1 Consciousness and Cognition Janusz A. Starzyk Cognitive Architectures

Motivated Learning  Various pains and external signals compete for attention.  Attention switching results from competition.  Cognitive perception is aided by winner of competition.  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.

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

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

MOTOR FUNCTION SENSOR OBJECTREDUCES PAININCREASES PAIN EatFoodHungerLack of Food Buy Food at Grocery StoreLack of FoodLack of Money Withdraw fromBank Account Lack of MoneyOverdrawn Account Work in The officeOverdrawn AccountLack of job opportunities Study at SchoolLack of job opportunities - Play with Toys-- Goal Creation Experiment in ML

Pain signals in CGS simulation

Goal Creation Experiment in ML Action scatters in 5 CGS simulations

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

Goal Creation Experiment in ML Comparison between GCS and RL

Compare RL (TDF) and ML (GCS) Mean primitive pain P p 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

 Comparison of execution time on log-log scale  TD-Falcon green  GCS blue  Combined efficiency of GCS 1000 better than TDF Compare RL (TDF) and ML (GCS) Problem solved 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

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

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

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

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

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

Definition of Machine Consciousness Consciousness is attention driven cognitive perception motivations, 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; Photo:

Photo: Consciousness: functional requirements  Intelligence  Central executive  Attention and attention switching  Mental saccades  Cognitive perception  Cognitive action control

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

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.

Sensory- Motor Block Semantic memory Sensory processors Data encoders/ decoders Sensory units Motor skills Motor processors Data encoders/ decoders Motor units Emotions, rewards, and sub-cortical processing Sensory-motor  sensory processors integrated with semantic memory  motor processors integrated with motor skills  sub-cortical processors integrated with emotions and rewards

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 ahsmail.uwaterloo.ca/kin356/cexec/cexec.htm

Attention switching Action monitoring Motivation and goal processor Planning and thinking Central Executive  Tasks o cognitive perception o attention o attention switching o motivation o goal creation and selection o thoughts o planning o learning, etc. Central Executive

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

 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 Comic: Attention Switching !!!

Attention Switching !!!  Dynamic process resulting from competition between representations related to motivations sensory inputs internal thoughts including spurious signals (like noise). blog.gigoo.org/.../

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

Formulate episode Saccade control Changing perception Changing environment Advancement of a goal? YesNo Action control Changing motivation Write to episodic memory Loop 1 Loop 2 Attention spotlight Associative memory From virtual game Simplified Cognitive Machine

Input image A B C D A B C D A B C D WhatWhere Visual Saccades

Mental Saccades  This in turn activates memory traces in the global workspace area that will be used for mental searches (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.

Mental saccades in a conscious machine Perceptual saccadesChanging perception Changing environment Associative memory No Action control Loop 5 Loop 2 Perceptual saccadesChanging perception Changing environment Associative memory No Action control Advancement of a goal? Yes Learning Advancement of a goal? Advancement of a goal? Yes Learning Attention spotlight Mental saccades Continue search? Yes Loop 1 Attention spotlight Mental saccades Continue search? Continue search? Yes Loop 1 Plan action? No Yes Action? Yes No Changing motivation Loop 3 Loop 4 Plan action? No Yes Action? Yes No Changing motivation Loop 3 Loop 4 Loop 5 Loop 2

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

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: training.html

Computational Model: Implications  Motivations for actions are physically distributed o 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 o 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 o results in conscious experience decision of what is observed and where is it planning how to respond o a train of such experiences constitutes consciousness

NeoAxis Simulation Neoaxis Implementation VIDEO

Conclusions 1.Consciousness is computational 2.Intelligent machines can be conscious