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.• 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 • Top Down (General to Specific):

– Start with the most general (empty) rule. – Repeatedly add feature constraints that eliminate

negatives while retaining positives. – Stop when only positives are covered.

• Bottom Up (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 top down 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).

• Examplesedge: { <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> }

• Start with empty rule: path(X,Y) : . • 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> }

• Start with new empty rule: path(X,Y) : . • 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

acoustic/ phonetic syntax semantics pragmatic

s

Speech recognition Parsing

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.