A neural networks model of self-representation for autonomous agents in competitive multi-agent systems - Milton Martínez Luaces

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A neural networks model of self-representation for autonomous agents in competitive multi-agent systems - Milton Martínez Luaces

  1. 1. Awareness in computation – University of Birmingham symposium A neural networks model of self-representation for autonomous agentsin competitive multi-gent systems Milton Martínez Luaces Polytechnic University of Madrid
  2. 2. Previous researchData Simulation, Preprocessing and Neural Networks applied to Electrochemical Noisestudies. (2006) WSEAS Transactions: Computer Science and Applications Journal, Issue4, Vol. 3. ISSN 1790-0832.A Training Methodology for Neural Networks Noise-Filtering when no Training Sets areavailable for Supervised Learning (2006) La Coruña, España. Publ: Proceedings IEEEhttp://irazu.pair.com/tjc/cimsa2006/status-accepted.phpIntelligent Virtual Environments: Operating Conditioning and Observational Learningin Agents using Neural Networks. (2006) IET 06, Atenas. IEEE.http://www2.theiet.org/oncomms/sector/computing/library.cfm?HeadingID=477Condicionamiento Operante y Aprendizaje Vicario en Agentes mediante RedesNeuronales en Entornos Virtuales Inteligentes. (2006) CLEI 06. Santiago de Chile.http://pitagoras.usach.cl/~gfelipe/clei/sesiones/sesion_7/Pdf_7/89.pdfSelf-conciousness for artificial entities using modular neural networks. (2008). Capítuloen Advanced Topics on Neural Networks. WSEAS. Ed:L. Zadeh et al. Pp. 113-118.www.worldses.org/books/2008/sofia/advanced-topics-neural-networks.pdfUsing modular neural networs to model self-consciousness and self-representation forartificial entities. (2008) International Journal of Mathematics and Computers inSimulation. NAUN, UK. Pp. 163-170.The social side and time dimension for artificial entities using modular neural networks.(2008) Neural Networks World
  3. 3. ObjectivesAnalyse consciousness modular structure andinteractions.Design a cognitive architecture for: – Self-awareness Self-representationOther individuals representations.Implement models in agents using ANN.Implement a simulator for model testing.Observe agents behaviour in different interactionscenarios.
  4. 4. Fields related with conciousness Psichologhy – Analytic approach – Emergent behaviour Neurobiologhy – Neural correlates – Modular nature of consciousness Artificial Intelligence – Computational models – Simulation
  5. 5. Cognitive Psicology approach: Analytic approach Cognitive functionsAdaptabilityAsociative memoryPersonalityLearningOptimizationAbstraction, representationPredictionGeneralization, inferenceEmotion, MotivationImaginationSense of belongingSelf awareness
  6. 6. Cognitive Psicologhy approach: Emergent behaviour Definition“The wole is greater than thesum of its parts” Examples Aplication in conciousness
  7. 7. Cognitive Psicology approach:Cognitive Architecture and behaviour
  8. 8. Cognitive Psicology approach:Self-awareness related functions Sense of belonging Self-body-consciousness Self-consciousness Self-representation Other individuals representation
  9. 9. Neurobiology approach: Neural corrrelate•Definition 1: NCC “describes neural systems and its features, related with consciousmental 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 thatsystem (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 orinconsistent. 2. Should include not only representations, but also accessand use of them.3. Models should include a temporal dimension.
  10. 10. Neurobiology approach: Neural topologiesLinearGridEncephalic
  11. 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. 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. 13. Perception and Representation Model for perception
  14. 14. Sense of belonging MANN topologySOM for nested clusteringPolynomic expression
  15. 15. Sense of belongingModel for self-awarenessInternal representationAffinities in three levelsCross affinities
  16. 16. Self-awareness Social natureCross inffluencesGravity centersVariability
  17. 17. ResultsInteraction in different scenarios
  18. 18. Self-awareness Direct and observational learningConcepts Direct learning Observational learning t1Aplication in virtual environments t1 t2 t2
  19. 19. Self-awarenessSelf-representation and others representations Modules Interaction
  20. 20. Learning processAgents learn from themselves and from other agents.Self-representations is continuosly transformed
  21. 21. MANN topologyMLP: self characteristicsPerceptron: others characteristics
  22. 22. Simulation. Agent interactionAgents of different size and stateOne to one interactions
  23. 23. Results Relative weighting evolutionRelative weighting in whole value of each agent evolves as a result ofagent interactions.
  24. 24. Results Evolution of self-representationsSelf-representations become more realistic after a great number ofinteractions
  25. 25. Results Evolution of other agent reprentationsNot only self-representation but also other agent representationsevolve.
  26. 26. Self-conciousness Temporal dimensionANN with temporal delay Moving window N-steps forecast
  27. 27. Self-awareness Temporal dimensionCognitive arquitechture
  28. 28. ConclusionsMANN for self-awarenessMANN suitable for models related with conciousnessInteraction between MANN as a correlate of cognitive funcion interactionsMulti agent systems prefereable to isolated agent simulationsSelf-awareness as a specialization of the sense of belongingMANN models integrating self-awareness with sense of belongingIntegrate self-awareness with other agent awarenessIntegrate self-representation and group-representation
  29. 29. ConclusionsLearning self-awareness modelsDynamic self-representation instead of static one.Self-awareness based in social interaction.Direct and observational learning.Temporal dimension of self-awareness
  30. 30. Conclusions Future research linesSelf-awareness: relation with other cognitivefunctions.Variability of self-representationInfluence of temporal self-representation in perception.

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