1 logic aided lamarckian evolution evelina lamma (1), fabrizio riguzzi (2), luís moniz pereira (3)...

1

Logic Aided Lamarckian Evolution

Evelina Lamma(1), Fabrizio Riguzzi(2), Luís Moniz Pereira(3)

(1) DEIS, University of Bologna, Italy(2) DI, University of Ferrara, Italy

(3) CENTRIA, Departamento de Informática Universidade Nova de Lisboa, Portugal

2

Summary

Genetic algorithms Lamarckian operator Multi-agent genetic algorithms Genes and Memes Multi-agent Crossover Belief revision Evolutionary approach to belief revision Example Experiments Conclusions

3

Genetic Algorithms

Darwinian operators: selection mutation crossover

4

Lamarckian operator

Given a chromosome: express it as a phenotype modify the phenotype in order to

improve its fitness translate back the phenotype into a

genotypeModel of cultural evolutionConcept of “meme”

5

GA in Multi Agent Systems (MAS)

MAS: communication of knowledge by means of explicit messages

New: communication of knowledge by exchange of genes and memes

If the number of agents is fixed, each has a pool of chromosomes of its own; or each agent is a single chromosome and there is a single pool of agents

6

Genetic Operators

Crossover: used in order to exchange genes and memes among agents a chromosome in an agent is crossed

with chromosomes from other agents

Lamarckian operator: used to locally improve the fitness by experience-directed self-mutation

7

Genes and Memes

Genes are modified only by Darwinian operators individual “physical” features are fixed inherited irrespective of parental learning

Memes are modified by Darwinian and Lamarckian operators individual “cultural” features are changeable inherited via parental learning

8

Asymmetrical flow of memes

Memes only go from teacher to learnerIn crossover:

genes are copied from both parents

memes are copied from another agent only if that agent has “accessed” and “tagged” them:

• accessed: confirmed or modified after an application of the Lamarckian operator

• tagged: an extra bit is associated to each meme in order to code whether the meme has been accessed.

9

Multi-agent crossover

A new agent offspring is produced from two parent chromosomes one parent comes from the pool of another agent bits from each parent are copied according to a

mask

The mask is such that: genes are selected randomly, half from each parent memes are selected randomly, half from memes in

the other agent, but only if they have been accessed

10

Multiagent crossover

1 0 0 0 1 1 1

0 1 1 1

0 1 0 1 1 1 0

1 1 0 0

1 1 0 0 1 1 0

0 1 0 0 1 1 1

0 0 0 0

Mask

1 – take from Ag1

0 – take from Ag2

Ag1

tags

Ag2

tags

child in Ag1pool

genesmemes

11

Genetic algorithm (GA)

GA(max_gen, p, r ,m, l, Fitness)

max_gen : maximum number of generations before termination

p : number of individuals in the population

r : fraction of population to be replaced by crossover at each step

m : fraction of population to be mutated

l : fraction of population that evolves Lamarckianly

Fitness : fitness function F(hi)

12

Genetic algorithm

GA(max_gen, p, r ,m, l, Fitness)Initialize population P := set of p hypotheses randomly

generatedgen :=0while gen <= max_gen

Generate PS by applying the following operators to P :

selectioncrossovermutationLamarck

update: P := PS

return the hypothesis from PS with the highest fitness

13

Genetic operators

select: select (1- r) p hypotheses from P with a probability Pb proportional to their fitness and add them to PS

crossover: for i:=1 to r pselect h1 from P with probability Pb

select h2 from another agent chosen at random

crossover h1 with h2 obtaining h’1, add h’1 to PS

mutate: choose m percent of the members of PS with uniform probability

and, for each, invert randomly one bit

Lamarck: choose l p hypotheses from PS with uniform probability and apply to them the Lamarckian operator

14

How to mutate and tag memes

Use belief revison to tag memes

Follow the chain of logical steps forward, from assumptions (memes) to conclusions (predictions)

When predictions differ from experience (observation), follow the logical steps backward to those assumptions (memes) supporting the predictions

Confirm or mutate, and tag those memes

15

Simple revision example (1)

P: flies(X) bird(X), not ab(X). bird(a) .

ab(X) penguin(X).• We learn penguin(a).

P {penguin(a)} is consistent. Nothing more to be done.• We learn instead ¬flies(a).

P {¬flies(a)} is inconsistent. What to do?

Since the inconsistency rests on the assumption not ab(a), remove that assumption (e.g. by adding the fact ab(a), or forcing it undefined with ab(a) u) obtaining a new program P’.

If an assumption supports contradiction, then go back on that assumption.

16

Simple revision example (2)P: flies(X) bird(X), not ab(X). bird(a) .

ab(X) penguin(X).

If an assumption supports contradiction, then go back on that assumption.

If later we learn flies(a).

P’ {flies(a)} is inconsistent.

The contradiction does not depend on assumptions.

Cannot remove contradiction!

Some programs are non-revisable.

17

What to remove?

Which assumptions should be removed?normalWheel not flatTyre, not brokenSpokes.

flatTyre leakyValve. ¬normalWheel wobblyWheel.

flatTyre puncturedTube. wobblyWheel .

Contradiction can be removed by either dropping not flatTyre or not brokenSpokes

We’d like to delve deeper in the model and (instead of not flatTyre) either drop not leakyValve or not puncturedTube.

18

Revisables

Revisables = not {leakyValve, punctureTube, brokenSpokes}

Revisions in this case are {not lv}, {not pt}, and {not bs}

Solution: Define a set of revisables:

normalWheel not flatTyre, not brokenSpokes.

flatTyre leakyValve. ¬normalWheel wobblyWheel.

flatTyre puncturedTube. wobblyWheel .

19

Integrity Constraints

For convenience, instead of:¬normalWheel wobblyWheel

we may use the denial: normalWheel, wobblyWheel

Can further generalize ICs into:L1 … Ln Ln+1 … Lm

where Lis are literals (possibly not L).

20

ICs and Contradiction

In an ELP with ICs, add for every atom A: A, ¬A

A program P is contradictory iff P

where is the paraconsistent derivation of SLX

21

ExampleRev = not {a,b,c}

p, q

p not a.

q not b, r.

r not b.

r not c.

p q

not a r not b

not b not c

Support sets are:{not a, not b}and {not a, not b, not c}.

Removal sets are: {not a} and {not b}.

22

Example

In 2-valued revision: some removals must be deleted; the process must be iterated.

p. a. b, not c.p not a, not b.

a

X

p

not a not bb not c

XThe only support is {not a, not b}.Removals are {not a} and {not b}.

• P U {a} is contradictory (and unrevisable).• P U {b} is contradictory (though revisable).

But:

23

Algorithm for 2-valued revision

1 Let Revs={{}}2 For every element R of Revs:

Add it to the program and compute removal sets. Remove R from Revs For each removal set RS:

Add R U not RS to Revs3 Remove non-minimal sets from Revs4 Repeat 2 and 3 until reaching a fixed point of Revs. The revisions are the elements of the final Revs.

24

• Choose {b}. The removal set of P U {b} is {not c}. Add {b, c} to Rev. • Choose {b,c}. The removal set of P U {b,c} is {}. Add {b, c} to Rev.

• Choose {}. Removal sets of P U {} are {not a} and {not b}. Add them to Rev.

Example of 2-valued revision p. a. b, not c.p not a, not b.

Rev0 = {{}}

Rev1 = {{a}, {b}}

• Choose {a}. P U {a} has no removal sets.

Rev2 = {{b}}

Rev3 = {{b,c}}

•The fixed point had been reached. P U {b,c} is the only revision.

= Rev4

25

Revision and Diagnosis

In model based diagnosis one has: a program P with the model of a system

(the correct and, possibly, incorrect behaviors)

a set of observations O inconsistent with P (or not explained by P).

The diagnoses of the system are the revisions of P U O.

26

Diagnosis Example

1

1

1

10

c1=0

c3=0

c6=0

c7=0

c2=0

0

1

g10

g11

g16

g19

g22

g23

27

Diagnosis Program Observablesobs(out(inpt0, c1), 0).obs(out(inpt0, c2), 0).obs(out(inpt0, c3), 0).obs(out(inpt0, c6), 0).obs(out(inpt0, c7), 0).obs(out(nand, g22), 0). obs(out(nand, g23), 1). Predicted and observed values cannot be different

obs(out(G, N), V1), val(out(G, N), V2), V1 V2.

Connectionsconn(in(nand, g10, 1), out(inpt0, c1)).conn(in(nand, g10, 2), out(inpt0, c3)).…conn(in(nand, g23, 1), out(nand, g16)).conn(in(nand, g23, 2), out(nand, g19)).

Value propagationval( in(T,N,Nr), V ) conn( in(T,N,Nr), out(T2,N2) ), val( out(T2,N2), V ).val( out(inpt0, N), V ) obs( out(inpt0, N), V ). Normal behaviorval( out(nand,N), V ) not ab(N), val( in(nand,N,1), W1), val( in(nand,N,2), W2),

nand_table(W1,W2,V). Abnormal behaviorval( out(nand,N), V ) ab(N), val( in(nand,N,1), W1), val( in(nand,N,2), W2),

and_table(W1,W2,V).

Run

28

Diagnosis Example

c1=0

c3=0

c6=0

c7=0

c2=0

0

1

g10

g11

g16

g19

g22

g23

Revision are:{ab(g23)}, {ab(g19)}, and {ab(g16),ab(g22)}

1

1

1

1 1

0

1

1

0

1 1

0

1

1

1

0 1

0

29

Belief Revision

Important functionality of agents.Problem definition. Given

an extended logic program containing integrity constraints, i.e.:

B1,…,Bn, not C1,…,not Cm

a set of revisable literals, i.e., literals for which the revision is allowed. They must not have any definition

Find…

30

Belief Revision

Find: a truth value for the revisable literals so

that the program is not contradictory, i.e., does not belong to the model of the program

31

GAs for Belief Revision

Genetic Algorithms can be used for Belief Revision: each revisable is encoded with a

meme the meme has value 1 if the revisable

is true and 0 if it is false each set of assumptions about the

values of revisables is coded as a chromosome

32

Fitness function

ni number of integrity constraints satisfied by hypothesis hi

n total number of integrity constraints

n

nhF i

i )(

33

Example

Digital circuit diagnosis

Revisable literals indicate the assumed behaviour mode of each gate:

not ab(gate) : gate behaves normally ab(gate): gate behaves abnormally

34

Example: circuit c17

g10

g11

g22

g16

g19g23

g6

g1

g3

g2

g7

0

0

0

1

0

0

1

1

1

0

11

1

obs

obs

35

Example: circuit c17

val( in(Type,Name,Nr), V ) :-

conn( in(Type,Name,Nr), out(Type2,Name2) ), val( out(Type2,Name2),

V ).

val( out(nand,Name), V ) :- not ab(Name), val( in(nand,Name,1), W1), val( in(nand,Name,2), W2), nand_table(W1,W2,V).

nand_table(0,0,1).…...

val( out(nand,Name), V ) :- ab(Name), val( in(nand,Name,1), W1), val( in(nand,Name,2), W2), and_table(W1,W2,V).

val( out(inpt0, Name), V ) :- obs( out(inpt0, Name), V ).

36

Topology

conn(in(nand, g10, 1), out(inpt0, g1)).

conn(in(nand, g10, 2), out(inpt0, g3)).

conn(in(nand, g11, 1), out(inpt0, g3)).

conn(in(nand, g11, 2), out(inpt0, g6)).

conn(in(nand, g16, 1), out(inpt0, g2)).

conn(in(nand, g16, 2), out(nand, g11)).

conn(in(nand, g19, 1), out(nand, g11)).conn(in(nand, g19, 2), out(inpt0, g7)).

conn(in(nand, g22, 1), out(nand, g10)).conn(in(nand, g22, 2), out(nand, g16)).

conn(in(nand, g23, 1), out(nand, g16)).conn(in(nand, g23, 2), out(nand, g19)).

37

Observations and constraints :- obs(out(nand, g22), 0), val(out(nand, g22), 1). :- obs(out(nand, g22), 1), val(out(nand, g22), 0). :- obs(out(nand, g23), 0), val(out(nand, g23), 1). :- obs(out(nand, g23), 1), val(out(nand, g23), 0).

obs(out(inpt0, g1), 0).obs(out(inpt0, g2), 1).obs(out(inpt0, g3), 0).obs(out(inpt0, g6), 0).obs(out(inpt0, g7), 0).obs(out(nand, g22), 0).obs(out(nand, g23), 1).

38

Diagnosis

One of the integrity constraints is violated: the observed output for g22 is

different from the computed output.

Contradiction is removed by assuming ab(g22)

which is a diagnosis for the circuit.

39

Belief Revision

Support Set: a support set of a literal L of a program P, denoted by SS(L), is a set of revisables sufficient to support a derivation of L in P

40

Belief Revision

Hitting set: a hitting set of for a collection of SS(L) is the union of one non-empty subset from each SS(L). It is minimal iff no proper subset is a hitting set.

A contradiction removal set is a hitting set for the SS().

41

Lamarckian operator

The Lamarckian operator uses techniques similar to BR ones.

It differs from BR because it starts from an arbitrary chromosome C

The Lamarckian support sets are all the support sets that are subsets of the current chromosome C

42

Lamarckian operator

find all the Lamarckian support sets for with respect to C

find a hitting set HS() for themchange in C all its literals which are

in HS().

43

Example: circuit c17

Suppose, initially: C={ab(g10), not ab(g11), ab(g16),

not ab(g19), not ab(g22), not ab(g23)}In this case, two constraints are

violated because out(g22)=1 and out(g23)=0

44

Example: circuit c17

A BR operator would return as changes to C: {not ab(g10), not ab(g11),

not ab(g16), not ab(g19), ab(g22), not ab(g23)}

these are consistent with both ICs

45

Example: circuit c17

Lamarckian support sets of : [not ab(g11),not ab(g19),not ab(g11),ab(g16),not ab(g23)] [not ab(g11),ab(g16),ab(g10),not ab(g22)]

Lamarck returns these changes to C, one for each hitting set: C={ab(g10), ab(g11), ab(g16),

not ab(g19), not ab(g22), not ab(g23)} C={ab(g10), not ab(g11), not ab(g16),

not ab(g19), not ab(g22), not ab(g23)} one constraint in either case is still violated

46

Experiments

ISCAS85 collection of benchmark digital circuits

Four algorithms considered:S-L: single agent GA without the

Lamarckian operatorM-L: as S-L but multi agentM+L-A: as M-L plus Lamarck, without

asymmetryM+L+A: as M+L-A plus asymmetry

47

Results

alu4_flat circuit 100 gates (100 revisables) 8 outputs (16 constraints) 4 agents, with same observations and

constraints 10 chromosomes each, l=0.6 5 experiments

Average fitness:

S-L M-L M+L-A M+L+A

Fitness 0.8958 0.9375 0.9583 0.9791

48

Conclusions

Framework for solving problems represented with logic: belief revision dynamic world, control of observable outputs

Performance improvement by distributed agents Lamarckian operator asymmetric crossover on memes

49

Future work

Situations where: agents do not have the same observations,

constraints or revisables observations change over time

Three-valued memes for expressing irrelevancy

Integrating Lamarckism with other agent features

50

THE END

[ab(c11gat),ab(c19gat),ab(c11gat),ab(c16gat),ab(c23gat)],[not ab(c11gat),ab(c19gat),ab(c11gat),ab(c16gat),ab(c23gat)],[ab(c11gat),not ab(c19gat),ab(c11gat),ab(c16gat),ab(c23gat)],[not ab(c11gat),not ab(c19gat),ab(c11gat),ab(c16gat),ab(c23gat)],[ab(c11gat),ab(c19gat),not ab(c11gat),ab(c16gat),ab(c23gat)],[not ab(c11gat),ab(c19gat),not ab(c11gat),ab(c16gat),ab(c23gat)],[ab(c11gat),ab(c19gat),ab(c11gat),not ab(c16gat),ab(c23gat)],[not ab(c11gat),ab(c19gat),ab(c11gat),not ab(c16gat),ab(c23gat)],[ab(c11gat),ab(c19gat),not ab(c11gat),not ab(c16gat),ab(c23gat)],[not ab(c11gat),ab(c19gat),not ab(c11gat),not ab(c16gat),ab(c23gat)],[ab(c11gat),not ab(c19gat),not ab(c11gat),not ab(c16gat),ab(c23gat)],[not ab(c11gat),not ab(c19gat),not ab(c11gat),not ab(c16gat),ab(c23gat)],[ab(c11gat),not ab(c19gat),not ab(c11gat),ab(c16gat),not ab(c23gat)],[not ab(c11gat),not ab(c19gat),not ab(c11gat),ab(c16gat),not ab(c23gat)],[ab(c11gat),not ab(c19gat),ab(c11gat),not ab(c16gat),not ab(c23gat)],[not ab(c11gat),not ab(c19gat),ab(c11gat),not ab(c16gat),not ab(c23gat)],[not ab(c11gat),ab(c16gat),not ab(c10gat),ab(c22gat)],[ab(c11gat),not ab(c16gat),not ab(c10gat),ab(c22gat)],[ab(c11gat),ab(c16gat),ab(c10gat),not ab(c22gat)],[not ab(c11gat),ab(c16gat),ab(c10gat),not ab(c22gat)],[ab(c11gat),not ab(c16gat),ab(c10gat),not ab(c22gat)],[not ab(c11gat),not ab(c16gat),ab(c10gat),not ab(c22gat)],[ab(c11gat),ab(c16gat),not ab(c10gat),not ab(c22gat)],[not ab(c11gat),not ab(c16gat),not ab(c10gat),not ab(c22gat)]

52

Belief Revision

Support Set: a support set of a literal L of a program P, denoted by SS(L), is obtained as follows: if L is not a revisable literal, then, for

each rule L B in P, there is one SS(L) given by the union of SS(Bi) for each Bi B. If B is empty then SS(L)={}

if L is a revisable literal then SS(L)={L}

53

Lamarckian operator

Lamarckian support set: given an hypothesis C, a Lamarckian support set of a literal L of a program P, denoted by SS(L), is obtained as follows:

if L is not a revisable literal, then, for each rule L B in P there is one SS(L) given by the union of SS(Bi) for each Bi B. If B is empty then SS(L)={}if L is a revisable literal then

if L belongs to C, then SS(L)={L}if L is not in C or the default complement belongs to C then the SS(L) under construction is not a support set

54

Results, single agent

Single agent, with and without the Lamarckian operator

Fitness function:

fi number of revisables of hi that are false

5.0)( i

iii h

f

n

nhF

Circuit l Fitness Correct solution0 1.175 100%voter

0.6 1.218 100%

0 1.296 0%alu4_flat

0.6 1.310 30%

55

Logic Programs RevisionThe problem:

A LP represents consistent incomplete knowledge;

New factual information comes.

How to incorporate the new information?

The solution: Add the new facts to

the program If the union is

consistent this is the result

Otherwise restore consistency to the union

The new problem: How to restore consistency to an

inconsistent program?

56

Algorithm for 3-valued revision

Find all derivations for , collecting for each one the set of revisables supporting it. Each is a support set.

Compute the minimal hitting sets of the support sets. Each is a removal set.

A revision of P is obtained by adding{A u: A R}

where R is a removal set of P.

57

2-valued Revision

In diagnosis one often wants the IC:ab(X) v not ab(X)

With these ICs (that are not denials), 3-valued revision is not enough.

A two valued revision is obtained by adding facts for revisables, in order to remove contradiction.

For 2-valued revision the algorithm no longer works…

58

Simple diagnosis exampleinv(G,I,0) node(I,1), not ab(G).inv(G,I,1) node(I,0), not ab(G).node(b,V) inv(g1,a,V).node(a,1).¬node(b,0).

%Fault modelinv(G,I,0) node(I,0), ab(G).inv(G,I,1) node(I,1), ab(G).

a=1 b0g1

The only revision is:P U {ab(g1) u}

It does not conclude node(b,1).

In diagnosis applications (when fault models are considered) 3-valued revision is not enough.

59

Abduction as Revision

For abductive queries:

Declare as revisable all the abducibles If the abductive query is Q, add the IC:

not Q The revision of the program are the

abductive solutions of Q.

60

Revision and Debugging

Declarative debugging can be seen as diagnosis of a program.

The components are: rule instances (that may be incorrect). predicate instances (that may be uncovered)

The (partial) intended meaning can be added as ICs.

If the program with ICs is contradictory, revisions are the possible bugs.

61

Debugging Transformation

Add to the body of each possibly incorrect rule r(X) the literal not incorrect(r(X)).

For each possibly uncovered predicate p(X) add the rule:

p(X) uncovered(p(X)).

For each goal G that you don’t want to prove add: G.

For each goal G that you want to prove add: not G.

62

Debugging example

a not b

b not c

WFM = {not a, b, not c}

b should be false

a not b, not incorrect(a not b)

b not c, not incorrect(b not c)a uncovered(a)b uncovered(b)c uncovered(c) bRevisables are incorrect/1 and uncovered/1

Revision is:

{incorrect(b not c)}

{uncovered(c)}

BUT a should be false!

Add a

Revisions now are:

{inc(b not c), inc(a not b)}

{unc(c ), inc(a not b)}

BUT c should be true!

Add not c

The only revision is:

{unc(c ), inc(a not b)}