IMAT3406 Fuzzy Logic and Knowledge Based Systems (AI)

1. IMAT3406 Fuzzy Logic and Knowledge Based Systems (AI) Knowledge Acquisition I

2. Introduction • We look at the different types of knowledge • We identify what we mean by “knowledge acquisition”? • The difficulties of acquiring knowledge is discussed. • Key players, who affect the process, and their roles are identified and discussed.

3. Topics of Discussion • Knowledge acquisition. • Difficulties in acquiring knowledge. • Key players.

4. Knowledge • “Knowledge is information of which someone is aware. Knowledge is also used to mean the confident understanding of a subject, potentially with the ability to use it for a specific purpose”. (http://en.wikipedia.org/wiki/Knowledge) • Other definitions: • knowledge 'tracks the truth‘ (Robert Nozick) • It is suggested that our definition of knowledge needs to require that the believer's evidence is such that it logically necessitates the truth of the belief (Richard Kirkham) • Knowledge is "information combined with experience, context, interpretation, and reflection. It is a high-value form of information that is ready to apply to decisions and actions." (T. Davenport et al., 1998) • "Explicit or codified knowledge refers to knowledge that is transmittable in formal and systematic language. Tacit knowledge has a personal quality, which makes it hard to formalize and communicate." (I. Nonaka, 1994)

5. Knowledge • Explicit knowledge is referred to the knowledge which has been articulated, codified and stored in certain mediums. The most common form of explicit knowledge are manuals, documents, procedures and stories. The are also other forms of knowledge can be in the form of audio vision and other multimedia form of representations. A work of art and product design can be seen as yet another forms of explicit knowledge where human skills, motives and knowledge are externalized. • Tacit Knowledge: Tacit knowledge consists often of habits and culture that we do not recognize in ourselves. The tacit aspects of knowledge are those that cannot be codified, but can only be transmitted via training or gained through personal experience. Tacit knowledge has been found to be a crucial input to the innovation process. A nation’s ability to innovate depends on its level of tacit knowledge of how to innovate (conduct research, develop prototypes of new products & processes, adapt these prototypes into models fit for mass-production) and of how to implement innovations into manufacturing, defense, communications, transportation, etc. Ref: http://en.wikipedia.org/

6. Knowledge • “Information is data endowed with relevance and purpose. Converting data into information thus requires knowledge” (Peter Drucker) • Example to define knowledge, information and data: How to bake a cake (Mezei, D. , 2003) • Data - the different ingredients i.e. flour, water, eggs, sugar etc. • Information - the recipe i.e. mix flour, eggs and water, preheat oven to 400 etc. • Knowledge - the know how the cook uses to bake the cake, to best utilize the data and information available

7. Knowledge acquisition • Definition - The process of acquiring, organizing, and studying knowledge.  •  Two main types of sources of knowledge • Documented (which can take many forms) and • Undocumented (usually in the expert's mind).  • There are two types of knowledge: • shallow knowledge • deep knowledge.

8. Knowledge acquisition • Shallow knowledge: An expert has a base understanding of the subject, some of which could be described as general. • Example: surface level information might be represented as: If the weather is bad then stay in bed.

9. Knowledge acquisition • Deep Knowledge: This is the knowledge that has been acquired by years of experience and study and is the detailed `core` of the knowledge base.   • Example: if we take the weather example again, we may ask: for instance what do we class as bad weather, why does bad weather cause us not to want to go out, what's so good about staying in bed etc. Frames & semantic networks enable us to represent deeper knowledge. 

10. Knowledge acquisition • Categories of Knowledge (three main ones): • Declarative - i.e. descriptive knowledge, facts.  • Procedural - how things are done, how to use the declarative knowledge.  • Semantics - consider words & symbols & what they mean, how they are related & manipulated. Reflects cognitive structure. 

11. Difficulties of acquiring knowledge • Why is it difficult to transfer knowledge?  • Hard to get experts to express how they solve problems • Compiling and refining all experts’ knowledge • Bringing together the ideas of all those involved in the knowledge transfer process.  • Representation on machine requires detailed expression i.e. at a very low level. Must be represented in a structured way.

12. Key Players involved in developing an expert system are: • Expert(s) who have the knowledge of an area being considered for designing a KBS for • Knowledge Engineer(s) who collect the information that the client wants in the system and then put it all into the program in a logical way. • Example: if a client, who is an expert in cars, wanted a program to identify different types of cars, then the knowledge engineer will need to collect the necessary information about different cars and their features. It is up to the knowledge engineer to capture the knowledge of the domain expert into a knowledge base, which is then used to build up an expert system. • User(s) • Programmers • Management

13. Conclusion • Defined what we mean by knowledge and its acquisition. • Why is it difficult to acquire knowledge? • Key players who are important in the process of knowledge acquisition.