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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
s and CreativConclusions ve A
nalogiess
SOME DEFINITIONS FIRST
Analogy is ...“a subjective guess about the likely worthiness of a Fu
given path of exploration”Creativity is ...
uild Concepts
“the ability to find relations between apparently unrelated knowledge”
s and Creativg
Artificial Intelligence is ...
ve Analogies... s
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]
uild Concepts[ , ] [ , ] s and C
reativ
CreativityAnalogy
ve AnalogiesCreativitygy
especially in scientific and intellect al conte t
s
intellectual contexts
Fuild Conceepts and C
re
BACK IN THE 90’SA i f H f t dt ’ k
eative Analog
An overview of Hofstadter’s work
gies
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
uild Concepts
NumboTarget: 114; Bricks: 11 20 7 1 6
s and Creativ
Copycatabc abd; ijk ?
ve Analogies; j
TabletopLetter Spirit
s
Letter Spirit
COPYCAT: ARCHITECTURECOPYCAT: ARCHITECTURE[HOFSTADTER & MITCHELL, 1993]
Fu
Long-termMemory
uild Conceptss and C
reativ
WorkingMemory
ve Analogiess
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
uildC
oncepts
What to look at nextWhether to build a structure there
s and Creativ
How fast to build itWhether to destroy an existing structure there
ve Analogiesy g s
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
uildC
oncepts
unlikely
s and Creativve A
nalogiess
COPYCAT: TEMPERATURE
Measures how good the current “understanding “of the world is Fu
Temperature feeds back to codelets:Little organization High temperature
uild Concepts
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
ve Analogies
System gradually goes from random, parallel, bottom-up processing to deterministic, serial, top-
s
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
s
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
s and Creativy
perceptionreality as a result of its operation in the worldRepresentation is build of
ve Analogies
[Vernon, 2005]
psub-symbols (nodes, weights…)
s
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
ve Analogiesg ( )
concepts”
Almost 20 years later
s
Almost 20 years laterthis is still true!
Fuild Conce
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)
gies
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
uild Conceptshuman cognition
concrete abstract
s and Creativ
Perception spectrum
object relations abstract complex
ve Analogies
recognitionapple, table…
apple is onthe table
relationsBush is in therepublican
situationslove affair, war…
s
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)
ve Analogiess
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?
s and Creativve A
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
uild Conceptss and C
reativ
Models capable of
ve Analogiesp
building primitive representations of the environment
s
Fuild Conceepts and C
re
CONCEPTNET[Li d Si h 2004]
eative Analog
[Liu and Singh, 2004]
Start by selecting a preferred type of representation
gies
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
uild Conceptss and C
reativve Analogiess
CONCEPTNET: KNOWLEDGE BASE STRUCTURE
1.6 million assertions Fu
20 relation-types
uild Conceptss and C
reativve Analogiess
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’)
ve Analogies(PropertyOf x sweet )
(PropertyOf x ‘fruit’)
Analogous concepts of war: fire, murder, pollution, gun,
s
g p , , p , g ,car, fight, disaster, knife, …
Fuild Conce
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
ve Analogies
The robot already developed motor skills to interact with the world
s
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
re
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]
s
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
re
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
uild Concepts
domains“Integrating perceptual processes into a cognitive
s and Creativg g p p p g
model leads to flexible representations, and flexible representations lead to flexible actions”[H f t dt 1996]
ve Analogies
[Hofstadter, 1996]
… Flexible actions lead to analogical reasoning, and l i l i l d t t l ti hi
s
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
s
Springer. 2007.
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