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

5. 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

6. Cognitive Psicologhy approach:
Emergent behaviour
Definition
“The wole is greater than the
sum of its parts”
Examples
Aplication in conciousness

7. Cognitive Psicology approach:
Cognitive Architecture and behaviour

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.

10. Neurobiology approach:
Neural topologies
Linear
Grid
Encephalic

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)

13. Perception and Representation
Model for perception

14. Sense of belonging
MANN topology
SOM for nested clustering
Polynomic expression

15. Sense of belonging
Model for self-awareness
Internal representation
Affinities in three levels
Cross affinities

16. Self-awareness
Social nature
Cross inffluences
Gravity centers
Variability

17. Results
Interaction in different scenarios

18. Self-awareness
Direct and observational learning
Concepts
Direct learning
Observational learning
t1
Aplication in virtual environments
t1
t2
t2

19. Self-awareness
Self-representation and others representations
Modules
Interaction

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

21. MANN topology
MLP: self characteristics
Perceptron: others characteristics

22. Simulation. Agent interaction
Agents of different size and state
One to one interactions

23. Results
Relative weighting evolution
Relative weighting in whole value of each agent evolves as a result of
agent interactions.

24. Results
Evolution of self-representations
Self-representations become more realistic after a great number of
interactions

25. Results
Evolution of other agent reprentations
Not only self-representation but also other agent representations
evolve.

26. Self-conciousness
Temporal dimension
ANN with temporal delay
Moving window
N-steps forecast

27. Self-awareness
Temporal dimension
Cognitive arquitechture

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.