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Explanation-based Learning and New Ideas About AI Yin Wang, Shiliang Sun, Naveed

Explanation-based Learning and New Ideas About AI Yin Wang, Shiliang Sun, Naveed

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Page 1: Explanation-based Learning and New Ideas About AI Yin Wang, Shiliang Sun, Naveed

Explanation-based Learning and New Ideas About AI

Yin Wang, Shiliang Sun, Naveed

Page 2: Explanation-based Learning and New Ideas About AI Yin Wang, Shiliang Sun, Naveed

Prolog-EBG---Evaluation

Prolog-EBG can be viewed as an enhanced version of Find-SBoth consider only positive examplesEvery hypothesis in Find-S can be

expressed by a conjunction of clauses in Prolog-EBG

Page 3: Explanation-based Learning and New Ideas About AI Yin Wang, Shiliang Sun, Naveed

Analog

Find-S : Candidate-Elimination

= Prolog-EBG : ?

generalize ?Version Space

How can the negative examples

be useful ?

Page 4: Explanation-based Learning and New Ideas About AI Yin Wang, Shiliang Sun, Naveed

Knowledge-level Learning

IF ((PlayTennis = Yes) (Humidity = x)) THEN ((PlayTennis = Yes) (Humidity <= x ))

Example: (Humidity = .30 and PlayTennis = Yes)

New hypothesis: (PlayTennis = Yes) (Humidity <= 0.30 )

Input of example into domain theory by means of Abstraction (Here the variable is x)

What type of logic is this clause?

Page 5: Explanation-based Learning and New Ideas About AI Yin Wang, Shiliang Sun, Naveed

Alternative Preimage Structures

The space of preimage will become large for some problems

The need for fast rule-matching algorithms and new representations

Can the rules be represented as a hierachical structure which goes down only into certain level footimage ?

Page 6: Explanation-based Learning and New Ideas About AI Yin Wang, Shiliang Sun, Naveed

SafeToStack(x,y) Volume(x) * Density(x) < 5 and Type(y, Endtable)Too specific ?Is SafeToStack(x,y) Weight(x) < Weight(y) enough ? (We have a balance?)Restrict the reasoning in a reasonable level. Don’t go too much into details !

ExampleHow should the

rules be structured for matching ?

Page 7: Explanation-based Learning and New Ideas About AI Yin Wang, Shiliang Sun, Naveed

Evaluation of The Second Paper

Can be useful for understanding human intelligence

Maybe useful for AI in the future

GOD is a far better engineer than us

Can mechanical things have intelligence? Philosophy or religion ?

Page 8: Explanation-based Learning and New Ideas About AI Yin Wang, Shiliang Sun, Naveed

Symbol vs. Concept

Symbol is NOT Concept

Concept involves more than symbols Sporadic memory of sensory signals (How to

represent them ?) Personal history of the concept ( non-intentional

memory? People remember much more than symbols ! )

Without feelings and non-intentional memory, there will be no true intelligence

Intuition ?

Page 9: Explanation-based Learning and New Ideas About AI Yin Wang, Shiliang Sun, Naveed

Muscle Memory

Memory in the motor system is not restricted in the brainMotor system of the machine should have something like the muscle memory, rather than all computed by the CPUDancingMartial-arts…

Page 10: Explanation-based Learning and New Ideas About AI Yin Wang, Shiliang Sun, Naveed

The Right-side Brain

How can a machine imitate the parallel processing of the right side brain ?

Need a restructurable processor ?

Page 11: Explanation-based Learning and New Ideas About AI Yin Wang, Shiliang Sun, Naveed

Emotional Thinking

All previous models are based on rational thinkingTrue living human have more I Think I Feel I Desire

Let the learning process be desire-driven !

How can a machine have desire ?

What is desire ?

Give the Machine the

desire for truth?

Page 12: Explanation-based Learning and New Ideas About AI Yin Wang, Shiliang Sun, Naveed

More Mental Abilities

Sympathy

Imagination

Creativity