For Friday

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# For Friday - PowerPoint PPT Presentation

For Friday. Read chapter 22 Program 4 due. Program 4. Any questions?. Learning mini-project. Worth 2 homeworks Due Monday Foil6 is available in /home/ mecalif /public/itk340/foil A manual and sample data files are there as well.

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For Friday
• Program 4 due
Program 4
• Any questions?
Learning mini-project
• Worth 2 homeworks
• Due Monday
• Foil6 is available in /home/mecalif/public/itk340/foil
• A manual and sample data files are there as well.
• Create a data file that will allow FOIL to learn rules for a sister/2 relation from background relations of parent/2, male/1, and female/1. You can look in the prolog folder of my 327 folder for sample data if you like.
• Electronically submit your data file—which should be named sister.d, and turn in a hard copy of the rules FOIL learns.
Strategies for Learning a Single Rule
• Top­Down (General to Specific):
• Repeatedly add feature constraints that eliminate negatives while retaining positives.
• Stop when only positives are covered.
• Bottom­Up (Specific to General):
• Start with a most specific rule (complete description of a single instance).
• Repeatedly eliminate feature constraints in order to cover more positive examples.
• Stop when further generalization results in covering negatives.
FOIL
• Basic top­down sequential covering algorithm adapted for Prolog clauses.
• Background provided extensionally.
• Initialize clause for target predicate P to

P(X1 ,...Xr ) :­ .

• Possible specializations of a clause include adding all possible literals:
• Qi (V1 ,...Vr )
• not(Qi (V1 ,...Vr ))
• Xi = Xj
• not(Xi = X )

where X's are variables in the existing clause, at least one of V1 ,...Vr is an existing variable, others can be new.

• Allow recursive literals if not cause infinite regress.
Foil Input Data
• Consider example of finding a path in a directed acyclic graph.
• Intended Clause:

path(X,Y) :­ edge(X,Y).

path(X,Y) :­ edge(X,Z), path (Z,Y).

• Examples

edge: { <1,2>, <1,3>, <3,6>, <4,2>, <4,6>, <6,5> }

path: { <1,2>, <1,3>, <1,6>, <1,5>, <3,6>, <3, 5>, <4,2>, <4,6>, <4,5>, <6, 5> }

• Negative examples of the target predicate can be provided directly or indirectly produced using a closed world assumption. Every pair <x,y> not in positive tuples for path.
Example Induction

+ : { <1,2>, <1,3>, <1,6>, <1,5>, <3,6>, <3, 5>, <4,2>,<4,6>, <4,5>, <6, 5> }

- : {<1,4>, <2,1>, <2,3>, <2,4>, <2,5> <2,6>, <3,1>, <3,2>, <3,4>, <4,1> <4,3>, <5,1>, <5,2>, <5,3>, <5,4> <5,6>, <6,1>, <6,2>, <6,3>, <6,4> }

• Among others, consider adding literal edge(X,Y) (also consider edge(Y,X), edge(X,Z), edge(Z,X), path(Y,X), path(X,Z), path(Z,X), X=Y, and negations)
• 6 positive tuples and NO negative tuples covered.
• Create “base case” and remove covered examples:

path(X,Y) :­ edge(X,Y).

+ : { <1,6>, <1,5>, <3, 5>, <4,5> }

- : { <1,4>, <2,1>, <2,3>, <2,4>, <2,5> <2,6>, <3,1>, <3,2>, <3,4>, <4,1>,<4,3>, <5,1>, <5,2>, <5,3>, <5,4> <5,6>, <6,1>, <6,2>, <6,3>, <6,4> }

• Consider literal edge(X,Z) (among others...)
• 4 remaining positives satisfy it but so do 10 of 20 negatives
• Current rule: path(x,y) :­ edge(X,Z).
• Consider literal path(Z,Y) (as well as edge(X,Y), edge(Y,Z), edge(X,Z), path(Z,X), etc....)
• No negatives covered, complete clause.

path(X,Y) :­ edge(X,Z), path(Z,Y).

• New clause actually covers all remaining positive tuples of path, so definition is complete.
Picking the Best Literal
• Based on information gain (similar to ID3).

|p|*(log2 (|p| /(|p|+|n|)) - log2 (|P| /(|P|+|N|)))

P is number of positives before adding literal L

N is number of negatives before adding literal L

p is number of positives after adding literal L

n is number of negatives after adding literal L

• Given n predicates of arity m there are O(n2m) possible literals to chose from, so branching factor can be quite large.
Other Approaches
• Golem
• CHILL
• Foidl
• Bufoidl
Domains
• Any kind of concept learning where background knowledge is useful.
• Natural Language Processing
• Planning
• Chemistry and biology
• DNA
• Protein structure
Natural Language Processing
• What’s the goal?
Communication
• Communication for the speaker:
• Intention: Decided why, when, and what information should be transmitted. May require planning and reasoning about agents' goals and beliefs.
• Generation: Translating the information to be communicated into a string of words.
• Synthesis: Output of string in desired modality, e.g.text on a screen or speech.
Communication (cont.)
• Communication for the hearer:
• Perception: Mapping input modality to a string of words, e.g. optical character recognition or speech recognition.
• Analysis: Determining the information content of the string.
• Syntactic interpretation (parsing): Find correct parse tree showing the phrase structure
• Semantic interpretation: Extract (literal) meaning of the string in some representation, e.g. FOPC.
• Pragmatic interpretation: Consider effect of overall context on the meaning of the sentence
• Incorporation: Decide whether or not to believe the content of the string and add it to the KB.
Ambiguity
• Natural language sentences are highly ambiguous and must be disambiguated.

I saw the man on the hill with the telescope.

I saw the Grand Canyon flying to LA.

I saw a jet flying to LA.

Time flies like an arrow.

Horse flies like a sugar cube.

Time runners like a coach.

Time cars like a Porsche.

Syntax
• Syntax concerns the proper ordering of words and its effect on meaning.

The dog bit the boy.

The boy bit the dog.

* Bit boy the dog the

Colorless green ideas sleep furiously.

Semantics
• Semantics concerns of meaning of words, phrases, and sentences. Generally restricted to “literal meaning”
• “plant” as a photosynthetic organism
• “plant” as a manufacturing facility
• “plant” as the act of sowing
Pragmatics
• Pragmatics concerns the overall commuinicative and social context and its effect on interpretation.
• Can you pass the salt?
• Passerby: Does your dog bite?

Clouseau: No.

Passerby: (pets dog) Chomp!

I thought you said your dog didn't bite!!

Clouseau:That, sir, is not my dog!

Modular Processing

Speech recognition

Parsing

acoustic/ phonetic

syntax

semantics

pragmatics

Sound waves

words

Parse trees

literal meaning

meaning

Examples
• Phonetics

“grey twine” vs. “great wine”

“youth in Asia” vs. “euthanasia”

“yawanna” ­> “do you want to”

• Syntax

I ate spaghetti with a fork.

I ate spaghetti with meatballs.

More Examples
• Semantics

I put the plant in the window.

Ford put the plant in Mexico.

The dog is in the pen.

The ink is in the pen.

• Pragmatics

The ham sandwich wants another beer.

John thinks vanilla.