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A neural networks model of self-representation for autonomous agents in competitive multi-agent systems - Milton Martínez Luaces
1. Awareness in computation – University of Birmingham symposium
A neural networks model
of self-representation
for autonomous agents
in competitive multi-gent systems
Milton Martínez Luaces
Polytechnic University of Madrid
2. Previous research
Data Simulation, Preprocessing and Neural Networks applied to Electrochemical Noise
studies. (2006) WSEAS Transactions: Computer Science and Applications Journal, Issue
4, Vol. 3. ISSN 1790-0832.
A Training Methodology for Neural Networks Noise-Filtering when no Training Sets are
available for Supervised Learning (2006) La Coruña, España. Publ: Proceedings IEEE
http://irazu.pair.com/tjc/cimsa2006/status-accepted.php
Intelligent Virtual Environments: Operating Conditioning and Observational Learning
in Agents using Neural Networks. (2006) IET 06, Atenas. IEEE
.http://www2.theiet.org/oncomms/sector/computing/library.cfm?HeadingID=477
Condicionamiento Operante y Aprendizaje Vicario en Agentes mediante Redes
Neuronales en Entornos Virtuales Inteligentes. (2006) CLEI 06. Santiago de Chile.
http://pitagoras.usach.cl/~gfelipe/clei/sesiones/sesion_7/Pdf_7/89.pdf
Self-conciousness for artificial entities using modular neural networks. (2008). Capítulo
en Advanced Topics on Neural Networks. WSEAS. Ed:L. Zadeh et al. Pp. 113-118.
www.worldses.org/books/2008/sofia/advanced-topics-neural-networks.pdf
Using modular neural networs to model self-consciousness and self-representation for
artificial entities. (2008) International Journal of Mathematics and Computers in
Simulation. NAUN, UK. Pp. 163-170.
The social side and time dimension for artificial entities using modular neural networks.
(2008) Neural Networks World
3. Objectives
Analyse consciousness modular structure and
interactions.
Design a cognitive architecture for:
– Self-awareness
Self-representation
Other individuals representations.
Implement models in agents using ANN.
Implement a simulator for model testing.
Observe agents behaviour in different interaction
scenarios.
4. Fields related with conciousness
Psichologhy
– Analytic approach
– Emergent behaviour
Neurobiologhy
– Neural correlates
– Modular nature of consciousness
Artificial Intelligence
– Computational models
– Simulation
8. Cognitive Psicology approach:
Self-awareness related functions
Sense of belonging
Self-body-consciousness
Self-consciousness
Self-representation
Other individuals representation
9. 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.
11. Artificial intelligence approach:
Modular Artificial Neural Networks
Structures
Competitives
Voting (suitable i.e. for clasification).
Average (suitable i.e. for regression).
Weighted average
PCA Regresions
Discriminant analysis
Colaboratives
12. Modular Artificial Neural Networks
Training
Sampling
Many objective functions
Search space splitting
Divide responsabilites 100
BackProp
90 BP BackProp w ith Momentum
Conjugated Gradient
80
70
60 BP with
Mom
MSE
50
CG
40
30
20
10
0
1 2 3 4 5 6
Epochs (hundreds)
28. 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
29. 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
30. Conclusions
Future research lines
Self-awareness: relation with other cognitivefunctions.
Variability of self-representation
Influence of temporal self-representation in perception.