CSCI3406 Fuzzy Logic and Knowledge Based Systems AI

1
CSCI3406 Fuzzy Logic and Knowledge Based Systems
(AI)
Knowledge Acquisition (KA) II
2
Introduction

• In this lecture we cover the actual process of
  Knowledge Acquisition (KA) from preliminary work
  to automated KA systems.

3
Topics of Discussion

• Preliminary work
• Knowledge Handbook
• KA techniques

4
Knowledge acquisition involves elicitation,
analysis, modelling and validation of knowledge

• Employing a technique to elicit data (usually
  verbal) from the expert.
• Interpreting these verbal data (more or less
  skilfully) in order to infer what might be the
  expert's underlying knowledge and reasoning
  process.
• Using this interpretation to guide the
  construction of some model or language that
  describes (more or less accurately) the expert's
  knowledge and performance.
• Interpretation of further data is guided in turn
  by this evolving model.
• The principle focus for the knowledge acquisition
  team should be in constructing models, in domain
  definition, or problem identification and problem
  analysis.
• Ref Johnson, L. Johnson, N.E., Knowledge
  Elicitation Involving Teachback Interviewing in
  Kidd in Knowledge Acquisition for Expert
  Systems A Practical Handbook, 1987

5
Roles for knowledge acquisition

• Knowledge engineering and management
  technological innovation, ontology construction,
  document mark-up
• AI systems development generic methodologies
  (e.g., KADS KADS stands for Knowledge Analysis
  and Documentation System''. Later on, other
  interpretations have been given to this acronym,
  such as Knowledge Analysis and Design
  Support'. KADS is the name of a structured
  methodology for the development of knowledge
  based systems that is now in practical use in
  many places in Europe and elsewhere.)
• Organizational analysis process approaches
• Task analysis job design
• User analysis generation of cognitive
  specifications for tasks, the mitigation of human
  error in domains of risk or time pressure, the
  enhancement of proficiency through training and
  skill remediation
• Requirements elicitation systems or design
  analysis, conceptual database design, software
  requirements definition

6
Preliminary Work - I

• Preliminary work is carried out by knowledge
  engineer(s)
• Knowledge engineering is knowledge acquisition
  for expert system development, and used to
  describe the reduction of a large body of
  knowledge to a precise set of facts and rules
  (Ref Feigenbaum, E.A. (1980). Knowledge
  Engineering the Applied Side of Artificial
  Intelligence. Report STAN-CS-80-812. Department
  of Computer Science, Stanford University)
• Knowledge engineer is a computer software
  programmer who gathers knowledge from experts and
  then translates the knowledge into the knowledge
  base of a computerised expert system in a
  structured and logical way, and eventually
  constructs computerised expert systems.
• "Knowledge acquisition is a bottleneck in the
  construction of expert systems. The knowledge
  engineer's job is to act as a go-between to help
  an expert build a system. Since the knowledge
  engineer has far less knowledge of the domain
  than the expert, however, communication problems
  impede the process of transferring expertise into
  a program. The vocabulary initially used by the
  expert to talk about the domain with a novice is
  often inadequate for problem-solving thus the
  knowledge engineer and expert must work together
  to extend and refine it. One of the most
  difficult aspects of the knowledge engineer's
  task is helping the expert to structure the
  domain knowledge, to identify and formalize the
  domain concepts." (Ref Hayes-Roth, F., Waterman,
  D.A. Lenat, D.B., Eds. (1983). Building Expert
  Systems. Reading, Massachusetts Addison-Wesley)

7
Preliminary Work - II

• When acquiring knowledge about a domain it is
  absolutely crucial that the knowledge engineer
  can converse with the expert using the expert
  terminology.
• The knowledge engineer has to have a good grasp
  of the domain to be able to ask intelligent
  questions to extract important and relevant
  knowledge from the experts who have vast amounts
  of knowledge a lot of which is tacit knowledge.
• The knowledge engineer must therefore do some
  preliminary work including research on the domain
  in question before the first interview with the
  expert takes place.
• Some requirements for KA Techniques
• Take experts off the job for short time periods
• Allow non-experts to understand the knowledge
• Focus on the essential knowledge
• Try to capture tacit knowledge
• Allow knowledge to be collated from different
  experts
• Allow knowledge to be validated and maintained

8
Preliminary Work

• Reading
• Observation
• Discussion

9
The Knowledge Handbook

• One of the functions of the knowledge engineer
  during the knowledge acquisition phase is to
  document the knowledge that has been acquired.
  One idea suggested (Wolfgram et. al. 1987 and
  others) is that of building a knowledge handbook.
  
• Wolfgram et. al. describe the contents of the
  knowledge handbook as follows
• The general problem description.
• Who the users are and their expectations from the
  system.
• A breakdown of the problems into sub-problems and
  sub-domains for future knowledge acquisition.

10
The Knowledge Handbook

• A detailed description of the domain or
  sub-domain to be used for the prototype.
• A bibliography of reference documents.
• A list of vocabulary, concepts, terms, phrases
  and acronyms in the domain.
• A list of experts for the prototype.
• Some reasonable performance standards for the
  system, based on consultation with the experts
  and users.
• Descriptions of typical reasoning scenarios
  gained from the knowledge acquisition.

11
Basic knowledge engineering for knowledge
acquisition - I

• Knowledge engineer act as a go-between the
  expert and knowledge base. This can be achieved
  by means of eliciting knowledge from the expert,
  encoding it for the knowledge base, and refining
  it in collaboration with the expert in order to
  achieve acceptable performance. The process is
  basically as follows
• The knowledge engineer interviews the expert to
  elicit his or her knowledge
• The knowledge engineer encodes the elicited
  knowledge for the knowledge base
• The shell uses the knowledge base to make
  inferences about particular cases specified by
  clients
• The clients use the shell's inferences to obtain
  advice about particular cases
• Ref http//ksi.cpsc.ucalgary.ca/articles/KBS/KSS0
  /

12
Basic knowledge engineering for knowledge
acquisition - II

• Basic knowledge engineering model with manual
  acquisition of knowledge from an expert
  (left-hand side of the figure). This is also
  followed by interactive application of the
  knowledge with multiple clients through an expert
  system shell (right-hand side of the figure).
• Ref http//ksi.cpsc.ucalgary.ca/articles/KBS/KSS0
  /

13
Interactive Knowledge Engineering for Interactive
Knowledge Acquisition -I

• In an interactive knowledge engineering process
  for interactive knowledge acquisition, knowledge
  engineers have responsibility for
• Advising the experts on the process of
  interactive knowledge elicitation
• Managing the interactive knowledge acquisition
  tools, setting them up appropriately
• Editing the unencoded knowledge base in
  collaboration with the experts
• Managing the knowledge encoding tools, setting
  them up appropriately
• Editing the encoded knowledge base in
  collaboration with the experts
• Validating the application of the knowledge base
  in collaboration with the experts
• Setting up the user interface in collaboration
  with the experts and clients
• Training the clients in the effective use of the
  knowledge base in collaboration with the expert
  by developing operational and training procedures.

14
Interactive Knowledge Engineering for Interactive
knowledge acquisition -II
15
Interactive Knowledge Engineering for Interactive
Knowledge Acquisition -III

• Interactive knowledge acquisition and encoding
  tools can greatly reduce the need for the
  knowledge engineer to act as an intermediary but,
  in most applications, they leave a substantial
  role for the knowledge engineer.
• This use of interactive elicitation can be
  combined with manual elicitation and with the use
  of the interactive tools by knowledge engineers
  rather than, or in addition to, experts.
  Knowledge engineers can directly elicit knowledge
  from the expert and use the interactive
  elicitation tools to enter knowledge into the
  knowledge base.
• Such approach is very useful and effective as it
  allows use of
• Multiple knowledge engineers since the tasks may
  require the effort of more than one person, and
  some specialization may be appropriate
• Multiple experts since one person (expert) should
  not be expected to have all the knowledge
  required, and, even if such an expert exists,
  comparative elicitation from multiple experts is
  itself a valuable knowledge elicitation technique
• Validation process, which is a key to an
  effective and successful system development

16
KA Techniques

• There have been many techniques developed to
  help elicit knowledge from expert(s). These are
  referred to as knowledge elicitation or knowledge
  acquisition (KA) techniques.
• Some of KA techniques are
• Interviews-General (Focused and Structured
  Interview)
• Observation
• Protocol Analysis
• Walkthroughs
• Repertory Grids
• Computer aided Knowledge Acquisition
• Automated Rule Induction

17
Another way of listing principle KA techniques
I(see http//www.epistemics.co.uk/Notes/63-0-0.ht
m for further details)

• 1-Protocol-generation techniques include various
  types of interviews (unstructured,
  semi-structured and structured), reporting
  techniques (such as self-report and shadowing)
  and observational techniques
• 2-Protocol analysis techniques are used with
  transcripts of interviews or other text-based
  information to identify various types of
  knowledge, such as goals, decisions,
  relationships and attributes. This acts as a
  bridge between the use of protocol-based
  techniques and knowledge modelling techniques.
• 3-Hierarchy-generation techniques, such as
  laddering, are used to build taxonomies or other
  hierarchical structures such as goal trees and
  decision networks.
• Laddering techniques involve the creation,
  reviewing and modification of hierarchical
  knowledge, often in the form of ladders (i.e.
  tree diagrams).
• Various forms of ladders have been used Concept,
  Attribute, Composition and Process ladders.

18
Another way of listing principle KA techniques -
II

• 4-Matrix-based techniques involve the
  construction of grids indicating such things as
  problems encountered against possible solutions.
  Important types include the use of frames for
  representing the properties of concepts and the
  repertory grid technique used to elicit, rate,
  analyse and categorise the properties of
  concepts.
• 5-Sorting techniques are used for capturing the
  way people compare and order concepts, and can
  lead to the revelation of knowledge about
  classes, properties and priorities.
• 6-Limited-information and constrained-processing
  tasks are techniques that either limit the time
  and/or information available to the expert when
  performing tasks. For instance, the
  twenty-questions technique provides an efficient
  way of accessing the key information in a domain
  in a prioritised order.
• 7-Diagram-based techniques include the generation
  and use of concept maps, state transition
  networks, event diagrams and process maps. The
  use of these is particularly important in
  capturing the "what, how, when, who and why" of
  tasks and events.

19
Comparison of KA Techniques
Ref http//www.epistemics.co.uk/Notes/63-0-0.htm
20
Typical Use of KA Techniques

• The followings can summarise general process for
  How and When the KA techniques are used in a
  knowledge acquisition project
• Conduct an initial interview with the expert in
  order to (a) scope what knowledge is to be
  acquired, (b) determine what purpose the
  knowledge is to be put, (c) gain some
  understanding of key terminology, and (d) build a
  rapport with the expert. This interview (as with
  all session with experts) is recorded on either
  audiotape or videotape.
• Transcribe the initial interview and analyse the
  resulting protocol. Create a concept ladder of
  the resulting knowledge to provide a broad
  representation of the knowledge in the domain.
  Use the ladder to produce a set of questions
  which cover the essential issues across the
  domain and which serve the goals of the knowledge
  acquisition project.
• Conduct a semi-structured interview with the
  expert using the pre-prepared questions to
  provide structure and focus.
• Transcribe the semi-structured interview and
  analyse the resulting protocol for the knowledge
  types present. Typically these would be concepts,
  attributes, values, relationships, tasks and
  rules.
• Represent these knowledge elements using the most
  appropriate knowledge models, e.g. ladders,
  grids, network diagrams, hypertext, etc. In
  addition, document anecdotes, illustrations and
  explanations in a structured manner using
  hypertext and template headings.
• Use the resulting knowledge models and structured
  text with contrived techniques such as laddering,
  think aloud problem-solving, twenty questions and
  repertory grid to allow the expert to modify and
  expand on the knowledge already captured.
• Repeat the analysis, model building and
  acquisition sessions until the expert and
  knowledge engineer are happy that the goals of
  the project have been realised.
• Validate the knowledge acquired with other
  experts, and make modifications wherever
  appropriate and necessary.
• Aggregate knowledge collected from all experts
  if/wherever/whenever appropriate

This part is mostly adapted from
http//www.epistemics.co.uk/Notes/63-0-0.htm