1. Generic Tasks In the 80’s, 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 expert’s 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 expert’s 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.