EE141 How to Motivate Machines to Learn and Help Humans in Making Water Decisions? Janusz Starzyk School of Electrical Engineering and Computer Science, Ohio University, USA

EE141  Embodied Intelligence (EI)  Embodiment of Mind  EI Interaction with Environment  How to Motivate a Machine  Goal Creation Hierarchy  Goal Creation Experiment  Promises of EI  To economy  To society Outline

EE141  “…Perhaps the last frontier of science – its ultimate challenge- is to understand the biological basis of consciousness and the mental process by which we perceive, act, learn and remember..” from Principles of Neural Science by E. R. Kandel et al.  E. R. Kandel won Nobel Price in 2000 for his work on physiological basis of memory storage in neurons.  “… The question of intelligence is the last great terrestrial frontier of science...” from Jeff Hawkins On Intelligence.  Jeff Hawkins founded the Redwood Neuroscience Institute devoted to brain research Intelligence AI’s holy grail From Pattie Maes MIT Media Lab

EE141 Traditional AI Embodied Intelligence  Abstract intelligence  attempt to simulate “highest” human faculties: –language, discursive reason, mathematics, abstract problem solving  Environment model  Condition for problem solving in abstract way  “brain in a vat”  Embodiment  knowledge is implicit in the fact that we have a body –embodiment is a foundation for brain development  Intelligence develops through interaction with environment  Situated in a specific environment  Environment is its best model

EE141 Design principles of intelligent systems from Rolf Pfeifer “Understanding of Intelligence”, 1999  Interaction with complex environment  cheap design  ecological balance  redundancy principle  parallel, loosely coupled processes  asynchronous  sensory-motor coordination  value principle Agent Drawing by Ciarán O’Leary- Dublin Institute of Technology

EE141 Embodied Intelligence Definition  Embodied Intelligence (EI) is a mechanism that learns how to survive in a hostile environment –Mechanism: biological, mechanical or virtual agent with embodied sensors and actuators –EI acts on environment and perceives its actions –Environment hostility is persistent and stimulates EI to act –Hostility: direct aggression, pain, scarce resources, etc –EI learns so it must have associative self-organizing memory –Knowledge is acquired by EI

EE141 Embodiment of a Mind  Embodiment contains intelligence core and sensory motor interfaces under its control to interact with environment  Necessary for development of intelligence  Not necessarily constant or in the form of a physical body  Boundary transforms modifying brain’s self- determination

EE141  Brain learns own body’s dynamic  Self-awareness is a result of identification with own embodiment  Embodiment can be extended by using tools and machines  Successful operation is a function of correct perception of environment and own embodiment Embodiment of a Mind

EE141 INPUTOUTPUT Simulation or Real-World System Task Environment Agent Architecture Long-term Memory Short-term Memory Reason Act Perceive RETRIEVALLEARNING EI Interaction with Environment From Randolph M. Jones, P :

EE141 How to Motivate a Machine ? The fundamental question is how to motivate a machine to do anything, in particular to increase its “brain” complexity? How to motivate it to explore the environment and learn how to effectively work in this environment? Can a machine that only implements externally given goals be intelligent? If not how these goals can be created?

EE141  I suggest that hostility of environment motivates us.  It is the pain that moves us.  Our intelligence that tries to minimize this pain motivates our actions, learning and development  We need both the environment hostility and the mechanism that learns how to reduce inflicted by the environment pain How to Motivate a Machine ?  I propose based on the pain mechanism that motivates the machine to act, learn and develop.  So the pain is good.  Without the pain there will be no intelligence.  Without the pain there will be no motivation to develop.

EE141 Pain-center and Goal Creation  Simple Mechanism  Creates hierarchy of values  Leads to formulation of complex goals  Reinforcement : Pain increase Pain decrease  Forces exploration + - Environment Sensor Motor Pain level Dual pain level Pain increase Pain decrease (-) (+) Excitation (-) (+) Wall-E’s goal is to keep his plants from dying

EE141 Primitive Goal Creation -+ Pain Dry soil Primitive level open tank sit on garbage refill faucet w. can water Dual pain

EE141 Abstract Goal Creation  The goal is to reduce the primitive pain level  Abstract goals are created to reduce abstract pains in order to satisfy the primitive goals  Abstract pain center -+ Pain Dual pain + Dry soil Abstract pain “water can” – sensory input to abstract pain center Sensory pathway (perception, sense) Motor pathway (action, reaction) Primitive Level Level I Level II faucet - w. can open water Activation Stimulation Inhibition Reinforcement Echo Need Expectation

EE141 Abstract Goal Hierarchy  A hierarchy of abstract goals is created - they 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

EE141 GCS vs. Reinforcement Learning Actor-critic design Goal creation system Case study: “How can Wall-E water his plants if the water resources are limited and hard to find?” Sensory pathway Motor pathway GCS Environment Pain States Gate control Desired action &state Action decision Action

EE141 Goal Creation Experiment Sensory-motor pairs and their effect on the environment PAIR # SENSORYMOTORINCREASESDECREASES 1water canwater the plantmoisturewater in can 8faucetopenwater in canwater in tank 15tankrefillwater in tankreservoir water 22pipeopenreservoir waterlake water 29rainfalllake water-

EE141 Results from GCS scheme pain Dry soil pain No water in can pain No water in tank pain No water in reservoir pain No water in lake

EE141 Averaged performance over 10 trials: GCS: RL: Machine using GCS learns to control all abstract pains and maintains the primitive pain signal on a low level in demanding environment conditions. GCS vs. Reinforcement Learning

EE141 Goal Creation Experiment Action scatters in 5 CGS simulations

EE141 Goal Creation Experiment The average pain signals in 100 CGS simulations Primitive pain – dry soil Pain Lack of water in can Pain Lack of water in tank Pain Lack of water in reservoir Pain Lack of water in lake Pain Discrete time

EE141 Promises of embodied intelligence  To society  Advanced use of technology –Robots –Tutors –Intelligent gadgets  Intelligence age follows –Industrial age –Technological age –Information age  Society of minds –Superhuman intelligence –Progress in science –Solution to societies’ ills  To industry  Technological development  New markets  Economical growth ISAC, a Two-Armed Humanoid Robot Vanderbilt University

EE Biomimetics and Bio-inspired Systems Impact on Space Transportation, Space Science and Earth Science Mission Complexity Biological Mimicking Embryonics Extremophiles DNA Computing Brain-like computing Self Assembled Array Artificial nanopore high resolution Mars in situ life detector Sensor Web Biological nanopore low resolution Skin and Bone Self healing structure and thermal protection systems Biologically inspired aero-space systems Space Transportation Memristors

EE141 Sounds like science fiction  If you’re trying to look far ahead, and what you see seems like science fiction, it might be wrong.  But if it doesn’t seem like science fiction, it’s definitely wrong. From presentation by Feresight Institute

EE141Questions?