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Consciousness and C ognition

Cognitive Architectures. Consciousness and C ognition. Janusz A. Starzyk. Motivated Learning. Definition: Motivated learning (ML) is pain based motivation, goal creation and learning in embodied agent. Machine creates abstract goals based on the primitive pain signals.

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Consciousness and C ognition

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  1. Cognitive Architectures Consciousness and Cognition Janusz A. Starzyk

  2. Motivated Learning • Definition: Motivated learning (ML) is pain based motivation, goal creation and learning in embodied agent. • Machine creates abstract goals based on the primitive pain signals. • It receives internal rewards for satisfying its goals (both primitive and abstract). • ML applies to EI working in a hostile environment. • Various pains and external signals compete for attention. • Attention switching results from competition. • Cognitive perception is aided by winner of competition.

  3. faucet refill garbage sit on water w. can tank open - + Dual pain Pain Primitive level Dry soil Primitive Goal Creation • Reinforcing a proper action

  4. Motor pathway (action, reaction) Sensory pathway (perception, sense) tank refill Level III - + faucet open Level II - + w. can water Level I - + Activation Stimulation Primitive Level Dry soil Inhibition Reinforcement Echo Need Expectation Abstract Goal Hierarchy • Abstract goals are created to reduce abstract pains and to satisfy the primitive goals • A hierarchy of abstract goalsis created to satisfy the lower level goals

  5. Goal Creation Experiment in ML

  6. Goal Creation Experiment in ML Pain signals in CGS simulation

  7. Goal Creation Experiment in ML Action scatters in 5 CGS simulations

  8. Goal Creation Experiment in ML The average pain signals in 100 CGS simulations

  9. Goal Creation Experiment in ML Comparison between GCS and RL

  10. Mean primitive pain Pp value as a function of the number of iterations: • - green line for TDF • blue line for GCS. • Primitive pain ratio with pain threshold 0.1 Compare RL (TDF) and ML (GCS)

  11. Problem solved Compare RL (TDF) and ML (GCS) • Comparison of execution time on log-log scale • TD-Falcon green • GCS blue • Combined efficiency of GCS 1000 better than TDF Conclusion: embodied intelligence, with motivated learning based on goal creation system, effectively integrates environment modeling and decision making – thus it is poised to cross the chasm

  12. Reinforcement Learning Motivated Learning Single value function Various objectives Measurable rewards Predictable Objectives set by designer Maximizes the reward Potentially unstable Action depends on the state of the environment Learning effort increases with complexity Always active Multiple value functions One for each goal Internal rewards Unpredictable Sets its own objectives Solves minimax problem Always stable Action depends on the states of the environment and agent Learns better in complex environment than RL Acts when needed http://www.bradfordvts.co.uk/images/goal.jpg

  13. Primitive needs Water Reservoir Abstract Needs Wash in Water Drink Water Irrigate Dirty Thirsty Drought Primitive Needs

  14. Abstract needs Well Public Money Spend Money to Buy Draw own Water Spend Money to Build Water Reservoir Abstract Needs Wash in Water Drink Water Irrigate Dirty Thirsty Drought Primitive Needs

  15. Abstract needs Tourists' Attractions Ground Water Well Building Wealthy Taxpayers Build Ecotourism Dig a Well Rise Taxes Water Supply Well Public Money Build Water Recreation Spend Money to Buy Draw own Water Spend Money to Build Water Reservoir Abstract Needs Wash in Water Drink Water Irrigate Dirty Thirsty Drought Primitive Needs

  16. Abstract needs Management Planning Policy Resource Management and Planning Regulate Use Receive Salary Employment Opportunities Develop Infrastructure Tourists' Attractions Ground Water Well Building Wealthy Taxpayers Build Ecotourism Dig a Well Rise Taxes Water Supply Well Public Money Build Water Recreation Spend Money to Buy Draw own Water Spend Money to Build Water Reservoir Abstract Needs Wash in Water Drink Water Irrigate Dirty Thirsty Drought Primitive Needs

  17. Definition of Machine Consciousness Consciousness is attention drivencognitive perceptionmotivations, thoughts, plans and action monitoring. A machine is conscious IFF besides ability to perceive, act, learn and remember, it has a central executive mechanism that controls all the processes (conscious or subconscious) of the machine; http://hplusmagazine.com/sites/default Photo: www.spectrum.ieee.org/.../biorobot11f-thumb.jpg

  18. Consciousness: functional requirements • Intelligence • Central executive • Attention and attentionswitching • Mental saccades • Cognitive perception • Cognitive action control Photo: http://eduspaces.net/csessums/weblog/11712.html http://faculty.virginia.edu/consciousness

  19. Computational Model of Machine Consciousness Inspiration: human brain Photo (brain): http://www.scholarpedia.org/article/Neuronal_correlates_of_consciousness Central Executive Episodic Memory & Learning Attention switching Action monitoring Emotions, rewards, and sub-cortical processing Planning and thinking Queuing and organization of episodes Motivation and goal processor Episodic memory Sensory-motor Semantic memory Motor skills Motor processors Sensory processors Data encoders/ decoders Data encoders/ decoders Motor units Sensory units

  20. Sensory and Motor Hierarchies • Sensory and motor systems appear to be arranged in hierarchies with information flowing between each level of the sensory and motor hierarchies. 20

  21. Emotions, rewards, and sub-cortical processing Sensory-motor Sensory- Motor Block http://www.ourbabynews.com/wp-content • sensory processors integrated with semantic memory • motor processors integrated with motor skills • sub-cortical processors integrated with emotions and rewards Semantic memory Motor skills Motor processors Sensory processors Data encoders/ decoders Data encoders/ decoders Motor units Sensory units

  22. Central Executive • Platform for the emergence of consciousness • Controls its conscious and subconscious processes • Is driven by • attention switching • learning mechanism • creation and selection of motivations and goals http://www.unifesp.br/dpsicobio/eventos/workingmemory/ ahsmail.uwaterloo.ca/kin356/cexec/cexec.htm

  23. Central Executive Central Executive Attention switching Action monitoring • Tasks • cognitive perception • attention • attention switching • motivation • goal creation and selection • thoughts • planning • learning, etc. http://prodinstres.pbworks.com Planning and thinking Motivation and goal processor

  24. Central Executive Central Executive Attention switching Action monitoring • Interacts with other units for • performing its tasks • gathering data • giving directions to other units • No clearly identified decision center • Decisions are influenced by • competing signals representing • motivations, pains, desires, plans, and interrupt signals • need not be cognitive or consciously realized • competition can be interrupted by attention switching signal http://www.resourceroom.net/ Planning and thinking Motivation and goal processor

  25. Attention Switching !!! http://www.mukyaa.com • Attention • is a selective process of cognitive perception, action and other cognitive experiences like thoughts, action planning, expectations, dreams • Attention switching • is needed to have a cognitive • experience • leads to sequences of cognitive experiences http://brandirons.com/ Comic: http://lonewolflibrarian.wordpress.com/2009/08/05/attention-and-distraction-what-are-you-paying-attention-to-08-05-09/

  26. Attention Switching !!! • Dynamic process resulting from competition between • representations related to motivations • sensory inputs • internal thoughts including spurious signals (like noise). blog.gigoo.org/.../ http://www.cs.miami.edu

  27. Attention Switching !!! • May be a result of : • deliberate cognitive experience (and thusfully conscious signal) • subconsciousprocess (stimulated by internal or external signals) • Thus, while paying attention is a conscious experience, • switching attention does not have to be.

  28. Advancement of a goal? Simplified Cognitive Machine Yes No Formulate episode Associative memory Saccade control Write to episodic memory Loop 1 Changing motivation Attention spotlight Action control Changing perception From virtual game Loop 2 Changing environment

  29. Visual Saccades What Where C C D D A A B B C D A Input image B

  30. Mental Saccades • Selected part of the image resulting from an eye saccade. • Perceived input activates object recognition and associated areas of semantic and episodic memory. • This in turn activates memory traces in the global workspace area that will be used for mental searches (mental saccades).

  31. Mental saccades in a conscious machine Advancement of a goal? Continue search? Plan action? Action? No No Advancement Advancement Attention spotlight Attention spotlight of a goal? of a goal? Yes Yes Loop 1 Loop 1 Mental saccades Mental saccades Learning Learning Yes Yes Continue Continue Changing motivation Changing motivation search? search? No No Loop 3 Loop 3 Associative memory Associative memory Plan action? Plan action? Yes Yes Loop 2 Loop 2 No No No No Action? Action? Changing perception Changing perception Perceptual saccades Perceptual saccades Loop 4 Loop 4 Yes Yes Action control Action control Changing environment Changing environment http://cdn-3.lifehack.org/wp-content Loop 5 Loop 5

  32. Comprehensive Cognitive Model • Proposed cognitive system organization • Contains • Semantic, episodic and procedural memories. • WTA attention switching • Visual and mental saccades • Scene building • Action planning • And more… • Figure represents our top-level design model

  33. Computational Model: Summary • Self-organizing mechanism of emerging motivations and other signals competing for attention is fundamental for conscious machines. • A central executive controls conscious and subconscious processes driven by its attention switching mechanism. • Attention switching is a dynamic process resulting from competition between representations, sensory inputs and internal thoughts • Mental saccades of the working memory are fundamental for cognitive thinking, attention switching, planning, and action monitoring Photo: http://www.prlog.org/10313829-homeless-man-earns-250000-after-viewing-prosperity-consciousness-video-subliminal-mind-training.html

  34. Computational Model: Implications • Motivations for actions are physically distributed • competing signals are generated in various parts of machine’s mind • Before a winner is selected, machine does not interpret the meaning of the competing signals • Cognitive processing is predominantly sequential • winner of the internal competition is an instantaneous director of the cognitive thought process, before it is replaced by another winner • Top down activation for perception, planning, internal thought or motor functions • results in conscious experience • decision of what is observed and where is it • planning how to respond • a train of such experiences constitutes consciousness

  35. NeoAxis Simulation Neoaxis Implementation VIDEO

  36. Conclusions • Consciousness is computational • Intelligent machines can be conscious

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