Understanding Consciousness with Model Abstractions Firmo Freire Paris, Juillet 9, 2007

LIP6-09/07/20072 Agenda Introduction Function of Consciousness Control Systems Fundamentals Internal Models The Simulator Experiment and Results Related Work Future Work Conclusions References

LIP6-09/07/20073 Introduction This presentation is about: –A software platform for research into Artificial Consciousness; –The conceptual foundation for this research.

LIP6-09/07/20074 What Is Consciousness? It is perhaps too early to try and define consciousness. A better course would be to try to understand its added value to behavior and then try to define consciousness as consequence of this understanding. Consciousness must be have physical (direct or indirect) influence(s) on the environment, otherwise it would not be detectable by evolution and selected as a trait for survival.

LIP6-09/07/20075 A Function of Consciousness Consciousness allows for flexibility of action/behavior How can the brain make successful limb and body movements? Environmental conditions are constantly changing and have to be adapted to.

LIP6-09/07/20076 Control Systems Concepts (1/6) Open Loop Control (Feed Forward Control Systems)

LIP6-09/07/20077 Control Systems Concepts (2/6) Closed Loop Control (Feedback Control Systems)

LIP6-09/07/20078 Control Systems Concepts (3/6) System Identification -Black-boxes -Gray-boxes, and -White-boxes

LIP6-09/07/20079 Control Systems Concepts (4/6) Model Predictive Control

LIP6-09/07/ Control Systems Concepts (5/6) Model Predictive Control Dynamics

LIP6-09/07/ Control Systems Concepts (6/6) Forward and Inverse Models

LIP6-09/07/ Internal Models (1/3) Delay in Closed Loop Systems

LIP6-09/07/ Internal Models (2/3) Plant Model in the Control Loop

LIP6-09/07/ Internal Models (3/3) Benefits –Feedback control –Anomaly detection –Anticipation Comparison with other techniques –Flexibility

LIP6-09/07/ The Simulator (1/4 ) Simulator Structure

LIP6-09/07/ The Simulator (2/4) Environment Structure

LIP6-09/07/ The Simulator (3/4) Agent Cognitive Structure

LIP6-09/07/ The Simulator (4/4) Internal Model General States for Skills

LIP6-09/07/ Experiments and Results (1/5) Test of Stop Model

LIP6-09/07/ Experiments and Results (2/5) Test of Car-Following Model

LIP6-09/07/ Experiments and Results (3/5) Car Following Zoom

LIP6-09/07/ Experiments and Results (4/5) Car Following Dynamics

LIP6-09/07/ Experiments and Results (5/5) Choosing Between Conflicting Alternatives The next experiment will include a higher level Internal Model that will deal with potentially conflicting situations. For example the agent is near his destination but has a slow moving car in front of him. If the agent overtakes the leading car he runs the risk of overshooting his destination. This would be an example in that a frustrating (negative) experience would have to be endured in order to achieve a greater good.

LIP6-09/07/ Related Work Owen Holland(Holland and Goodman 2003) in the paper Robots With Internal Models

LIP6-09/07/ Future Work Simulator as a Framework (MAS) 3D Graphical Interface Model Refining Model Implementation Technology Time Considerations: Real Time and Synchronisms Learning Features and Mechanisms

LIP6-09/07/ Conclusions Promising approach to the study of cognitive processes in general and Artificial Consciousness in particular. A simulator architecture that can grow and expand to a multiprocessing environment, thus affording greatly enhanced computing power. If various functions attributed to consciousness can be unequivocally be implemented then it comes down to: a) These functions do not need consciousness to steer behavior, or b) The machine is exhibiting some level of consciousness within the domain of the simulated environment.

LIP6-09/07/ References Churchland, P.S. (2002), “Brain_Wise: Studies in Neurophilosophy, The MIT Press, pages Damasio, A. (1994), “Descartes Error: Emotion, Reason, and the Human Brain. New York: Grossett/Putnan. Damasio, A. (1999), “A Feeling of What Happens”. New York: Harcourt Brace. Grush, R. (1997), “The Architecture of Representation”, in Philosophical Psychology 10:5-23. Iacoboni M.,Monar-Szakacs, Gallese V., Buccino G., and Mazziotta J.C. (2005), “Grasping the Intentions of Others with One’s Own Mirror System”, in PloS Biology (

LIP6-09/07/ References (Cont.) Gaschler K. (2006), “One Person, One Neuron?”, Scientific American Mind (February/March), pp: Pouget, A., and T.J. Sejnowski (1997), “Spatial Transformations in the Parietal Cortex Using Basis Functions”, Journal of Cognitive Neural Science 9(2): Rizzolatti G., Fogassi L. and Gallese V. (2006), “Mirrors in the Mind”, Scientific American (November), pp: Sloman, A. (2004), “GC5 The Architecture of Brain and Mind”, in Grang Challenges in Computing – Research, edited by Tony Hoare and Robin Milner, BCS, 21, 24. Wolpert, D.M., Z. Ghahramani, and M.I. Jordan (1995), “An Internal Model for Sensorimotor Integration”, in Science 269: