DT22 8 /3

1. DT228/3 Software and Knowledge Engineering Lecturer: Deirdre Lawless

2. Data... is raw. simply exists and has no significance beyond its existence (in and of itself). Information data that has been given meaning by way of relational connection. "meaning" can be useful, but does not have to be. Knowledge the appropriate collection of information, such that it's intent is to be useful. Understanding... cognitive and analytical. It is the process by which you can take knowledge and synthesize new knowledge from the previously held knowledge. The difference between understanding and knowledge is the difference between "learning" and "memorizing". People who have understanding can undertake useful actions Wisdom... an extrapolative and non-deterministic, non-probabilistic process. It calls upon all the previous levels of consciousness, and specifically upon special types of human programming (moral, ethical codes, etc.). Data, Information, Knowledge, Wisdom

3. Data, Information, Knowledge, Wisdom Examples • Data represents a fact or statement of event without relation to other things. • Ex: It is raining. • Information embodies the understanding of a relationship of some sort, possibly cause and effect. • Ex: The temperature dropped 15 degrees and then it started raining. • Knowledge represents a pattern that connects and generally provides a high level of predictability as to what is described or what will happen next. • Ex: If the humidity is very high and the temperature drops substantially the atmospheres is often unlikely to be able to hold the moisture so it rains. • Wisdom embodies more of an understanding of fundamental principles embodied within the knowledge that are essentially the basis for the knowledge being what it is. Wisdom is essentially systemic. • Ex: It rains because it rains. And this encompasses an understanding of all the interactions that happen between raining, evaporation, air currents, temperature gradients, changes, and raining.

4. Transition

5. Knowledge • Abugt dbesbt regtc uatn s uitrzt. • ubtxte pstye ysote anet sser extess • ibxtedstes bet3 ibtes otesb tapbesct ehracts • Does this mean anything to you ?

6. Knowledge • I have a box. • The box is 3' wide, 3' deep, and 6' high. • The box is very heavy. • The box has a door on the front of it. • When I open the box it has food in it. • It is colder inside the box than it is outside. • You usually find the box in the kitchen. • There is a smaller compartment inside the box with ice in it. • When you open the door the light comes on. • When you move this box you usually find lots of dirt underneath it. • Junk has a real habit of collecting on top of this box. • What is it?

7. What is Knowledge Management ? • An approach based on the central role of knowledge in organisations • Objective to manage and support knowledge work and to maximise the added value of knowledge for the organisation • Aims: • identifying and analysing knowledge and knowledge work • developing procedures and systems for generating, storing, distributing and using knowledge in the organisation.

8. What is Knowledge Management About? • improving the ability to acquire knowledge, • improving the quality of knowledge, • and using knowledge to its greatest advantage

9. Objective of KM • To create added value for the organisation at three distinct levels: • Improvement of existing business processes • what can we do better • Development of new products and services • what can we do more • Improving the strategic position, aimed at: • Developing unique knowledge • Applying knowledge to innovative products and services • Strengthening the competitive position • Safeguarding the organisation’s continuity • Improving flexibility • Creating an attractive work environment • Making the organisation independent of the individual employee’s knowledge

10. How can computers help? • Share knowledge • Discover Knowledge • Assist people • About both people and technology • Knowledge not just stored in a knowledge base but constructed through co-operation with a person using that knowledge base

11. Knowledge Engineering • KE is an engineering discipline that involves integrating knowledge into computer systems in order to solve complex problems normally requiring a high level of human expertise. • At present, it refers to the building, maintaining and development of knowledge-based systems

12. Knowledge Engineering • Or it refers to transferring human knowledge into some form of knowledge based system (KBS) • Five steps • Acquisition • Obtaining knowledge from various sources human experts, documents, existing computer systems etc • Validation • Check knowledge acquired using test cases • Representation • Producing a map of knowledge and encoding into some sort of knowledge base • Inferencing • Forming links in the knowledge so that a KBS can make a decision or provide advice • Explanation and justification • Allow a KBS to show how it reached a conclusion

13. Knowledge Engineer • Person who translates knowledge relating to an area of expertise into the knowledge base which supports a KBS

14. Types of Knowledge • Procedural • How to • E.g. I Know How To Drive A Car • Processes, Tasks, Activities • And conditions under which tasks are performed • And sequence of tasks • Conceptual • I know that … • About ways in which things (concepts) are related to each other and their properties

15. Types of Knowledge • Explicit • Knowledge at the forefront of a person’s brain • Thought about in a deliberate, conscious way • Concerned with basic tasks, basic relationships between concepts, basic properties of concepts • Not difficult to explain • Tacit • Deep, embedded knowledge • At the back of a person’s brain • Built from experience rather than being taught • Gain when practice • Leads to activities which seem to require no conscious thought at all

16. Procedural Knowledge How to boil an egg How to interview an expert How to tie a shoelace The properties of knowledge The position of keys on a keyboard E=mc2 Conceptual Knowledge Basic, Explicit Knowledge Deep, Tacit Knowledge Types of Knowledge • How to Boil An Egg • Simple task easily explained • How to tie a shoelace • Requires demonstration with commentary • E=mc2 • Simply relates concepts • The position of keys on a keyboard • Most people know this sub-conciously but few conciously Taken from Knowledge Acquisition in Practice A Step By Step Guide, Millton, Springer-Verlag

17. Eliciting Knowledge Most knowledge is in the heads of experts Experts have vast amounts of knowledge Experts have a lot of tacit knowledge They don't know all that they know and use Tacit knowledge is hard (impossible) to describe Experts are very busy and valuable people Each expert doesn't know everything

18. Knowledge Acquisition/Knowledge Engineering Knowledge Representation is about representing some knowledge First need to determine what that knowledge is the process of Knowledge Acquisition and Elicitation non-trivial process The information is often locked away in the heads of domain experts The experts themselves may not be aware of the implicit conceptual models that they use Have to draw out and make explicit all the known knowns, unknown knowns, etc….

19. Knowledge Acquisition • Capturing knowledge about a subject domain • From experts • And other sources • Using this to create a store of knowledge • Usable by many different applications, users and benefits • Does not have to be a database • Can be a knowledge web, ontology, knowledge document etc

20. Difficulties of knowledge acquisition • Experts find it difficult to • Express their knowledge in a manner fully comprehensible to the knowledge engineer • Know exactly what the engineer wants • Give the right level of detail • Present ideas in a clear and logical order • Explain all the jargon and terminology of the subject domain • Recall everything relevant to the project • Avoid drifting into talking about irrelevant things

21. Difficulties of knowledge acquisition • Engineers find it difficult to • Understand everything the expert says • Note down everything the expert says • Keep the expert talking about relevant issues • Maintain high level of concentration needed • Check they have fully understood what has been said

22. Difficulties of Knowledge Acquisition • Arise due to human cognition and communication • Humans are good at communication and performing complex activities • Not good at communicating complex activities to those not from the same subject areas

23. Knowledge Acquisition Bottleneck Nothing happens until knowledge is acquired Sources of knowledge are unreliable Domain experts provide incomplete, even incorrect knowledge Domain experts may not be able to articulate their knowledge Knowledge bases are hard to build Computational knowledge representations are complex Techniques Limited range Ignorance Experts poor appreciation of different types ignorance Expertise poor appreciation of different types ignorance need to organise knowledge into higher level units

24. What is a Knowledge Based System ? Use knowledge to solve problems Exercise knowledge to solve problems Knowledge used is that possessed by people knowledgeable in the domain Cause-and-effect Heuristics Etc Definition: A computerised system that uses domain knowledge to arrive at a solution to a problem within that domain. The solution is essentially the same as one concluded by a person knowledgeable about the domain when confronted with the same problem.

25. What is a Knowledge Based System ? Computer system that is programmed to imitate or assist with human problem-solving By means of artificial intelligence And reference to a database containing human knowledge on a particular subject. Core components are the knowledge base and the inference mechanisms. Typical Architecture a knowledge base (where the knowledge is stored) Data plus more, an inferencing engine or reasoning engine, a working memory where the initial data and intermediate results are stored

26. Knowledge based systems Use highly specific domain knowledge Heuristic nature of knowledge rather than algorithmic Human ability Separation of knowledge from how it is used Knowledge of how to infer something

27. Knowledge Based Systems Development Team Expert System Development Team Project Manager Knowledge Engineer Domain Expert Programmer Expert System End-User

29. Intelligence ?

30. Intelligent System ?

31. Artificial Intelligence Definition ? Science that provides computers with the ability to represent and manipulate symbols so that they can be used to solve problems not easily solved through algorithmic methods Most methods founded on realization that intelligence is tightly coupled with knowledge Knowledge is associated with symbols that are manipulated Human intelligence ? Definition ?

32. What is Artificial Intelligence ? Agreement that it is concerned with two things Studying human thought processes Representing these processes via machines Computers Robots Artificial Intelligence is behaviour by a machine which if performed by a human would be considered intelligent “Artificial Intelligence is the study of how to make computers do things at which, at the moment, people are better” (Rich & Knight 1991)

33. Typical problems addressed by KBS

34. Knowledge Representation Programming language is a means of representing knowledge Procedural knowledge “how to” Knowledge about how to perform some task Declarative knowledge “what is “

35. Rule-based reasoning • One can often represent the expertise that someone uses to do an expert task as rules. • A rule means a structure which has an if component and a then component.

36. Other Examples of Rules • if - the leaves are dry, brittle and discoloured • then - the plant has been attacked by red spider mite • if - the customer closes the account • then - delete the customer from the database

37. Rules • The statement, or set of statements, after the word if represents some pattern which you may observe. • The statement, or set of statements, after the word then represents some conclusion that you can draw, or some action that you should take. • IF some condition(s) exists THEN perform some action(s) • IF-THEN • Test-Action

38. Rule-Based Systems • A rule-based system, therefore • identifies a pattern and draws conclusions about what it means OR • identifies a pattern and advises what should be done about it OR • identifies a pattern and takes appropriate action.

39. Long Term Memory Short Term Memory Production rule Fact Interpreter (Inference engine) Conclusion Rule-based system model

40. Knowledge Representation Rules represent Relations Recommendations Directives Strategies

41. Knowledge Representation…Relations • IF fuel tank is empty then car is dead.

42. Recommendation • If the season is autumn • And the sky is cloudy • And the forecast is drizzle • Then the adivce is take an umberella

43. Directive • If the car is dead • And the fuel tank is empty • Then the action is refuel the car

44. Strategy • If the car is dead • Then the action is check the fuel tank • step 1 is complete • If step1 is complete • And the fuel tank is empty • Then check the battery • step 2 is complete

45. Class Exercise : Rule-Based System for Tic-Tac-Toe What rules do we need ? Rules may have tests that are satisfied at the same time – need some mechanism for selecting right rule