Knowledge Base Systems

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Knowledge Base Systems

• production rule representation.

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Production Systems

• Introduction
• These are a form of knowledge representation
  which have found widespread application in AI
  particularly in the area of Expert Systems.
• Production systems consist of three parts
• a) A rule base consisting of a set of production
  rules.
• b) the data
• c) an interpreter which controls the systems
  activity.

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Production Rules

• A production rule can be thought of as a
  condition action pair. They take the form
• IF condition holds THEN do action e.g.
• IF traffic light is red THEN stop car.

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Firing Production Rules

• A production rule whose conditions are satisfied
  can fire, i.e. the associated actions can be
  performed.
• Conditions are satisfied or not according to what
  data is currently available.
• Existing data may be modified as production rules
  are fired. Changes in data can lead to new
  conditions being satisfied.
• New production rules may then be fired.

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• The decision which production rule to fire next
  is taken by a program known as the interpreter.
• The interpreter therefore controls the systems
  decisions and actions and must know how to do
  this.

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Production Systems

• Production systems were first proposed in 1943 by
  Post.
• Present day systems however bear little
  resemblance to those earlier ones.
• Newell and Simon in 1972 used the idea of
  production systems for their models of human
  cognition and AI in general has found widespread
  use for them.

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Suitable Domains

• Davis and King (1977) proposed the following
  criteria for problem domains where productions
  rules are suitable.
• 1 Domains in which knowledge is diffuse,
  consisting of many different strands of knowledge
  rather than some domain which is based on a
  unified theory like Physics.
• 2 Domains in which processes cane be represented
  as a set of independent actions as opposed to
  domains with dependent subprocesses.
• 3 Domains in which knowledge can be easily
  separated from the manner in which it is to be
  used as opposed to cases where representation and
  control are merged.

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Knowledge Base Systems

• Knowledge Base systems are intended to perform
  tasks which require some specialized knowledge
  and reasoning.
• Medical diagnosis, geological analysis, and
  chemical compound identification are examples of
  tasks to which Knowledge Base systems have been
  applied.

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Expert Systems

• Knowledge Base systems are often called expert
  systems because the problems in their application
  domain are usually solved by human experts.
• For example medical diagnosis is usually
  performed by a doctor.

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Parts of a Knowledge Base System

• Knowledge Base systems consist of Four major
  parts
• The Knowledge Base,
• The Inference Engine
• The User Interface and
• The Explainer
• Knowledge Acquisition Module

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Inference Engine
Knowledge Base (rules)
Working Memory (facts)
Agenda
Explanation Facility
Knowledge Acquisition Facility
User Interface
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• The knowledge to which the Knowledge Base system
  has access, is stored in the Knowledge Base,
  (hence the name).
• The Inference Engine is the part of a Knowledge
  Base system which is responsible for using its
  knowledge in a productive way.
• The Knowledge Base system's reasoning mechanisms
  are built into the Inference Engine. Most
  Knowledge Base systems employ deductive reasoning
  mechanisms.

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• The Knowledge Base system communicates with the
  user through the User Interface.
• In many applications the Knowledge Base system is
  required to explain its reasoning to the user.
  This is particularly true in situations such as
  the identification of chemical structures where
  new results must be verified.
• The Explainer is that part of the Expert System
  which provides explanation and verification

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• Knowledge Acquisition Modules
• These help with acquiring the systems knowledge.
• They do a variety of tasks for example in some
  systems they provide system analysis facilities
  similar to those of database development tools
• Mostly they help encode the knowledge from a high
  level format into a computer usable
  representation.

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System Architecture
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Inference Engine
Knowledge Base (rules)
Working Memory (facts)
Agenda
Explanation Facility
Knowledge Acquisition Facility
User Interface
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• Obviously the knowledge to which a Knowledge Base
  system has access is no good to it, if it cannot
  use it to solve the problems in the application
  domain.
• Therefore the systems knowledge must be
  represented in a form which can be manipulated by
  the reasoning mechanisms of the Inference Engine.
  

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• While the Knowledge Base, the IE and the User
  Interface are essential components, in many
  Knowledge Base systems there are also facilities
  to help in the acquisition of new knowledge.
• Teiresias, DAVIS '76, which is used in
  association with MYCIN, Shortliffe '76, Davis
  '76 is an example of such a system.
• It elicits high level information from the user
  which it converts into structured knowledge for
  its Knowledge Base. It also performs consistency
  checks on the updated knowledge base.
• The ability to acquire new knowledge is important
  since the amount of knowledge to which a system
  has access determines the range of problems which
  it can solve.

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Applications of Knowledge Base systems

• Knowledge Base systems have been applied to many
  diverse problem domains, such as the following.
• Diagnostic Aids such as MYCIN, Shortliffe '76,
  Davis '76, which diagnoses bacterial blood
  infections and PUFF, Kunz et al '78, which
  diagnose pulmonary disorders.
• MYCIN was a joint venture between Dept. of
  Computer Science and the Medical School of
  Stanford University.
• Much of the work took place in the 1970's.
• Mycin was designed to solve the problem of
  diagnosing and recommending treatments for
  meningitis and bacteremia, (blood infections).

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• Aids to Design and Manufacture such as R1,
  McDermott '82, which configures computers.
• Teaching Aids such as SCHOLAR Carbonell '70
  which gives Geography Tutorials and SOPHIE,
  Brown et al '82, which teaches how to detect
  breakdown in electrical circuits.
• Problem Solving

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• Recognition of forms, e.g. DENDRAL, Buchanan
  and Feigenbaum '78, Lindsay et al '80, which
  recognizes the structures of chemical compounds.
• Robotics e.g. SHDRLU, Winograd '73, which
  manipulates polygons in a restricted environment.
  
• Game playing systems such as Waterman's Poker
  Player, Waterman '70, and

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• Automatic theorem Provers such as AM, Lenat
  '82.
• Hayes-Roth et al '83, Handbook A.I. '82,
  Waterman '86, describe some more categories
  than those mentioned above. These include
  Planning systems such as NOAH, Sacerdoti '75
  and MOLGEN, Friedland '75 and
• Prediction systems such as Political Forecasting
  Systems, Schrodt '86 based on the Holland
  Classifier, Holland '86.

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Rule-Based Expert Systems

• Work with
• a set of facts describing the current world state
  
• a set of rules describing the expert knowledge
• inference mechanisms for combining facts and
  rules in reasoning

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Architecture of Rule-Based XPS 1

• Knowledge-Base / Rule-Base
• stores expert knowledge as condition-action-rules
  (or if-then- or premise-consequence-rules)
• objects or frame structures are often used to
  represent concepts in the domain of expertise,
  e.g. club in the golf domain.
• Working Memory
• stores initial facts and generated facts derived
  by the inference engine
• additional parameters like the degree of trust
  in the truth of a fact or a rule (? certainty
  factors) or probabilistic measurements can be
  added

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Knowledge Engineering

• Knowledge engineering is a general term for the
  processes involved in building expert systems
  planning, knowledge acquisition, system
  building,system installation, system maintenance.
• In the following notes "KE" stands for knowledge
  engineer, and "DE" stands for domain expert.

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Architecture of Rule-Based Systems

• Inference Engine
• matches condition-part of rules against facts
  stored in Working Memory (pattern matching)
• rules with satisfied condition are active rules
  and are placed on the agenda
• among the active rules on the agenda, one is
  selected (see conflict resolution, priorities of
  rules) as next rule for execution (firing)
  consequence of rule can add new facts to Working
  Memory, modify facts, retract facts, and more

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Rule-Based Systems- Example Grades -

• Rules to determine grade
• If study then get good_grade
• If do not_study then get bad_grade
• If sun_shines THEN go_out
• If go_out then do not_study
• If stay_home then study
• If awful_weather then stay_home

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Questions

• Ask the following questions of the expert System
• Q1 If the weather is awful, do you get a good or
  bad grade?
• Q2 When do you get a good grade?

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• forward reasoning rule chain
• given fact awful_weather 6,5,1
• backward reasoning
• hypothesis/goal good_grade 1,5,6
• Answer Question 1 Good Grade
• Exercise Answer question 2 with forward and
  backward chaining

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Explaining

• Note we presented a rule chain when we solved
  this problem.
• If we asked How do you know we get a good grade
  in awful weather we can say that
• By rule 6 if the weather is awful stay at home
• By rule 5 If stay at home then you will study
• And finally By rule 1 if you study you get a good
  grade

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In other words

• In order to explain how we arrive at a solution
  we list the chain of rules that were fired on
  rout to this conclusion.
• This is the basis of expert system explainers

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Knowledge Acquisition

• Obtaining knowledge for use in the knowledge base
  of an expert system.

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Sources of knowledge

• Documents textbooks, journal articles, technical
  reports, 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.

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Knowledge Analysis

• Simultaneously with the knowledge acquisition
  process, a knowledge analysis process takes
  place.
• The KE uses the data from the knowledge
  acquisition sessions to build a good model of the
  expertise that the DE is using to solve problems
  in the domain. This may or not rely heavily on
  building a prototype - see below.

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Knowledge Elicitation

• The most important branch 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. This is the
  principle reason why expert systems have not
  become more widespread - the knowledge
  elicitation bottleneck.
• It is necessary to find out what the expert(s)
  know, and how they use their knowledge..

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Human experts

• Expert knowledge includes
• domain-related facts principles
• modes of reasoning
• reasoning strategies
• explanations and justifications.

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Knowledge elicitation and experts

• The knowledge elicitation (and 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.
• Repeated interviews with the expert(s), plus task
  analysis, concept sorting, etc, etc..
• Knowledge structuring converting the raw data
  (taken from the expert) into intermediate
  representations, prior to building a working
  system.

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Knowledge Elicitation continued

• Building a model of the knowledge derived from
  the expert, for the expert to criticise. From
  then on, the development proceeds by stepwise
  refinement.
• 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" (c.f. a compiled
  computer program fast efficient, but
  unreadable).

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Techniques used in Knowledge Elicitation

• Various different forms of interview
• Unstructured. A general discussion of the domain,
  designed to provide a list of topics and
  concepts.
• Structured. Concerned with a particular concept
  within the domain.
• Problem-solving.
• The expert 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.

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• Think-aloud. As above, but the expert 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.
• Dialogue.
• The expert interacts with a client, in the way
  that they would normally do during their normal
  work routine.

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Review.

• It is standard practice to tape record KE
  sessions. However, KEs should be aware of the
  costs this involves, in time and money.
• The KE and DE examine the record of on of the
  sessions described above, together.

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More Methods

• Sample lecture preparation. The expert prepares a
  lecture, and the KE analyses its content.
• Concept sorting ("card sort").
• Questionnaires. Especially useful when the
  knowledge is to be elicited from several
  different experts.
• Repertory grid (particularly the "laddered grid"
  technique).

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Computerised knowledge elicitation.

• The state of the art in AI (especially NLP) is
  not sufficiently advanced to permit
  fully-automated knowledge elicitation.
• However, 'knowledge elicitation workbenches', or
  'knowledge engineering environments', are
  commercially available (e.g KEE, KnAcqTools)
  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.