Generic Tasks

1
Generic Tasks

• In the 80s, KB engineering used approaches like
  this

Rules Logic Heurist.
Expert solving the problem
Knowledge Based System
knowl eng.
result
2
The Problem

• What if the experts approach did not fit the
  rules, logic or heuristic paradigm?
• Knowledge Engineers forced the knowledge into one
  of these forms, appropriately or not
• OR, Starting from scracth, the KE would build
  their system out of Lisp or Prolog and construct
  the system in a free-form way
• The experts approach and knowledge may be more
  suitable for another approach
• What approach?

3
Task Decomposition

• Similar to top-down design

High Level Tasks diagnosis, planning Mid
Level Tasks classification, recognition Low
Level Tasks rules, heuristics,
matching Domain Knowledge
4
What Level is Appropriate?

• Low levels are very primitive, offer flexibility
  but no structure (e.g. compare assembly language
  to Pascal)
• High levels offer structure but no flexibility
  (e.g. write a merge sort using Quattro Pro)
• Middle levels are a nice compromise which offer
  some structure and some flexibility

5
Similarities in problem solving

• All diagnostic problems have similar procedural
  approaches whether medical, mechanical,
  electrical or debugging
• Many planning problems have similar procedural
  approaches whether linear, non-linear,
  hierarchical, routine, or reactive
• Many interpretation problems are like diagnostic
  problems - recognition tasks

6
Generic Tasks

• Information Processing Strategies
• Functionally Defined - tells us how this task
  might fit in with other tasks
• What is the input?
• What is the output?
• Implied Method(s)
• Tells us what knowledge is needed to solve the
  problem for knowledge acquisition
• Helps for automated explanations

7
Example Diagnosis

• Malfunction hierarchy
• Rule-out knowledge
• Associational knowledge
• Differential knowledge
• Evaluation knowledge
• Test-ordering knowledge
• Refinement knowledge

8
Example Design/Planning

• Device/Component interactions
• Design plans
• Preferences
• Adjustment/Failure-handling knowledge

9
Generic Tasks

• Hierarchical Classification (HC)
• Routine Recognition/Hypothesis Matching (RR)
• State Abstraction (FR)
• Plan Selection/Refinement (RD)
• Data Inferencing (DI)
• Abductive Assembly (AA)

10
Problems Solved with GTs

• Diagnosis (HC, RR, possibly DI, AA)
• Design (RD, possibly FR)
• Decision Making (RD, or HC/RR/AA)
• Interpretation of Data (RR, AA, possibly HC, DI)
• Discovery (FR, AA)
• Prediction (FR, RR)
• Program Debugging (HC, DI, RR)
• Perception (RR, AA, possibly DI, HC)

11
Problems with GTs

• Control issues
• Learning with GTs
• Need for Deep knowledge
• Need for Common Sense knowledge
• How to perform explanations
• What about problems not listed on the previous
  slide? Can any problem be solved in this way?

12
What is Intelligence?
Neural Matchers Hierarchies GTs
Problems Beliefs Nets
Concepts General- low
feature concept task (diagnosis,
izations level rec. rec. level
planning, rec. processes etc...)
Learning takes place at each level with a
general upward flow as we generalize and learn
more complex things.