Basic concept of AI and its applications with knowledge based engineering

1. Artificial Intelligence
SUBMITTED BY,
PRASAAD KULKARNI
(15MCD0035)
VIT University, Vellore, Tamilnadu

2. Outline
 AI History
 Overview of AI application areas

3. History: AI and the Rationalist
 Modern research issues in AI are formed and
evolve through a combination of historical,
social and cultural pressures.
 The rationalist tradition had an early
proponent in Plato, and was continued on
through the writings of Pascal, Descates, and
Liebniz
 For the rationalist, the external world is
reconstructed through the clear and distinct
ideas of a mathematics

4. History: Development of
Formal Logic
 The goal of creating a formal language for
thought also appears in the work of George
Boole, another 19th
century mathematician
whose work must be included in the roots of
AI
 The importance of Boole’s accomplishment is
in the extraordinary power and simplicity of
the system he devised: Three Operations

5. History: the Turning Test
 The imitation game (1950)

6. AI application areas
 Game Playing
 Much of the early research in state space search
was done using common board games such as
checkers, chess, and the 15-puzzle
 Games can generate extremely large search
spaces. Theses are large and complex enough to
require powerful techniques for determining what
alternative to explore

7. AI application areas
 Automated reasoning and Theorem Proving
 Theorem-proving is one of the most fruitful
branches of the field
 Theorem-proving research was responsible in
formalizing search algorithms and developing
formal representation languages such as predicate
calculus and the logic programming language

8. AI application areas
 Expert System
 One major insight gained from early work
in problem solving was the importance of
domain-specific knowledge
 Expert knowledge is a combination of a
theoretical understanding of the problem
and a collection of heuristic problem-
solving rules

9. AI application areas
 Expert System
 Current deficiencies:

Lack of flexibility; if human cannot answer a
question immediately, he can return to an
examination of first principle and come up
something

Inability to provide deep explanations

Little learning from experience

10. AI application areas
 Natural Language Understanding and
Semantics
 One of the long-standing goals of AI is the
creation of programming that are capable
of understanding and generating human
language

11. AI application areas
 Modeling Human Performance
 Capture the human mind (knowledge
representation)

12. AI application areas
 Robotics
 A robot that blindly performs a sequence of
actions without responding to changes or
being able to detect and correct errors
could hardly considered intelligent
 It should have some degree of sensors and
algorithms to guild it

13. AI application areas
 Machine Learning
 Learning has remained a challenging area
in AI
 An expert system may perform extensive
and costly computation to solve a problem;
unlike human, it usually don’t remember
the solution

14. Decision Tree Example

15. Software development: conventional
systems and KBS
 You are probably familiar with a standard model of
the software development life cycle. It is likely to be
something like this:
Feasibility study
→ Analysis
→ Requirements definition
→ Design →
Implementation
→ Testing
→ Maintenance & review

16. Software development: conventional
systems and KBS
♦ Knowledge-based systems require
special approaches to systems
analysis, especially to the collection of
the data (or rather knowledge) on which
the system is based.
♦ We will discuss the ways in which this
model needs to be modified to take
account of these special features in
lecture 8.

17. KNOWLEDGE BASED
ENGINEERING

18. Knowledge Engineering
♦ The term "knowledge engineering" is
often used to mean the process of
♦ designing
♦ building
♦ installing
an expert system or other knowledge-
based system. In other words, the
whole process of making a KBS, from
beginning to end.

19. Knowledge Engineering
♦ Some authors use the term to mean just
the phase in which the knowledge base
is built.

20. Building the knowledge base
 Five processes can be identified:
 1. Knowledge acquisition
 2. Knowledge analysis & representation
 3. Knowledge validation
 4. Inference design
 5. Explanation and justification
 These are not stages that have to follow each
other - some of them will run concurrently.

21. Knowledge Acquisition
 Knowledge acquisition is:
The process of gathering the knowledge to stock the expert
system's knowledge base.
 This has proved to be the most difficult component of the
knowledge engineering process. It's become known as the
'knowledge acquisition bottleneck', and expert system
projects are more likely to fail at this stage than any other.
 This is the principle reason why expert systems have not
become more widespread.

22. Knowledge Acquisition
 Sources of knowledge:
 Documents: textbooks, journal articles, technical
reports, records containing case histories, etc.
 This will almost never be sufficient to provide the
knowledge base for a real-world expert system.
 The range of problems which a textbook examines and
solves is always smaller than the range of problems that a
human expert is master of.

23. Knowledge Acquisition
 Sources of knowledge:
 Human experts

24. Knowledge Elicitation
 The most important part of knowledge acquisition is
knowledge elicitation - obtaining knowledge from a human
expert (or human experts) for use in an expert system.
 Knowledge elicitation is difficult. Hence the knowledge
acquisition bottleneck mentioned above.
 It is necessary to find out what the expert(s) know, and
how they use their knowledge.

25. Knowledge Elicitation
 Expert knowledge includes:
 domain-related facts & principles;
 problem-solving strategies;
 meta-knowledge - for instance, knowledge about when
to use a particular piece of knowledge;
 explanations and justifications.

26. Knowledge Elicitation
 The knowledge elicitation/analysis task
involves
♦ finding at least one expert in the domain who:
♦ is willing to provide his/her knowledge;
♦ has the time to provide his/her knowledge;
♦ is able to provide his/her knowledge.
- any or all of these are liable to prove
difficult.

27. Knowledge Elicitation
 The knowledge elicitation/analysis task involves
 repeated interviews with the expert(s), probably
combined with other, non-interview, techniques.

28. Knowledge Elicitation - the
compiled knowledge problem
 One major obstacle to knowledge elicitation:
experts cannot easily describe all they know about
their subject.
 They do not necessarily have much insight into the
methods they use to solve problems.
 Their knowledge is "compiled" (like a compiled
computer program - fast & efficient, but
unreadable).

29. Knowledge Elicitation -
interview techniques
 Some of the interview techniques used in
knowledge elicitation:
 Unstructured interview. A general discussion of the
domain, designed to provide a list of topics and
concepts.
 Structured interview. Concerned with a particular
concept within the domain - a particular problem-solving
skill or small group of skills.

30. Knowledge Elicitation -
interview techniques
 Interview techniques :
 Problem-solving interview. The DE is provided with a
real-life problem, of a kind that they deal with during
their working life, and asked to solve it. As they do so,
they are required to describe each step, and their
reasons for doing what they do. The transcript of their
verbal account is called a protocol.

31. Knowledge Elicitation -
interview techniques
 Interview techniques :
 Think-aloud interview. As above, but the DE merely
imagines that they are solving the problem presented to
them, rather than actually doing it. Once again, they
describe the steps involved in solving the problem.

32. Knowledge Elicitation -
interview techniques
 interview techniques :
 Critical incident analysis. The DE is asked to provide
details of cases which were particularly difficult, or of
special interest for some other reason. He/she
describes how they were solved, and the lessons that
were learnt.

33. Knowledge Elicitation -
non-interview techniques
 Some of the non-interview techniques used in
knowledge elicitation:
 Sample lecture preparation. The DE prepares a lecture,
and the KE analyses its content.

34. Knowledge Elicitation -
non-interview techniques
 Non-interview techniques:
 Concept sorting ("card sort"). The DE is presented with
a series of cards, with the names of domain concepts
written on them, spread out on a table top, and asked to
arrange them into clusters, in such a way that the cards
in each cluster have something important in common.
Then the DE is asked to name the principles that he/she
has used to form these clusters. This process can be
repeated to produce a hierarchy of concepts.

35. Knowledge Elicitation -
non-interview techniques
 Non-interview techniques:
 Repertory grid (particularly the "laddered grid"
technique).
 Questionnaires. Especially useful when the knowledge
is to be elicited from several different experts.

36. Knowledge Elicitation -
interview techniques
 It is standard practice to tape-record KE sessions.
 For something like a problem-solving interview,
one would wish to videotape it as well.
 However, KEs should be aware of the costs this
involves, in time and money - it can take as much
as 15 hours of secretarial time to transcribe and
edit a one-hour interview.

37. Knowledge analysis &
representation
 Simultaneously with the knowledge acquisition
process, a knowledge analysis process takes
place. The KE uses the data - the transcripts and
protocols, etc - from the knowledge acquisition
sessions to build a good model of the
expertise that the DE is using to solve problems
in the domain.

38. Knowledge analysis &
representation
 The raw data is converted into intermediate
representations. These are structured
representations of the knowledge, but not yet the
sort of coded knowledge that can be put into the
knowledge base.
 This will improve the knowledge engineer's
understanding of the subject;

39. Knowledge analysis &
representation
 This will probably provide knowledge in a form that
can be shown to the DE, for criticism and
correction;
 This provides easily-accessible knowledge for
future KEs to work from (knowledge archiving).
 The intermediate representation is then converted
into the knowledge representation formalism which
is to be used in the KBS software.

40. Knowledge validation
 It is necessary to verify the knowledge against the
knowledge source (the expert or document).
 It is also necessary to validate the knowledge
against known outcomes.
 The objective is to produce knowledge of high
integrity.

41. Computer-assisted knowledge
elicitation
 Since knowledge engineering skills, and
hence knowledge engineers, are rare
(see appendix), it would be desirable to
automate the job.
 i.e. to write an expert system to do
knowledge engineering.

42. Computer-assisted knowledge
elicitation
 The state of the art in AI (especially in
natural language processing) is not
sufficiently advanced to permit fully-
automated knowledge elicitation.

43. Computer-assisted knowledge
elicitation
 However, 'knowledge elicitation workbenches', or
'knowledge engineering environments', are
commercially available
 e.g. KEE, KnAcqTools, ETS, KRITON, AQUINAS;
 their principle use is to simplify the task of
converting a protocol into frames, rules, etc., and
inserting these structures into an expert system
shell as soon as they are formulated.

44. Fully computerised knowledge
acquisition
 It might be thought that one could avoid
using a domain expert altogether, by
building a system that could extract
knowledge, given facts about the domain.
♦ This is the approach taken by machine
learning systems:
♦ "classic" machine learning systems such as ID3
(Quinlan, 1979) & AQ11 (Michalski & Chilauski,
1980);

45. Fully computerised knowledge
acquisition
 systems designed to provide knowledge for a
particular system's knowledge base, e.g. META-
DENDRAL, designed to discover rules for the
rule-base in DENDRAL;
 data mining systems; these do a similar job to
classic machine learning systems, but work on
a very large database of information.
 sub-symbolic systems, i.e. neural nets and
genetic algorithms. More about these in the last
lecture in this course.

46. Fully computerised knowledge
acquisition
 There are plenty of examples of machine
learning systems producing formerly-
unknown knowledge, and knowledge that
was better than that of a domain expert