AI

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AI Week 21 Machine Learning Macro Learning

• Lee McCluskey, room 2/09
• Email lee_at_hud.ac.uk
• http//scom.hud.ac.uk/scomtlm/cha2555/

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Term 2 Summary

• 13 - Introduction to planning and learning
• 14 - Introduction to formulations/jargon of
  Planning
• 15 - Operator Schemas and state space search
• 16 - Planner Implementation BreadthFS in Prolog
• 17 - Planner Implementation BreadthFS, BestFS,
  Heuristics
• 18 - Graphplan Planning Alg
• 19 - Graphplan Planning Alg
• 20 reading week
• 21 Knowledge Acquisition GIPO
• 22 - Learning example 1 Macro Learning ?
  we are here
• 23 - Learning example 2 GIPO's Opmaker
• 24 - Learning example 3 Information Extraction

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Types of Learning

• Learning by ROTE (remember facts)
• - this is purely storing and remembering facts
  without integrating or recognising the meaning of
  the facts
• Learning by BEING TOLD (programmed)
• - this is storing and remembering facts /
  procedures, but implies some kind of
  understanding / integration of what is being
  told, with previous knowledge.
• Learning by EXAMPLE/ANALOGY (trained/taught)
• this invovles a benevolent teacher who gives
  classified examples to the leaner. The learner
  performs some generalisation the examples to
  infer new knowledge. Previous knowledge maybe
  used to steer the generalisations. In analogy the
  learner performs the generalisation based on some
  previously learnt situation.

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Types of Learning

• Learning by OBSERVATION (self-taught)
• this is similar to the category above but without
  classification by teacher - the learner uses
  pre-learned information to help classify
  observation (eg conceptual clustering)
• Learning by DISCOVERY
• this is the highest level of learning covering
  invention etc and is composed of some of the
  other types below
• TWO ASPECTS OF LEARNING
• KNOWLEDGE/SKILL ACQUISITION
• Inputting NEW knowledge
• KNOWLEDGE/SKILL REFINEMENT
• Changing/integrating old knowledge to create
  better (operational) knowledge (Inputs no or
  little new knowledge)

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Recap Knowledge Acquisition

• Knowledge Acquisition is the process of encoding
  knowledge in a way that intelligent processes can
  use effectively.
• ? Do we always need an AI expert to encode
  knowledge?
• ? Can we get programs to learn or acquire
  knowledge for themselves ?
• Next week we will see how GIPO can be used to
  learn new planning operators

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KNOWLEDGE/SKILL REFINEMENT

• Changing/integrating old knowledge to create
  better (operational) knowledge (Inputs no or
  little new knowledge)
• Examples
• Learning heuristics (improve search)
• Re-representing knowledge (improve search space)
• Learning procedures (remove search altogether!)
• Automatically removing bugs in representations

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KNOWLEDGE/SKILL REFINEMENT MACRO ACQUISITION
AND USE FOR AI PLANNING

• Roughly a planner solves a problem and induces
  one or more macros from the solution sequence by
  compiling the operator sequence into one macro.
• Definition
• WP (weakest precondition) of operator O to
  achieve goal(s) G
• WP(O,G) Elements of G that O does not achieve
  UNION Os preconditions
• Learning task
• Find a solution T (o(1),..,o(N)) to goal G from
  initial state s(0)
• Form a Macro- Operator (macro) based on
• Pre-condition WP (T, G)
• Post-condition G

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Macro acquisition algorithm

• Starting with goal G, we regress through the
  states backwards
• Assume we have the last operator o(N) applied to
  s(N-1) to form the final state s(N), with add
  list o(N).add and precondition o(N).pre
• Then regressing G through o(N) gives
• NewG WP(o(N),T) G o(N).add UNION with
  o(N).pre
• NewG can now be regressed further until the
  initial state is reached. The full regressed
  goal is the weakest precondition that T achieves
  the goal G.

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Macro Use

• The triple (G, WP(T,G), T) can be stored and
  retrieved
• Given a planning problem (S, G1),
• IF S gt WP(T,G) and G gt G1 then
• achieve G1 by applying sequence T to S.
• The stored macro (G, WP(T,G), T) can be further
  generalised by changing the constants to
  variables ranging through all objects of the same
  sort as the original constants. In OCL for
  example, as all objects of the same sort share
  the same behaviour, this generalisation has some
  justification
• The macro could increase future performance as it
  may cut out the need to search for a solution to
  G.

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Macro (Learning) Utility

• There are other kinds of macro creation for a
  solution of size N, N -1 macros can be created as
  each of the regressed goals can form a macro.
  This may cause utility problems, however.
• Too many or too general macros may
• Increase the search space
• Increase the time of searching as the planner
  spends time looking for macros

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Conclusion

• There are various kinds of Learning manifest in
  nature and in AI
• Two important roles for Learning are in Knowledge
  Acquisition and Knowledge Refinement
• Macro acquisition is a form of KR where
  procedures are learned to make plan generation
  more efficient
• Sometimes, Learned information can degrade
  performance