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 www.ent.ohiou.edu/~starzyk
Outline • 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
Intelligence AI’s holy grail FromPattie Maes MIT Media Lab • “…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
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 Traditional AI Embodied Intelligence
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
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
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
Embodiment of a Mind • 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
EI Interaction with Environment Agent Architecture Reason Short-term Memory Perceive Act RETRIEVAL LEARNING Long-term Memory INPUT OUTPUT Task Environment Simulation or Real-World System From Randolph M. Jones, P : www.soartech.com
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?
How to Motivate a Machine ? • 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 • 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.
Dual pain level Pain increase Sensor (-) + (-) (+) Environment (+) (+) - (-) Motor Pain level Pain decrease Excitation Pain-center and Goal Creation • Simple Mechanism • Creates hierarchy of values • Leads to formulation of complex goals • Reinforcement : • Pain increase • Pain decrease • Forces exploration Wall-E’s goal is to keep his plants from dying
Primitive Goal Creation faucet refill garbage sit on water w. can tank open - + Dual pain Pain Primitive level Dry soil
Abstract Goal Creation Motor pathway (action, reaction) Sensory pathway (perception, sense) faucet open Level II - + “water can” – sensory input to abstract pain center Abstract pain w. can water Level I Activation Stimulation Inhibition Reinforcement Echo Need Expectation - + Dual pain Pain Primitive Level Dry soil • 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
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 • A hierarchy of abstract goalsis created - they satisfy the lower level goals
GCS vs. Reinforcement Learning States Desired action &state Gate control GCS Pain Sensory pathway Action decision Motor pathway Environment Action 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?”
Goal Creation Experiment Sensory-motor pairs and their effect on the environment
Dry soil 4 pain 2 0 600 0 100 200 300 400 500 Nowater in can 2 pain 1 0 0 100 200 300 400 500 600 No water in tank 2 pain 1 0 0 100 200 300 400 500 600 No water in reservoir 1 pain 0.5 0 600 0 100 200 300 400 500 No water in lake 4 pain 2 0 600 0 100 200 300 400 500 Results from GCS scheme
30 20 10 0 0 100 200 300 400 500 600 GCS vs. Reinforcement Learning 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.
Goal Creation Experiment Action scatters in 5 CGS simulations
Primitive pain – dry soil 0.5 Pain 0 0 100 200 300 400 500 600 Lack of water in can 0.2 Pain 0.1 0 0 100 200 300 400 500 600 Lack of water in tank 0.2 Pain 0.1 0 0 100 200 300 400 500 600 Lack of water in reservoir 0.2 Pain 0.1 0 0 100 200 300 400 500 600 Lack of water in lake 0.1 Pain 0.05 0 0 100 200 300 400 500 600 Discrete time Goal Creation Experiment The average pain signals in 100 CGS simulations
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 RobotVanderbilt University
Mission Complexity Biological Mimicking Biomimetics and Bio-inspired SystemsImpact on Space Transportation, Space Science and Earth Science 2002 2010 2020 2030 Self Assembled Array Space Transportation Embryonics Memristors Biologically inspired aero-space systems Brain-like computing Sensor Web Extremophiles Mars in situ life detector Skin and Bone Self healing structure and thermal protection systems DNA Computing Artificial nanopore high resolution Biological nanopore low resolution
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