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Josefina Sierra Josefina Santibez

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Title: Josefina Sierra Josefina Santibez


1
Acquisition of Linguistic Competencefor
Communicating Propositional Logic Sentences
  • Josefina Sierra Josefina
    Santibáñez
  • Tech. Univ of Cataluña University of
    La Rioja
  • Spain
    Spain

2
  • Language Game Guessing (Steels 1999)
  • Speaker chooses a formula from a propositional
    language, generates a sentence for expressing the
    formula and communicates the sentence to hearer.
  • Hearer tries to interpret the sentence generated
    by the speaker. If it can parse it using its
    lexicon and grammar, it extracts a meaning.
    Success speakers formula ? hearers
    meaning
  • If not success, the speaker communicates the
    formula it had in mind to hearer. They adjust
    their grammars to become sucessful in future.

3
  • Goals of the Experiments
  • Observe the evolution of
  • The communicative success average of sucess-ful
    language games in the last ten games played by
    the agents.
  • The internal grammars constructed by the
    individual agents.
  • The external language used by the population.

4
  • Definite clause grammarsemantic, score, use
  • s(right, 0.25, 20) ? right
  • s(light, 0.70, 50) ? light
  • s(P,Q, S, 12) ? 1, c1(P,S1,C1), s(Q,S2,C2), S
    is S1?S2?0.01
  • c1(not, 0.80, 55) ? not
  • s(P,Q,R,S,3) ? 2, c2(P,S1,C1), s(Q,S2,C2), s(R,
    S3,C3), S is S1?S2?S3?0.01
  • c2(and, 0.50, 35) ? and
  • Formula Meaning
    Sentence
  • right ? light and, right, light
    rightandlight
  • light not, light
    notlight

5
  • Invention
  • Generates a sentence E for a meaning M
  • If M is atomic, it invents a new word E.
  • If M is a list, it tries to construct an
    expression for each of the elements in M using
    the agents grammar.
  • If it cannot construct an expression for an
    element using its grammar, it invents a new
    expression.
  • It concatenates the expressions associated with
    the elements of M randomly in order to construct
    a sen-tence E for the whole meaning M.
  • Adds a new rule to the grammar s(M, 0.01, 0) ?
    E

6
  • Adoption
  • Communication fails because
  • The hearer cannot parse the speakers sentence
  • The speaker communicates the formula it had in
    mind to the hearer.
  • The hearer adopts an association between that
    formula and the sentence used by the speaker.
  • s(M, 0.01, 0) ? E
  • The hearer can parse the sentence, but its
    interpre-tation is not consistent with the
    speakers meaning.
  • Hearer and speaker decrase the scores of used
    associations.
  • It may adopt an association between the formula
    and the sentence used by the speaker.

7
  • Induction
  • The agents use some induction mechanisms to
    extract
  • generalisations from the grammar rules learnt
    so far.
  • The induction rules used in the experiments are
    based
  • on the following rules proposed in (Kirby
    2002)
  • Simplification
  • Chunk
  • They are applied whenever the agents invent or
    adopt
  • a new association.

8
  • Simplification
  • r1 ? left(m1, S1) ? e1 , S1 is E?s1
  • r2 ? n(m1, S2) ? e1, S2 is s2
  • Rule r1 is replaced with the following rule
  • left(X, S1) ? n(X,S), S1 is E?S?0.01
  • where
  • X and S are new variables,
  • s1 and s2 are the scores of rules r1 and r2, and
  • E is the product of the score variables of the
    arith-metic expression on the right hand side of
    rule r1.

9
  • Simplification example 1
  • r1 ? s(right, S1) ? right, S1 is 0.25
  • r2 ? s(and,light,right, S2) ? andlightright,
    S2 is 0.10
  • Rule r2 is replaced with rule r3
  • r3 ? S(and,light,R, S) ? andlight, s(R, S3), S
    is S3 ? 0.01
  • r4 ? s(light, S4) ? light, S4 is 0.70
  • Rule r3 is replaced with rule r5
  • r5 ? s(and,Q,R, S) ? 1, and, s(Q, S2), s(R,
    S3), S is S3 ? S2 ? 0.01

10
  • Simplification example 2
  • r1 ? s(right, S1) ? right, S1 is 0.25
  • r6 ? s(or,light,right, S6) ? orlightright,
    S6 is 0.1
  • Rule r6 is replaced with rule r7
  • r7 ? S(or,light,R, S) ? orlight, s(R, S3), S
    is S3?0.01
  • r4 ? s(light, S4) ? light, S4 is 0.70
  • Rule r7 is replaced with rule r8
  • r8 ? s(or,Q,R, S) ? 1, or, s(Q, S2), s(R, S3),
    S is S2?S3?0.01

11
  • Simplification example 3
  • r1 ? s(right, S1) ? right, S1 is 0.25
  • r9 ? s(or,light,right, S9) ? lightorright, S9
    is 0.10
  • Rule r9 is replaced with rule r10
  • r10 ? S(or,light,R, S) ? lightor, s(R, S3), S
    is S3?0.01
  • r4 ? s(light, S4) ? light, S4 is 0.70
  • Rule r10 is replaced with rule r11
  • r11 ? s(or,Q,R, S) ? 2, or, s(Q, S2), s(R, S3),
    S is S2?S3?0.01

12
  • Chunk I
  • r1 ? left(f(m1), S1) ? right(e1)?, S1 is E?s1
  • r2 ? left(f(m2), S2) ? right(e2)?, S1 is E?s1
  • A new category symbol n is created and rules
    added
  • n(m1, 0.01) ? e1 n(m2, 0.01) ?
    e2
  • Rules r1 and r2 are replaced with rule r3, of the
    form
  • left(f(X), S3) ? right?(n(X, S)), S3 is
    E?S?0.01
  • where
  • X and S are new variables,
  • s1 and s2 are the scores of rules r1 and r2, and
  • E is the product of the score variables of the
    arith-metic expression on the right hand side of
    rule r1.

13
  • Chunk I example 1
  • r1 ? s(and,Q,R, S) ? 1, and, s(Q, S2), s(R,
    S3), S is S2?S3?0.10
  • r2 ? s(or,Q,R, S) ? 1, or, s(Q, S2), s(R, S3),
    S is S2?S3?0.30
  • The following new rules are added to grammar
  • c2(and, 0.01) ? and c2(or, 0.01) ? or
  • Rules r1 and r2 are replaced with rule r3
  • r3 ? s(P,Q,R, S) ? 1, c2(P, S1), s(Q, S2),
    s(R, S3), S is S1?S2?S3?0.01

14
  • Chunk I example 2
  • r1 ? s(and,Q,R, S) ? 1, and, s(Q, S2), s(R,
    S3), S is S2?S3?0.10
  • r2 ? s(or,Q,R, S) ? 2, or, s(Q, S2), s(R, S3),
    S is S2?S3?0.30
  • Chunk cannot be applied to r1 and r2, because
    they
  • place the expressions associated with the
    connectives
  • and and or in different positions in the
    sentence.

15
  • Chunk I example 3
  • r1 ? s(and,Q,R) ? 2, and, s(R, S3), s(Q, S2),
    S is S2?S3?0.10
  • r2 ? s(or,Q,R, S) ? 2, or, s(Q, S2), s(R, S3),
    S is S2?S3?0.30
  • Chunk cannot be applied to r1 and r2, because
    they
  • place the expressions associated with the
    arguments
  • of the binay connective, Q and R, in different
    posi-
  • tions in the sentence.
  • Rules must agree on the positions of the expres-
  • sions associated with the connectives and their
  • arguments in the sentence.

16
  • Chunk II
  • r1 ? left(f(X), S1) ? right?(n(X, S)),
  • r2 ? left(f(m1), S2) ? right?(e1),
  • Rule r2 is replaced with the following rule
  • n(m1, 0.01) ? e1
  • right?(X) is the result of removing the
    arithmetic ex-
  • pression of the right hand side of a grammr rule.

17
  • Chunk II example 1
  • r1 ? s(P,Q,R, S) ? 1, c2(P, S1), s(Q, S2), s(R,
    S3), S is S1?S2?S3?0.20
  • r2 ? s(iff,Q,R, S) ? 1, iff, s(Q, S2), s(R,
    S3), S is S2?S3?0.50
  • Rule r2 is replaced with the following rule
  • c2(iff, 0.01) ? iff

18
  • Chunk II example 2
  • r1 ? s(P,Q,R, S) ? 1, c2(P, S1), s(Q, S2), s(R,
    S3), S is S1?S2?S3?0.20
  • r2 ? s(iff,Q,R, S) ? 3, iff, s(Q, S2), s(R,
    S3), S is S2?S3?0.50
  • Chunk cannot be applied, because rule r1 places
    the
  • expresion associated with the connective in first
    po-
  • sition in the sentence and rule r2 places the
    expres-
  • sion associated with the connective third
    position.

19
  • Need for Coordination The agents must reach
  • agreements on how to
  • name propositional constants and connectives
  • a1 if ? if
    a2 if ? si
  • order the expressions associated with the
    different components of non-atomic meanings
    consistently
  • a1 not ? un, 2pos not, right ?
    rightnot
  • a2 not ? un, 1pos not, right ?
    notright
  • a1 if ? bin, 2pos, inv if,right,light ?
    lightifright
  • a2 if ? bin, 2pos, noinv if,right,light ?
    rightiflight

20
  • Self-organization Coordinate agents grammars
  • The agents construct a shared external language
    and
  • prefer using the rules in that language over the
    rest
  • in the rules in their individual grammars.
  • The scores of the rules indicate the agents
    preferences
  • meaning ? sentence1 highest score
  • competing sentences sentence2, ,
    sentenceN
  • sentence ? meaning1 highest score
  • competing meanings meaning2, ,
    meaningN
  • The score of a sentence (or meaning) is computed
    at
  • generation (parsing) multiplying the scores of
    the rules
  • involved (Vogt 2005).

21
  • Score of a sentence (meaning) example
  • r1 s(right, 0.25) ? right r2 c1(if,
    0.50) ? if
  • r3 s(light, 0.70) ? light r4 c2(if,
    0.10) ? si
  • r5 s(P,Q,R,S) ? 1, c1(P,S1), s(Q,S2), s(R,S3),
    S is S1?S2?S3?0.10
  • r6 s(P,Q,R,S) ? 1, c2(P,S1), s(R,S3), s(Q,S2),
    S is S1?S2?S3?0.01
  • Meaning if, right, light ? Generation
  • Sentence ifrightlight score
    0.50?0.25?0.70?0.10
  • Comp senten silightright score
    0.10?0.25?0.70?0.01

22
  • Coordination takes place at the third stage of a
    lan-guage game when the speaker communicates the
    meaning it had in mind to the hearer.
  • hearers meaning ? speakers meaning
  • Speaker increases scores of rules ? sentence
  • decreases scores of rules ? competing
    sentences
  • Hearer increases scores of rules ? meaning
  • decreases scores of rules ? competing
    meanings
  • hearers meaning ? speakers meaning
  • Speaker and hearer decrease scores of rules they
    used for generating and interpreting the sentence.

23
  • Reinforcement and Inhibition
  • The rules used successfully are reinforced.
  • The rules used for generating competing sentences
    or competing meanings are inhibited.
  • The rules used for updating scores of grammar
    rules (Steels 1999) replace the original score S
    with
  • S1 ? minimum(1, S 0.1) if the score is
    increased
  • S2 ? maximum(0, S ? 0.1) if the score is
    decreased
  • Purging the rules that have been used more than
    30 times and have scores ? 0.01 are removed from
    the agents grammars.

24
Experiments Evolution Communicative Success
guessing game 5 agents, 15000 games about
propositional formulas La, b, c, r, l, u
constructed using ?, ?, ?, ?, ?
25
Guessing negation ?
26
Guessing conjuction ? (commut)
27
Guessing implication ? (non-com)
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