" (there is a connection through slot OWNER). Relations in the conclusions can be: EQ or = Equally (creation of the fact - slot in a frame-instance); IN Inclusion in the frame-owner (creation of connection - slot OWNER in the frame-instance); DL Remove of slot in a frame-instance; EX Execute of the external program; FR Output of a frame-instance; GO Execute of a rule; MS Output of the message to the screen; GR Output to the screen of a graphic file (formats *.gif, *.avi or *.htm).

13. Linguistic variables Parameters describing of ones: –Name –Set of symbolic values –For every symbolic values Minimal numeric value Maximal numeric value Number of values of membership function Set of values of membership function

14. Example of KB in ESWin (fragment)

15. Expert shell ESWin for developers

17. Editor of knowledge base

18. Editor of KB (editing of linguistic variables)

19. Other editor of KB

20. G2 (Gensym inc. ) Gensym's underlying product technology is built around its powerful G2 rule engine platform that drives critical business and operations decisions. Unlike other rule engines, G2 works in real time and is proven in the most demanding mission-critical applications. G2 combines real-time operational data, business policies, and expert knowledge to automate decisions and actions.

21. Products based on platform G2 G2 Optegrity for Manufacturing - for predictive abnormal condition management applications in the process manufacturing industries.G2 Optegrity for Manufacturing G2 NeurOn-Line for Manufacturing - for predicting, controlling, and optimizing the product quality of complex manufacturing processes through the use of real-time neural network and rule-based technology.G2 NeurOn-Line for Manufacturing G2 ReThink for Business Processes - for rule-driven, real-time business process management.G2 ReThink for Business Processes G2 e-SCOR for Supply Chains - for rules-driven, supply-chain modeling and simulation.G2 e-SCOR for Supply Chains G2 webSCOR.com for Supply Chains - for collaborative supply-chain design via the Web.G2 webSCOR.com for Supply Chains G2 Integrity for Networks - for maximizing the availability and service quality of telecommunication networks.G2 Integrity for Networks