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Developmental Artificial Intelligence. 1 er April 2014 Olivier.georgeon@liris.cnrs.fr http://www.oliviergeorgeon.com. t. Outline. Example Demos with robots. Conclusion of the course. Learning through experience. Intermediary level of AI: semantic cognition . Exercise

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developmental artificial intelligence

Developmental Artificial Intelligence

1er April 2014

Olivier.georgeon@liris.cnrs.fr

http://www.oliviergeorgeon.com

t

oliviergeorgeon.com

outline
Outline
  • Example
    • Demos with robots.
  • Conclusion of the course.
    • Learning through experience.
    • Intermediary level of AI: semantic cognition.
  • Exercise
    • Implement your self-programming agent (follow-up).

oliviergeorgeon.com

robotics research
Robotics research

Panoramic camera

Bumper tactile sensor

Ground optic sensor

oliviergeorgeon.com

http://liris.cnrs.fr/simon.gay/index.php?page=eirl&lang=en

experimentation
Experimentation

oliviergeorgeon.com

developmental learning in ai
Developmental Learning in AI

Theories

Validation paradigm

Implementations

Phenomenology (Dreyfus, 2007)

“even more Heideggarian AI”

Radical Interactionism, ECA (Georgeon et al., 2013)

perception-action inversion

(Pfeifer & Scheier, 1994)

Behavioral analysis rather than performance measure

Horde (Sutton et al., 2011)

SMC theory (O’Regan & Noë, 2001)

Cybernetic control theory (Foerster, 1960)

Learning by experiencing

“intrinsic motivation”

(Oudeyer et al. 2007)

Learning by registering

(Georgeon, 2014)

Embodied AI.

“Constructivist Schema mechanisms”

(Drescher, 1991).

Kuipers et al., (1997)

Naively confusing perception and sensing (Crowley, 2014)

Non-symbolic Machine Learning.

Reinforcement Learning. POMDP, etc.

Non-symbolic

CLARION

ACT-R

Soar

Symbolic

Newell & Simon (1972) goals drive problem solving;

(1976) Physical Symbol Systems.

symbolic ai
Symbolic AI

The “environment” passes “symbols” to the agent as input.

We encode the “semantics” of symbols in the agent.

We implement a “reasoning engine”.

(“symbolic” should not be mistaken with “discrete”)

developmental learning in ai1
Developmental Learning in AI

Theories

Validation paradigm

Implementations

Phenomenology (Dreyfus, 2007)

“even more Heideggarian AI”

Radical Interactionism, ECA (Georgeon et al., 2013)

perception-action inversion

(Pfeifer & Scheier, 1994)

Behavioral analysis rather than performance measure

Horde (Sutton et al., 2011)

SMC theory (O’Regan & Noë, 2001)

Cybernetic control theory (Foerster, 1960)

Learning by experiencing

“intrinsic motivation”

(Oudeyer et al. 2007)

Learning by registering

(Georgeon, 2014)

Embodied AI.

“Constructivist Schema mechanisms”

(Drescher, 1991).

Kuipers et al., (1997)

Naively confusing perception and sensing (Crowley, 2014)

Non-symbolic Machine Learning.

Reinforcement Learning. POMDP, etc.

Non-symbolic

CLARION

ACT-R

Soar

Symbolic

Newell & Simon (1972) goals drive problem solving;

(1976) Physical Symbol Systems.

learning by registering
Learning by registering

2

2

1

4

4

3

The “environment” passes “observations” to the agent as input.

The relation state -> observation is “statistically” a surjection.

We implement algorithms that assume that a given “state” induces a given “observation” (although partial and subject to noise).

developmental learning in ai2
Developmental Learning in AI

Theories

Validation paradigm

Implementations

Phenomenology (Dreyfus, 2007)

“even more Heideggarian AI”

Radical Interactionism, ECA (Georgeon et al., 2013)

perception-action inversion

(Pfeifer & Scheier, 1994)

Behavioral analysis rather than performance measure

Horde (Sutton et al., 2011)

SMC theory (O’Regan & Noë, 2001)

Cybernetic control theory (Foerster, 1960)

Learning by experiencing

“intrinsic motivation”

(Oudeyer et al. 2007)

Learning by registering

(Georgeon, 2014)

Embodied AI.

“Constructivist Schema mechanisms”

(Drescher, 1991).

Kuipers et al., (1997)

Naively confusing perception and sensing (Crowley, 2014)

Non-symbolic Machine Learning.

Reinforcement Learning. POMDP, etc.

Non-symbolic

CLARION

ACT-R

Soar

Symbolic

Newell & Simon (1972) goals drive problem solving;

(1976) Physical Symbol Systems.

positionnement dans le cadre de l ia
Positionnement dans le cadre de l’IA

Theories

Validation paradigm

Implementations

Phenomenology (Dreyfus, 2007)

“even more Heideggarian AI”

Radical Interactionism, ECA (Georgeon et al., 2013)

perception-action inversion

(Pfeifer & Scheier, 1994)

Behavioral analysis rather than performance measure

Horde (Sutton et al., 2011)

SMC theory (O’Regan & Noë, 2001)

Cognition située (Clancey 1992)

“intrinsic motivation”

(Oudeyer et al. 2007)

Apprentissage par l’expérience

Apprentissage « désincarnée »

Apprentissage par l’observation

(Georgeon, 2014)

En confondant naivement “input” et “perception”

(Crowley, 2014)

Reinforcement Learning.

Neural networks.

Machine learning. A* etc.

Non-symbolic

Symbolic

Newell & Simon (1972) goals drive problem solving;

(1976) Physical Symbol Systems.

change blindness
Change blindness

http://nivea.psycho.univ-paris5.fr/

oliviergeorgeon.com

learning by experiencing
Learning by experiencing

Time

The environment passes the “result” of an “experiment” initiated by the agent.

This is counter intuitive !

We implement algorithms that learn to “master the laws of sensorimotor contingencies”

(O’Regan & Noë, 2001).

accept the counter intuitiveness
Accept the counter-intuitiveness
  • We have the impression that the sun revolves around the earth.
    • False impression! (Copernic, 1519)
  • We have the impression to receive input data about the state of the world.
    • False impression! (Philosophy of knowledge since the enlightenments, at least).
    • How to translate this counter-intuitiveness into the algorithms?

oliviergeorgeon.com

the stakes semantic cognition
The stakes: semantic cognition

Reasoning and language

Rule-based systems, Ontologies,

traditional AI.

Knowledge-grounding, sense-making,

Self-programming.

Semantic cognition

Stimumuls-response adaptation

Reinforcement-learning, neural nets,

traditional machine learning.

oliviergeorgeon.com

conclusion
Conclusion
  • Think in terms of interactions
    • Rather than separating perception et action.
  • Think in terms of generated behaviors
    • Rather than in terms of learned data.
  • Keep your critical thinking
    • Invent new approaches !

http://e-ernest.blogspot.fr/

invent new approaches
Invent new approaches

“Hard problem of AI”

Formalized problem

A

E

A

Etc.

E

http://e-ernest.blogspot.fr/

exercice
Exercice

Part 4.

oliviergeorgeon.com

environnement 3 modified
Environnement 3 - modified
  • Behave like Environment 0 up to cycle 5, then like environment 1 up to cycle 10, then like environment 0 .
  • Implementation
    • If (step <= 5 or step > 10)
      • If (experiment = e1) then result = r1
      • If (experiment = e2) then result = r2
    • Else
      • If (experiment = e1) then result = r2
      • If (experiment = e2) then result = r1
    • Step++

oliviergeorgeon.com

agent 3 in environment 3
Agent 3 in Environment 3

Environnement 0

Environnement 1

Environnement 0

0. e1r1,-1,0

1. e1r1,-1,0

learn (e1r1e1r1),-2,1

activated (e1r1e1r1),-2,1

propose e1,-1

2. e2r2,1,0

learn (e1r1e2r2),0,1

3. e2r2,1,0

learn (e2r2e2r2),2,1

activated (e2r2e2r2),2,1

propose e2,1

4. e2r2,1,0

activated (e2r2e2r2),2,2

propose e2,2

5. e2r1,-1,0

learn (e2r2e2r1),0,1

6. e2r1,-1,0

learn (e2r1e2r1),-2,1

activated (e2r1e2r1),-2,1

propose e2,-1

7. e1r2,1,0

learn (e2r1e1r2),0,1

8. e1r2,1,0

learn (e1r2e1r2),2,1

activated (e1r2e1r2),2,1

propose e1,1

9. e1r2,1,0

activated (e1r2e1r2),2,2

propose e1,2

10. e1r1,-1,0

learn (e1r2e1r1),0,1

activated (e1r1e2r2),0,1

activated (e1r1e1r1),-2,1

propose e2,1

propose e1,-1

11. e2r2,1,0

activated (e2r2e2r1),0,1

activated (e2r2e2r2),2,2

propose e2,1

12. e2r2,1,0

activated (e2r2e2r1),0,1

activated (e2r2e2r2),2,3

propose e2,2

13. e2r2,1,0

oliviergeorgeon.com

principle of agent 3
Principle of Agent 3

AGENT

Activated

(i11,i11)

(it-1,it)

(i12,i12)

it-1

it

i11

Choose

e1

i12

i11

Learn

Execute

Propose

Activate

it-2

it-1

it= i11

it-4

it-3

i12

Time

PAST

FUTURE

PRESENT

oliviergeorgeon.com

environment 4
Environment 4
  • Returns result r2 only after twice the same experience.
  • e1 -> r1, e1 -> r2, e1 -> r1, e1-> r1, … e1->r1, e2 -> r1, e2->r2, e2 -> r1, …, e2 -> r1, e1 -> r1, e1 -> r2, e2 -> r1, e2 -> r2, e1 -> r1, e1 -> r2, …
  • If (experiencet-2!=experiencet &&experiencet-1==experiencet) result = r2;else result = r1;

oliviergeorgeon.com

rapport
Rapport
  • Agent 1
    • Explanation of the code.
    • Traces in environments 0 and 1 with different motivations.
    • Explanation of the behaviors.
  • Agent 2
    • Explanation of the code.
    • Traces in environments 0 to 2 with different motivations.
    • Explanation of the behaviors.
  • Agent 3
    • Explanation of the code.
    • Traces in environments 0 to 4 with différent motivations.
    • Explanation of the behaviors.
  • Conclusion
    • What would be the next step towards Agent 4 able to adapt to Environments 1 to 4?

oliviergeorgeon.com