Introduction to Developmental Learning 11 March 2014 [email protected]r http:// www.oliviergeorgeon.com t 1/33 oliviergeorgeon.com
Transcript
Slide 1
Introduction to Developmental Learning 11 March 2014
[email protected] http://www.oliviergeorgeon.com t
1/33oliviergeorgeon.com
Slide 2
Old dream of AI Instead of trying to produce a program to
simulate the adult mind, why not rather try to produce one which
simulates the child's? If this were then subjected to an
appropriate course of education one would obtain the adult brain.
Presumably, the child brain is something like a notebook []. Rather
little mechanism, and lots of blank sheets. []. Our hope is that
there is so little mechanism in the child brain that something like
it can be easily programmed. The amount of work in the education we
can assume, as a first approximation, to be much the same as for
the human child. Computing machinery and intelligence (Alan Turing,
1950, Mind, philosophy journal). 2/33oliviergeorgeon.com
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Is it even possible? Spiritualist vision of consciousness (it
would require a soul). Causal openness of physical reality (quantum
theory). Too complex. Materialist theory of consciousness (Julien
Offray de La Mettrie, 1709-1751). Consciousness as a computational
process (Chalmers 1994)
http://consc.net/papers/computation.htmlhttp://consc.net/papers/computation.html
No ? Yes? 3/33oliviergeorgeon.com
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Outiline Example Demo of developmental learning. Theoretical
bases Pose the problem. The question of self-programming. Exercise
Implement your self-programming agent. 4/33oliviergeorgeon.com
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6 Experiments The coupling agent/environment offers
hierarchical sequential regularities of interactions, for example :
After i 7, attempting i 1 or i 2 results more likely in i 1 than in
i 2. After i 9, i 3, i 1, i 8 , i 4, i 7, i 1 can often be enacted.
After i 8, sequence i 9, i 3, i 1 can often be enacted. After i 8,
i 8 can often be enacted again. i 1 (5) i 2 (-10) i 3 (-3) i 7 (-1)
i 8 (-1) i 5 (-1) i 6 (-1) i 9 (-1) i 10 (-1) i 4 (-3) 2 Results10
Interactions (value) 0101 0 0 1010 0101 0101 Example 1 5/28
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Exemple 1: Bump: Touch: Move Forward or bump(5) (-10) Turn left
/ right (-3) Feel right/ front / left (-1) 6/28
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Theoretical bases Philosophy of mind. Epistemology (theory of
knowledge) Developmental psychology. Biology (autopoiesis,
enaction). Neurosciences. 7/33oliviergeorgeon.com
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Philosophy : is it possible? John Locke (1632 1704) Tabula Rasa
La Mettrie (1709-1751). Matter can think David Chalmers A
Computational Foundation for the Study of Cognition (1994) Daniel
Dennett Consciousness explained (1991) Free will, individual
choice, self-motivation, dterminism 8/33oliviergeorgeon.com
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Key philosophical ideas for DIA cognition ascomputation in the
broad sense. Causal structure Example: neural net with chemistry
(neurotransmitters, hormones etc.). Determinisme does not
contradict free will. Do not mistake determinism for
predictibility. Herv Zwirn (Les systmes complexes, 2006)
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Epistmology (what can I know?) Concept of ontology Study of the
nature of being Aristotle (384 322 BC). Onto: being , Logos:
discourse. Discourse on the properties and categories of being.
Reality as such is unknowable Emmanuel Kant, (1724 1804)
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Key epistemological ideas for DAI Implement learning mechanism
with no ontological asumptions. Agnostic agents (Georgeon 2012).
The agent will never know its environment as we see it. But with
interactional assumptions Predefine the possibilities of
interaction between the agent and its environment. Let the agent
alone to construct its own ontology of the environment through its
experience of interaction. 11/33oliviergeorgeon.com
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Developmental psychology (How can I know?) Developmental
learning Jean Piaget (1896 1980) Teleology / motivational
principles the individual self-finalizes recursively. Do not
separate perception and action a priori: Notion of sensorimoteur
scheme Contructivist epistemology Jean-Louis Le Moigne (1931 - )
Ernst von Glasersfeld. Knowledge is an adaptation in the functional
sense. 12/33oliviergeorgeon.com
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Etapes dveloppementales indicatives Month 4: Bayesian
prediction. Month 5: Models of hand movement. Month 6: Objects and
face recognition. Month 7: Persistency of objects. Month 8: Dynamic
models of objects. Month 9: Tool use (bring a cup to the mouth).
Month 10: Gesture imitation, crawling. Month 11: Walk with the help
of an adult. Month 15: Walk alone. 13/45oliviergeorgeon.com
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Key psychological ideas for DAI Think in terms of interactions
rather than separating perception and action a priori. Focus on an
intermediary level of intelligence: Cognition smantique (Manzotti
& Chella 2012) stimulus-response adaptation Semantic cognition
Reasoning and language Low level High level Intermediary level
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Biology (why know?) Autopoiese auto: self, poise : creation
Maturana (1972) Structural coupling agent/environment. Relational
domain (the space of possibilities of interaction) Homeostasis
Internal state regulation Self-motivation Theory of enaction
Self-creation through interaction with the environment. Enactive
Artificial Intelligence (Froeze and Ziemke 2009).
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Key ideas from biology for DAI Constitutive autonomy is
necessary for sense- making. Evolution of possibilities of
interaction during the systems life. Individuation. Design systems
capable of programming themselves. The data that is learned is not
merely parameter values but is executable data.
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Neurosciences Many levels of analysis A lot of plasticity AND a
lot of pre-wiring 17/33oliviergeorgeon.com
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Neuroscience Connectome of C. Elegans: 302 neurons. Entirely
inborn connectome rather than acquired through experience
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Human connectome http://www.humanconnectomeproject.org
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Neurosciences Examples of mammalian brains No qualitative
rupture : human cognitive functions (e.g., language reasoning)
relies of brain structures that exist in other mammalian brains.
(This does not mean there is no innate differences !). The brain
serves at organizing behaviors in time and space.
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Key neuroscience ideas for DAI Renounce the hope that it will
be simple. Maybe begin at an intermediary level and go down if it
does not work? Biology can be source of inspiration Biologically
Inspired Cognitive Architectures. Importance of the capacity to
internally simulate courses of behaviors.
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Key ideas of the key ideas The objective is to learn (discover,
organze and exploit) regularities of interaction in time and space
to satisfy innate criteria (survival, curiosity, etc.). Without
pre-encoded ontological knowledge Which allows a kind of
constitutive autonomy (self-programming).
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Teaser for next course 23/5oliviergeorgeon.com
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Exercice 24/33oliviergeorgeon.com
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Exercice Two possible experiences E = {e 1,e 2 } Two possible
results R = {r 1,r 2 } Four possible interactions E x R = {i 11, i
12, i 21, i 22 } Two environments env 1 : e 1 -> r 1, e 2 ->
r 2 (i 12 et i 21 are never enacted) env 2 : e 1 -> r 2, e 2
-> r 1 (i 11 et i 22 are never enacted) Motivational systems:
mot 1 : v(i 11 ) = v(i 12 ) = 1, v(i 21 ) = v(i 22 ) = -1 mot 2 :
v(i 11 ) = v(i 12 ) = -1, v(i 21 ) = v(i 22 ) = 1 mot 2 : v(i 11 )
= v(i 21 ) = 1, v(i 12 ) = v(i 22 ) = -1 Implement un agent that
learn to enact positive interactions without knowing its motivatins
a priori (mot 1 or mot 2 ) neither its environnement (env 1 or env
2 ). Write a rapport of behavioral analysis based on activity
traces. 25/33oliviergeorgeon.com
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No hard-coded knowledge of the environment Agen{ public
Experience chooseExperience(){ If (env == env 1 and mot == mot 1 )
or (env == env 2 and mot == mot 2 ) return e 1 ; else return e 2 ;
} 26/33oliviergeorgeon.com
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Implementation public static Experience e1 = new experience();
Experience e2 = new experience(); public static Result r1 = new
result(); Result r2 = new result(); public static Interaction i11 =
new Interaction(e1,r1, 1); etc. Public static void main() Agent
agent = new Agent(); Environnement env = new Env1(); // Env2();
for(int i=0 ; i < 10 ; i++) e = agent.chooseExperience(r); r =
env.giveResult(e); System.out.println(e, r, value); Class Agent
public Experience chooseExperience(Result r) Class Environnement
public Result giveResult(experience e) Class Env1 Class Env2 Class
Experience Class Result Class Interaction(experience, result,
value) public int getValue() 27/33oliviergeorgeon.com