Prsentation PowerPoint
Developmental Artificial Intelligence27 March
[email protected]://www.oliviergeorgeon.com
toliviergeorgeon.com1/291OutlineOrganization in time and
space.Formalism for Spatio-temporal coupling.Cognitive
architecture.DemonstrationsExerciseImplement your self-programming
agent (follow up).
oliviergeorgeon.com2/292Reminder of theoretical ideasThe
objective is to learn (discover, organize and exploit) regularities
of interaction in time and space to satisfy innate criteria
(survival, curiosity, etc.).
To autonomously construct an ontology of reality. From
experience of interactionDetect and respond to increasingly
sophisticated affordances(self-programming).oliviergeorgeon.com
3/293ProposeTrace-Based ReasoningTimeHierarchical
abstractionMatchingSelectTry to EnactMore
abstractionoliviergeorgeon.com4/29Active self-motivated
hierarchical abstraction of sequence.
4Example 2
oliviergeorgeon.com5/295Examples of learned behaviors
oliviergeorgeon.com6/296
Example 2oliviergeorgeon.com7/297Spatio-sequential regularity
learningoliviergeorgeon.com8/298Concept of affordanceProperty of an
object or an environment that allows an individual to perform an
action.To afford an action. objects push us into doing (Heinz
Werner)Affordances are properties of the coupling between the agent
and the environment.We know the world in terms of possibilities of
interaction.
oliviergeorgeon.com9/29FormalismeAgentEnvironmentObservationActionTraditionnal
formalization O AAgentEnvironmentExprienceRsultatLearning by
experiencing R
Eoliviergeorgeon.com10/29AgentEnvironmentIntendedInteractionEnactedInteraction
I IRadical Interactionism:I = E RX: Interactions compositesHow to
learn the existence of possibly persistent entities in the
environment?How to adapt to different categories of entities?
Spatial Radical InteractionismAgentEnvironmentIntended
Interaction i IEnacted Interaction e
Ioliviergeorgeon.com11/29Spatial position Spatial transformation
represents the vestibular system. Can be implemented through an
accelerometer. represents position information (eye convergence,
interaural phase difference, etc.)
Spatial exemple 1oliviergeorgeon.com
AgentEnvironmentEnacted interaction:..
Spatial position = (1,0)Spatial transformation = (0,0)
Intendedinteraction12/29AgentEnvironment
Environment known at time tdecd Xdicd Xdep1 ip1 ipj Iepj
IDecisional mechanism Recursivity problemHow to maintain
recursivity ?oliviergeorgeon.comSpatial position Spatial
transformation 13/29Spatial Exemple 1
oliviergeorgeon.com14/29Enactive Cognitive Architecture
The agent programs itself through experience of interaction.The
architecture does not program itself.(Kantian space, e.g., Buzsaki
2013, Space, time, and memory)oliviergeorgeon.com15/29Inspiration
from neurosciencesoliviergeorgeon.com
16/29More inspiration from neurosciences?oliviergeorgeon.com
Cotterill R. (2001). Progress in Neurobiology.17/29ECA agent
exemple 2
oliviergeorgeon.com18/29From drives to goals
AffordedSimulatedoliviergeorgeon.com19/29Exercise
3oliviergeorgeon.com20/2920ExerciseTwo possible experiences E =
{e1,e2}Two possible results R = {r1,r2} Four possible interactions
E x R = {i11, i12, i21, i22} Two environmentsenvironment0: e1 ->
r1 , e2 -> r2 (i12 et i21 are never enacted)Environment1: e1
-> r2 , e2 -> r1 (i11 et i22 are never enacted)Environment2 :
e1 -> r2 , e2 -> r2Environment3Motivational
systems:motivation0: v(i12) = v(i22) = 1, v(i11) = v(i21) =
-1motivation1: etc.Implement un agent that learn to enact positive
interactions without knowing its motivatins and its environnement a
priori.Write a rapport of behavioral analysis based on activity
traces.
oliviergeorgeon.com21/29Implmentationpublic 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 Agent3(); // Agent1(); Agent2();
Environnement env = new Env3(); // Env1(); // Env2();for(int i=0 ;
i < 10 ; i++)e = agent.chooseExperience(r);r =
env.giveResult(e); System.out.println(e, r, value);Class AgentClass
Agent3public Experience chooseExperience(Result r)Class
EnvironnementClass Env3public Result giveResult(experience e)Class
ExperienceClass ResultClass Interaction(experience, result,
value)public int getValue()
oliviergeorgeon.com22/2922Analyse de traces
dactivit.oliviergeorgeon.come1,r2,-1e2,r1,1e2,r1,1e2,r1,1e2,r1,1e2,r1,1e2,r1,1e2,r1,1e2,r1,1e2,r1,1e1,r1,1e1,r1,1e1,r1,1e1,r1,1e1,r1,1e1,r1,1e1,r1,1e1,r1,1e1,r1,1e1,r1,1Motivation1,
Environnement 0.Motivation1, Environnement 1.Motivation1,
Environnement 2.e1,r2,1 learn e1r1-e1r2,0e1,r1,-1 learn
e1r2-e1r1,0e1,r1,-1 learn e1r1-e1r1,-2e2,r2,1 learn
e1r1-e2r2,0e2,r1,-1 learn e2r2-e2r1,0e2,r1,-1 learn
e2r1-e2r1,-2e1,r2,1 learn e2r1-e1r2,0e2,r2,1 learn
e1r2-e2r2,2e1,r2,1 learn e2r2-e1r2,2e2,r2,123/2923Environnement
3Behaves like Environnement0 during the first 10 cycles, then like
environnement1. ImplmentationIf (step < 10)If (experiment = e1)
then result = r1If (experiment = e2) then result = r2ElseIf
(experiment = e1) then result = r2If (experiment = e2) then result
= r1Step++
oliviergeorgeon.com24/2924TempsActivatedi11Proposei11it-3it-2it-4it-1it
=
i11i11PRESENTFUTURPASTlearnAGENTit(it-1,it)Activateit-1oliviergeorgeon.comPrinciple
of Agent 3(i11,i12)i12e1ChooseExecute(i11i11)25/2925Implementation
of Agent 2At the end of time step tRecord or reinforce ic = it-1,
it, w = pre(ic), post(ic), weight If ic already belongs to the set
of existing interactions ItWeight ++.At the beginning of time step
tConstruct the list of activated composite interactions AtAt= { i
It | pre(i) = it-1 }For each activated composite interaction in
AtCreate a proposition for post(ic).experience with proclivity
ic.weight * post(ic).valenceFor each experience, sum up the
proclivity of all its propositions.Choose the experience that has
the highest total proclivity.
oliviergeorgeon.com26/2926Class InteractionClass
Interaction//attributes: Experience experience;Result result;int
value;String label;Interaction preInteraction;Interaction
postInteraction;int
weight;oliviergeorgeon.comPostInteractoinComposite Interaction
weightpreInteraction27/2927Decision
mechanismoliviergeorgeon.comList propositions = new ArrayList();
for (Interaction activatedInteraction :
getActivatedInteractions()){Proposition proposition = new
Proposition(activatedInteraction.getPostInteraction().getExperience(),
activatedInteraction.getWeight() *
activatedInteraction.getPostInteraction().getValence());int index =
propositions.indexOf(proposition);if (index <
0)propositions.add(proposition);elsepropositions.get(index).addProclivity(activatedInteraction.getWeight()
*
activatedInteraction.getPostInteraction().getValence());}Collections.sort(propositions);If
(propositions.size()> 0)proposedExperience =
propositions.get(0).getExperience();28/2928Class PropositionClass
Proposition implements Comparable// attributs:Experience
experienceInt proclivity// constructorProposition(Experience
experience, int Proclivity)// methodsint compareTo(Proposition
proposition)return new
Integer(proposition.getProclivity()).compareTo(proclivity);boolean
equals(Object otherProposition)return
((Proposition)otherProposition).getExperience() ==
this.experience;void addProclivity(int proclivity)this.proclivity
+= proclivity;oliviergeorgeon.com29/2929
