Aprendizagem Simbólica e Sub-Simbólica 2009

FLUID CONCEPTS AND CREATIVEFLUID CONCEPTS AND CREATIVEANALOGIES

Iolanda Leite

OUTLINE

Analogy, Creativity, AI and its relationsOverview of Hofstadter’s projects Fu

The problems of perception, representation and analogy

uild Concepts

Artificial CreativityConclusions

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nalogiess

SOME DEFINITIONS FIRST

Analogy is ...“a subjective guess about the likely worthiness of a Fu

given path of exploration”Creativity is ...

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“the ability to find relations between apparently unrelated knowledge”

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Artificial Intelligence is ...

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HOW DO THESE CONCEPTS RELATE TOEACH OTHER?

Artificial

“traditional AI methods of problem solving are not capable of dealing Fu

Intelligence“analogy lies at the heart of intelligence”[Hofstadter, 1996]

not capable of dealing with unpredicted situations”[Câmara Pereira, 2006]

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reativ

CreativityAnalogy

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especially in scientific and intellect al conte t

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intellectual contexts

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BACK IN THE 90’SA i f H f t dt ’ k

eative Analog

An overview of Hofstadter’s work

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THE BOOK

Long-term goal:Uncover the secrets of creativity and consciousness

FuUncover the secrets of creativity and consciousness

Short-term goal:

uild Concepts

Understand fundamental cognitive processes by modelling them in restricted microdomains

P tt ti t l ti d

s and CreativPattern perception, extrapolation and

generalisation (analogy?) are the “true crux of creativity”

ve Analogiescreativity s

PROJECTS FROM HOFSTADTER’S GROUP

S k WhSeek-Whence1, 2, 2, 3, 3, 4, 4, 5, 5, 6, … Fu

Jumbolbujme jumble

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NumboTarget: 114; Bricks: 11 20 7 1 6

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Copycatabc abd; ijk ?

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TabletopLetter Spirit

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Letter Spirit

COPYCAT: ARCHITECTURECOPYCAT: ARCHITECTURE[HOFSTADTER & MITCHELL, 1993]

Fu

Long-termMemory

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WorkingMemory

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COPYCAT: WORKSPACE

C d l t b ild t l t t i i f ti Codelets build perceptual structures using information from the Slipnet

G d ll b ild dditi l d i ti d t t

Fu

Gradually build up additional descriptions and structures

Decisions are made by codelets in a probabilistic manner

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oncepts

What to look at nextWhether to build a structure there

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How fast to build itWhether to destroy an existing structure there

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COPYCAT: SLIPNET

Directed graph of nodes, representing concepts, and labeled links Fu

Distances between concepts can change over timeTemperature determines what slippages are likely and

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oncepts

unlikely

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nalogiess

COPYCAT: TEMPERATURE

Measures how good the current “understanding “of the world is Fu

Temperature feeds back to codelets:Little organization High temperature

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low confidence in decisionsdecisions are made more randomly

L t f i ti L t t

s and CreativLots of organization Low temperature

high confidence in decisionsdecisions are made more deterministically

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System gradually goes from random, parallel, bottom-up processing to deterministic, serial, top-

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down processing

COPYCAT: MAIN IDEAS

C t ti h tl t t d h l t f Constructing a coherently structured whole out of initially unattached partsU d t di d ti f i il it i b ilt

Fu

Understanding and perception of similarity is built up collectively by many independent simple “agents” working in parallel

uild Conceptsworking in parallel

Simulating fluid concepts, and not analogy per se: “agents” working together produce an emergent

s and Creativagents working together produce an emergent

understanding of analogyTries to imitate human reasoning on these kind of

ve AnalogiesTries to imitate human reasoning on these kind of

puzzles

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COGNITIVIST VS EMERGENT SYSTEMS: WHERE DOES COPYCAT FIT?

Computations defined The system is continually

Cognitivist Systems Emergent Systems

FuComputations defined over symbolicrepresentations

The system is continually re-constituting itself through system-

uild Conceptsrepresentations

Information about the world is abstracted by

environment interactionsThe agent constructs its reality as a result of its

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perceptionreality as a result of its operation in the worldRepresentation is build of

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[Vernon, 2005]

psub-symbols (nodes, weights…)

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COGNITIVIST VS EMERGENT SYSTEMS: WHERE DOES COPYCAT FIT?

“The philosophy underlying copycat relates to the emergent paradigm, but the actual program fits somewhere in between”

Fusomewhere in betweenBuilds flexible representations using fixed perceptual mechanisms (relation group correspondences )

uild Conceptsmechanisms (relation, group, correspondences...)

Why?“ d li i i i b b li l i ll

s and Creativ

“modeling cognition using a subsymbolic, neurologically architecture may be too ambitious at this point in cognitive science (...) we need to understand the nature of

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concepts”

Almost 20 years later

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Almost 20 years laterthis is still true!

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PERCEPTION REPRESENTATION

epts and CrePERCEPTION, REPRESENTATION

AND ANALOGY( h AI i t d i f t i iti ll

eative Analog

(why AI is not advancing as fast as initially planned)

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THE PROBLEM OF PERCEPTION

Perception: “the process of making sense of complex data at an abstract, conceptual level” [Hofstadter, 1996] Fu

Is deeply linked with other cognitive processesDismissal perceptual processes lead to distorted models of h iti

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concrete abstract

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Perception spectrum

object relations abstract complex

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recognitionapple, table…

apple is onthe table

relationsBush is in therepublican

situationslove affair, war…

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party

THE PROBLEM OF PERCEPTION

Perceptions are flexible and subjective. They are influenced by: Fu

Beliefs, anticipation of a situationGoals

uild Concepts

External contextAnd can be reshaped when necessary (change

s and Creativ

perspective)

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THE PROBLEM OF REPRESENTATION

What is the correct structure of mental representations? Fu

Understand how such representations can be derived from environmental data (perceptions)

uild Concepts

Which information is relevant and which isirrelevant?

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nalogiess

HOW TO OVERCOME THESE PROBLEMS?

Start by selecting a preferred type of representation and also Fuyp p

the relevant data for the problem at hand

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Models capable of

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building primitive representations of the environment

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CONCEPTNET[Li d Si h 2004]

eative Analog

[Liu and Singh, 2004]

Start by selecting a preferred type of representation

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y g p yp pand also the relevant data for the problem at hand

CONCEPTNET

Freely available commonsense knowledge baseSupports many practical textual-reasoning tasks Fu

including analogy-making

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CONCEPTNET: KNOWLEDGE BASE STRUCTURE

1.6 million assertions Fu

20 relation-types

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ANALOGY IN CONCEPTNET

C ti b t d i ht dConnections between nodes are weighted:Weakly semantic relations: LocationOf, IsA, … Fu

Strong semantic relations: PropertyOf, MotivationOf, …Two concepts are analogous if their sets of back-edges

l

uild Conceptsoverlap:

apple and cherry are analogous because they share the back-edges

s and Creativback-edges

(PropertyOf x ‘red’)(PropertyOf x ‘sweet’)

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(PropertyOf x ‘fruit’)

Analogous concepts of war: fire, murder, pollution, gun,

s

g p , , p , g ,car, fight, disaster, knife, …

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LEARNING OBJECT

epts and CreLEARNING OBJECT

AFFORDANCES[M t t l 2007]

eative Analog

[Montesano et al, 2007]

(close to) Models capable of building primitive

gies

( ) p g prepresentations of the environment

LEARNING OBJECT AFFORDANCES

Application: robots capable of acting in a complex world Fu

Affordances encode relationships between actions objects and effects

uild Conceptsactions, objects and effects

Learning affordances from t h i l

s and Creativscratch is an very large

dimension search problemTh b t l d d l d

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The robot already developed motor skills to interact with the world

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LEARNING OBJECT AFFORDANCES

Captures relations between actions object features and effects Fuactions, object features and effects

Bayesian networks to encode the dependencies

uild Conceptsp

Learned through observation and interaction with the world

s and Creativinteraction with the world

Detects the features that really matter for each affordance

ve Analogiesmatter for each affordance s

Fuild Conceepts and C

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ARTIFICIAL CREATIVITY

eative Analoggies

ARTIFICIAL CREATIVITY

JAPE joke generator [Binstead, 1996]

HR mathematical theory foundation program [Colton Fu

et al, 1999]

ASPERA poetry generator [Gervás, 2001]

uild Concepts

MuzaCazUza melody generator [Ribeiro et al, 2001]

I-Sounds (affective music based on emotional state of

s and CreativI Sounds (affective music based on emotional state of

characters) [Cruz et al., 2007]

CAST automatic storytelling system [Léon & Gervás

ve AnalogiesCAST automatic storytelling system [Léon & Gervás,

2008]

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ARTIFICIAL CREATIVITY: (AGAIN) THE PROBLEMARTIFICIAL CREATIVITY: (AGAIN) THE PROBLEMOF PERCEPTION/INPUT KNOWLEDGE

“the success of the program relies almost entirely on its being given data that have already been represented in a near optimal form”

Furepresented in a near optimal form[Hofstadter, 1996] about BACON, a program advertised as an accurate model for scientific discovery

uild Concepts

“the more fine-tuned a program is the less

s and Creativthe more fine tuned a program is, the less

creativity we attribute to it” [Colton et al, 2001]

ve Analogiess

Fuild Conceepts and C

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CONCLUSIONS

eative Analoggies

CONCLUSIONS

The problem of perception/representation in AI is far from being solved Fu

There are not many genuine creative systemsCreative analogies are possible only in very limited

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domains“Integrating perceptual processes into a cognitive

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model leads to flexible representations, and flexible representations lead to flexible actions”[H f t dt 1996]

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[Hofstadter, 1996]

… Flexible actions lead to analogical reasoning, and l i l i l d t t l ti hi

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analogical reasoning leads to truly creative machines

BIBLIOGRAPHY

Hofstadter, Douglas. Fluid Concepts and Creative Analogies: Computer Models of the Fundamental Mechanisms of Thought Basic

FuFundamental Mechanisms of Thought. Basic Books. 1995Câmara Pereira F Creativity and artificial

uild ConceptsCâmara Pereira , F. Creativity and artificial

intelligence: a conceptual blending approach. Walter de Gruyter, 2007. ISBN 3110186098.

s and CreativWalter de Gruyter, 2007. ISBN 3110186098.

Vernon, D. Cognitive vision: The case for embodied perception Image Vision Comput

ve Analogiesembodied perception Image Vision Comput.,

Butterworth-Heinemann, 2008, 26, 127-140.

s

BIBLIOGRAPHY

Liu, H. & Singh, P. ConceptNet: A PracticalCommonsense Reasoning Toolkit. BT TechnologyJournal 2004 22 211 226

FuJournal, 2004, 22, 211-226.Montesano, L.; Lopes, M.; Bernardino, A.; Santos Victor J Learning Object Affordances:

uild ConceptsSantos-Victor, J. Learning Object Affordances:

From Sensory-Motor Coordination to Imitation.IEEE Transactions on Robotics and Automation.

s and CreativIEEE Transactions on Robotics and Automation.

Volume 24, Issue 1, Feb. 2008, 15 – 26.S Colton A Pease and G Ritchie The Effect of

ve AnalogiesS. Colton, A. Pease and G. Ritchie. The Effect of

Input Knowledge on Creativity. Proceedings of the ICCBR'01 Workshop on Creative Systems,

s

Vancouver, Canada, 2001

BIBLIOGRAPHY

Bi t d K M hi H A I l t d Binsted, K. Machine Humour: An Implemented Model of Puns. Ph.D. Dissertation, Department of Artificial Intelligence, University of Edinburgh, Fuf g , y g ,1996.Colton, S.; Bundy, A.; andWalsh, T. HR:

uild Concepts, ; y, ; ,

Automatic concept formation in pure mathematics. In Proceedings of the 16th IJCAI, 1999 786–791

s and Creativ1999, 786–791.

Ribeiro, P.; Pereira, F. C.; Ferrand, M.; andCardoso A Case-based melody generation with

ve AnalogiesCardoso, A. Case based melody generation with

MuzaCazUza. In Wiggins, G., ed., Proceedings of the AISB’01 Symposium on Artificial Intelligence

d C ti it i A t d S i 2001 67 74

s

and Creativity in Arts and Science, 2001, 67–74.

BIBLIOGRAPHY

Gervás, P. Generating poetry from a prose text: Creativity versus faithfulness. In Wiggins, G., ed Proceedings of the AISB’01 Symposium on

Fued., Proceedings of the AISB 01 Symposium on Artificial Intelligence and Creativity in Arts and Science, 2001, 93–99.

uild Concepts, ,

Cruz, R., Brisson, A., Paiva, A., & Lopes, E. I-sounds. In Ana Paiva, Rui Prada and Rosalind

s and Creativsounds. In Ana Paiva, Rui Prada and Rosalind

Picard (Ed.), Proceedings of ACII 2007 (TheSecond International Conference on Affective

ve Analogies

Computing and Intelligent Interaction): LectureNotes in Computer Science (pp. 766-767). Springer 2007

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Springer. 2007.