1.  Knowledge Acquisition  Machine Learning

2. The transfer and transformation of potential problem solving expertise from some knowledge source to a program. Buchanan, 1983

3.  The process of acquiring, studying and organizing knowledge, so that it can be used in a knowledge-based system.  Expert may provide irrelevant, incomplete or inconsistent information.  Data and knowledge acquisition  Collect and analyze data and knowledge  Make key concepts of the system design more explicit

4.  Acquired knowledge may consist facts, rules, concept, procedures, heuristics, formulas, relationships, statistics, or other useful information Sources Documented Written, viewed, sensory, behavior Undocumented Memory Acquired from Human senses Machines

5. Levels Shallow Surface level Input-output Deep Problem solving Difficult to collect, validate Interactions between system components

6. Categories Declarative Descriptive representation Procedural How things work under different circumstances How to use declarative knowledge Problem solving Meta knowledge Knowledge about knowledge

7. Professionals who elicit knowledge from experts Empathetic, patient Broad range of understanding, capabilities Integrate knowledge from various sources Creates and edits code Operates tools Build knowledge base Validates information Trains users

8. Knowledge Acquisition Difficulties Problems in Transferring Knowledge Expressing Knowledge Transfer to a Machine Number of Participants Structuring Knowledge

9. Experts may lack time or not cooperate Testing and refining knowledge is complicated Poorly defined methods for knowledge elicitation System builders may collect knowledge from one source, but the relevant knowledge may be scattered across several sources May collect documented knowledge rather than use experts The knowledge collected may be incomplete Difficult to recognize specific knowledge when mixed with irrelevant data

10. Overcoming the Difficulties Knowledge acquisition tools with ways to decrease the representation mismatch between the human expert and the program (“learning by being told”) Simplified rule syntax Natural language processor to translate knowledge to a specific representation Impacted by the role of the three major participants Knowledge Engineer Expert End user

11. Critical The ability and personality of the knowledge engineer Must develop a positive relationship with the expert The knowledge engineer must create the right impression Computer-aided knowledge acquisition tools Extensive integration of the acquisition efforts Overcoming the Difficulties

12. Knowledge Acquisition Methods Manual Semiautomatic Automatic (Computer Aided)

13. Manual Methods - Structured Around Interviews Process (Figure next slide) Interviewing Tracking the Reasoning Process Observing Manual methods: slow, expensive and sometimes inaccurate

14. Elicitation Knowledge base Documented knowledge Experts Coding Knowledge engineer

15. Semiautomatic Methods Support Experts Directly (Figure next slide) Help Knowledge Engineers

16. Knowledge base Knowledge engineer Expert Coding Computer-aided (interactive) interviewing

17. Automatic Methods Expert’s and/or the knowledge engineer’s roles are minimized (or eliminated) Induction Method (Figure next slide)

18. Knowledge base Case histories and examples Induction system

19.  Machine learning is a specialized form of autonomous knowledge acquisition.  Autonomous knowledge creation or refinement through the use of computer programs.

20. Some tasks cannot be defined well, except by examples (e.g., recognizing people). Relationships and correlations can be hidden within large amounts of data. Machine Learning/Data Mining may be able to find these relationships. Human designers often produce machines that do not work as well as desired in the environments in which they are used.

21. 21 The amount of knowledge available about certain tasks might be too large for explicit encoding by humans (e.g., medical diagnostic). Environments change over time. New knowledge about tasks is constantly being discovered by humans. It may be difficult to continuously re-design systems “by hand”.

22.  Learning by memorization  Direct instruction  Analogy  Induction  Deduction

23.  It requires the least amount of inference and is accomplished by simply copying the knowledge in the same form that it will be used directly into the knowledge base.

24.  The knowledge must be transformed into an operational form before being integrated into the knowledge base  This type of learning used when a teacher presents a number of facts directly to us in a well organized manner.

25.  Is the process of learning a new concept or solution through the use of similar known concepts or solutions.  Here, previously learn examples serve as a guide.  Driving a truck using experience of driving a car.

26.  This form of learning requires the use of inductive inference  We use inductive learning when we formulate a general concept after seeing a number of instances or examples of the concept. Example: we learn the concepts of color or sweet taste after experiencing the sensations associated with several examples of color example objects or sweet foods

27.  It is accomplished through a sequence of deductive inference steps using known facts.  From the known facts, new facts and relationships are logically derived.  Example:(father X of Y), (father Y of Z); (Grandfather X of Z)