A neural networks model of self-representation

for autonomous agents in competitive multi-gent systems

Milton Martínez Luaces

Polytechnic University of Madrid

Awareness in computation – University of Birmingham symposium

Previous research

�� Data Simulation, Preprocessing and Neural Networks applied to ElData Simulation, Preprocessing and Neural Networks applied to Electrochemical Noise ectrochemical Noise

studiesstudies. (2006) WSEAS Transactions: Computer Science and Applications J. (2006) WSEAS Transactions: Computer Science and Applications Journal, Issue ournal, Issue

4, Vol. 3. ISSN 17904, Vol. 3. ISSN 1790--0832. 0832.

�� A Training Methodology for Neural Networks NoiseA Training Methodology for Neural Networks Noise--Filtering when no Training Sets are Filtering when no Training Sets are

available for Supervised Learningavailable for Supervised Learning (2006) La Coru(2006) La Coruñña, Espaa, Españñaa. Publ: Proceedings IEEE . Publ: Proceedings IEEE

http://irazu.pair.com/tjc/cimsa2006/statushttp://irazu.pair.com/tjc/cimsa2006/status--accepted.phpaccepted.php

�� Intelligent Virtual Environments: Operating Conditioning and ObsIntelligent Virtual Environments: Operating Conditioning and Observational Learning ervational Learning

in Agents using Neural Networks.in Agents using Neural Networks. (2006) IET 06, Atenas. IEEE (2006) IET 06, Atenas. IEEE

..http://www2.theiet.org/oncomms/sector/computing/library.cfm?Headhttp://www2.theiet.org/oncomms/sector/computing/library.cfm?HeadingID=477ingID=477

�� Condicionamiento Operante y Aprendizaje Vicario en Agentes mediaCondicionamiento Operante y Aprendizaje Vicario en Agentes mediante Redes nte Redes

Neuronales en Entornos Virtuales Inteligentes.Neuronales en Entornos Virtuales Inteligentes. (2006) CLEI 06. Santiago de Chile. (2006) CLEI 06. Santiago de Chile.

http://pitagoras.usach.cl/~gfelipe/clei/sesiones/sesion_7/Pdf_7/http://pitagoras.usach.cl/~gfelipe/clei/sesiones/sesion_7/Pdf_7/89.pdf89.pdf

�� SelfSelf--conciousness for artificial entities using modular neural networconciousness for artificial entities using modular neural networks. (2008). Capks. (2008). Capíítulo tulo

en Advanced Topics on Neural Networks. WSEAS. Ed:L. Zadeh et al.en Advanced Topics on Neural Networks. WSEAS. Ed:L. Zadeh et al. Pp. 113Pp. 113--118. 118.

www.worldses.org/books/2008/sofia/advancedwww.worldses.org/books/2008/sofia/advanced--topicstopics--neuralneural--networks.pdfnetworks.pdf

�� Using modular neural networs to model selfUsing modular neural networs to model self--consciousness and selfconsciousness and self--representation for representation for

artificial entities. (2008) International Journal of Mathematicsartificial entities. (2008) International Journal of Mathematics and Computers in and Computers in

Simulation. NAUN, UK. Pp. 163Simulation. NAUN, UK. Pp. 163--170.170.

�� The social side and time dimension for artificial entities usingThe social side and time dimension for artificial entities using modular neural networks. modular neural networks.

(2008) Neural Networks World(2008) Neural Networks World

Objectives

�� Analyse consciousness modular structure and Analyse consciousness modular structure and interactions.interactions.

�� Design a cognitive architecture for:Design a cognitive architecture for:

–– SelfSelf--awarenessawareness

SelfSelf--representationrepresentation

�� Other individuals representations.Other individuals representations.

�� Implement models in agents using ANN.Implement models in agents using ANN.

�� Implement a simulator for model testing.Implement a simulator for model testing.

�� Observe agents behaviour in different interaction Observe agents behaviour in different interaction scenarios.scenarios.

Fields related with conciousness

�� PsichologhyPsichologhy

–– Analytic approachAnalytic approach

–– Emergent behaviourEmergent behaviour

�� NeurobiologhyNeurobiologhy

–– Neural correlatesNeural correlates

–– Modular nature of consciousnessModular nature of consciousness

�� Artificial IntelligenceArtificial Intelligence

–– Computational modelsComputational models

–– SimulationSimulation

Cognitive Psicology approach:

Analytic approach

Cognitive functions

� Adaptability� Asociative memory� Personality� Learning� Optimization� Abstraction, representation� Prediction� Generalization, inference� Emotion, Motivation� Imagination� Sense of belonging� Self awareness

Cognitive Psicologhy approach:

Emergent behaviour

�� Definition

“The wole is greater than the sum of its parts”

� Examples

� Aplication in conciousness

Cognitive Psicology approach:

Cognitive Architecture and behaviour

Cognitive Psicology approach:

Self-awareness related functions

�� Sense of belongingSense of belonging

�� SelfSelf--bodybody--consciousnessconsciousness

�� SelfSelf--consciousnessconsciousness

�� SelfSelf--representationrepresentation

�� Other individuals representationOther individuals representation

Neurobiology approach:

Neural corrrelate•

Definition 1: NCC “describes neural systems and its features, related with conscious

mental states". (Fell, 2004)�

•

¿A NCC really exists? Different viewpoints. Correlation (1-1) (1-n)�

•

Definition 2: “a neural correlate is a neural system (S) plus a certain state of that

system (NS), that are correlated with a particular state of conciousness (C)” (Decity,

2003). NCC = S + NS(t) | NS(t) correl C(t)

•

Goals :

1. Models need not to be exhaustive but never contradictory or

inconsistent. 2. Should include not only representations, but also access

and use of them.

3. Models should include a temporal dimension.

Neurobiology approach:

Neural topologies

�� LinearLinear

�� GridGrid

�� EncephalicEncephalic

Artificial intelligence approach:

Modular Artificial Neural Networks Structures

�� CompetitivesCompetitives� Voting (suitable i.e. for clasification).

� Average (suitable i.e. for regression).

� Weighted average

� PCA Regresions

� Discriminant analysis

�� ColaborativesColaboratives

Modular Artificial Neural Networks Training

� Sampling

� Many objective functions

� Search space splitting

� Divide responsabilites

0

10

20

30

40

50

60

70

80

90

100

1 2 3 4 5 6

Epochs (hundreds)

MS

E

BackProp BackProp w ith Momentum Conjugated Gradient

BP

BP with

Mom

CG

Perception and RepresentationModel for perception

Sense of belongingMANN topology

�� SOM for nested clustering SOM for nested clustering

�� Polynomic expression Polynomic expression

Sense of belonging

�� Model for selfModel for self--awarenessawareness ��

�� Internal representationInternal representation

�� Affinities in three levelsAffinities in three levels

�� Cross affinitiesCross affinities

Self-awarenessSocial nature

� Cross inffluences

� Gravity centers

� Variability

�� Interaction in different scenariosInteraction in different scenarios

Results

Self-awarenessDirect and observational learning

�� ConceptsConcepts

�� Direct learningDirect learning

�� Observational learningObservational learning

�� Aplication in virtual environmentsAplication in virtual environments

t

1t

1t

2

t2

Self-awarenessSelf-representation and others representations

�� ModulesModules

�� Interaction Interaction

Learning process

Agents learn from themselves and from other agents. Self-representations is continuosly transformed

MANN topology

�� MLP: self characteristicsMLP: self characteristics

�� Perceptron: others characteristicsPerceptron: others characteristics

Simulation. Agent interaction

�� Agents of different size and stateAgents of different size and state

�� One to one interactionsOne to one interactions

ResultsRelative weighting evolution

�� Relative weighting in whole value of each agent evolves as a resRelative weighting in whole value of each agent evolves as a result of ult of agent interactions.agent interactions.

ResultsEvolution of self-representations

�� SelfSelf--representations become more realistic after a great number of representations become more realistic after a great number of interactionsinteractions

ResultsEvolution of other agent reprentations

�� Not only selfNot only self--representation but also other agent representations representation but also other agent representations evolve.evolve.

Self-conciousnessTemporal dimension

�� ANN with temporal delay ANN with temporal delay

�� Moving windowMoving window

�� NN--steps forecaststeps forecast

Self-awarenessTemporal dimension

�� Cognitive arquitechtureCognitive arquitechture

Conclusions

� MANN for self-awareness

� MANN suitable for models related with conciousness

� Interaction between MANN as a correlate of cognitive funcion interactions

� Multi agent systems prefereable to isolated agent simulations

� Self-awareness as a specialization of the sense of belonging

� MANN models integrating self-awareness with sense of belonging

� Integrate self-awareness with other agent awareness

� Integrate self-representation and group-representation

Conclusions

� Learning self-awareness models

� Dynamic self-representation instead of static one.

� Self-awareness based in social interaction.

� Direct and observational learning.

� Temporal dimension of self-awareness

ConclusionsFuture research lines

� Self-awareness: relation with other cognitivefunctions.

� Variability of self-representation

� Influence of temporal self-representation in perception.

�