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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
For Friday
  • Read chapter 22
  • Program 4 due
program 4
Program 4
  • Any questions?
learning mini project
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
Strategies for Learning a Single Rule
  • Top­Down (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.
  • 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.
slide5
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
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
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).

slide8
+ : { <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
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
Other Approaches
  • Golem
  • CHILL
  • Foidl
  • Bufoidl
domains
Domains
  • Any kind of concept learning where background knowledge is useful.
  • Natural Language Processing
  • Planning
  • Chemistry and biology
    • DNA
    • Protein structure
natural language processing
Natural Language Processing
  • What’s the goal?
communication
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 (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
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
  • 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
  • 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
  • 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
Modular Processing

Speech recognition

Parsing

acoustic/ phonetic

syntax

semantics

pragmatics

Sound waves

words

Parse trees

literal meaning

meaning

examples
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
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.