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UNESCO Workshop on Integrated Modeling Approaches to Support Water Resource Decision Making: Crossing the Chasm Motivated Machine Learning for Water Resource Management Janusz Starzyk School of Electrical Engineering and Computer Science, Ohio University, USA www.ent.ohiou.edu/~starzyk UNESCO Crossing the Chasm Outline Challenges in Water Management Embodied Intelligence (EI) Embodiment of Mind EI Interaction with Environment How to Motivate a Machine Goal Creation Hierarchy GCS Experiment Promises of EI To economy To society UNESCO Crossing the Chasm Challenges in Water Management Water management is challenging for various reasons: Strategies in water management are developed mostly on national level There is a growing competition between countries for water Water policy making effects environment and society, health and development, and economy Growing demands of countries’ populations for water Leads to hydrological nationalism Creates a need to integrate water sciences and policy making There is an acute need for legitimate scientific data Decision making in water-related health, food and energy systems are critical to economical development and security UNESCO Crossing the Chasm Challenges in Water Management Decision makers must answer important questions: How do we make water use sustainable? Who owns the water? What policies, institutional and legal framework can promote sustainable use of water? How to protect water resources from overuse and contamination? Water problems became too complex, interconnected and large to be handled by any one institution or by one group of professionals It is a challenge to evolve strategies and institutional frameworks for quick policy changes towards an acceptable water use It is necessary to create assessment and modeling tools to improve policy making and facilitate interaction. UNESCO Crossing the Chasm Challenges in Water Management Why development of integrated modeling to support decision making is important ? Computerized models were used for many years to support water related decisions. Models often simplify dynamics of economic, social and environmental interactions and lead to inappropriate policy making and management decisions. This note proposes models to emerge from interaction with real dynamically changing environments with all of their complexities and societal dependencies. The main objective is to suggest an integrated modeling framework that may assist with the tasks of water related decision making. UNESCO Crossing the Chasm Challenges in Water Management What are deficiencies of machine created models? Artificial neural networks, fuzzy logic, and genetic algorithms have all been used to model the hydrological cycle However, it is still difficult to apply these tools in making real-life water decisions as the related parameters are not explicitly known What may be needed is a motivated machine learning for characterizing the data and making predictions about their dynamic changes It could support intelligent decision making in dynamically changing environment It could be used to observe impacts of alternative water management policies It may consider social, cultural, political, economic and institutional elements that influence decision making This strategic note presents a goal creation approach in embodied intelligence (EI) that motivates machine to develop into a useful research toll. UNESCO Crossing the Chasm Challenges in Water Management Embodied intelligence may support decision making: EI mimics biological intelligent systems, extracting general principles of intelligent behavior and applying them to design intelligent agents It uses emerging, self-organizing, goal creation (GC) system that motivates embodied intelligence to learn how to efficiently interact with the environment Knowledge is not entered into such systems, but rather is a result of their successful use in a given environment. This knowledge is validated through active interaction with the environment. Motivated intelligent systems adapt to unpredictable and dynamic situations in the environment by learning, which gives them a high degree of autonomy Learning in such systems is incremental, with continuous prediction of the input associations based on the emerging models - only new information is registered in the memory UNESCO Crossing the Chasm Challenges in Water Management Use the motivated learning scheme to integrate modelling and decision making: It is suggested to apply ML approach to water management in changing environments where the existing methods fail or work with difficulty. For instance, using classical machine learning to predict the future for physical processes works only under the assumption that same processes will repeat. However, if a process changes beyond certain limits, the prediction will not be correct. GC systems may overcome this difficulty and such systems can be trained to advice humans. Design concepts, computational mechanisms, and architectural organization of embodied intelligence are presented in this talk The talk will explain internal motivation mechanism that leads to effective goal oriented learning In addition, a goal creation mechanism and goal driven learning will be described. UNESCO Crossing the Chasm Intelligence AI’s holy grail From Pattie 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 UNESCO Crossing the Chasm Traditional AI Abstract intelligence Embodied 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” UNESCO Crossing the Chasm Embodiment knowledge is implicit in the fact that we have a body – embodiment supports brain development Intelligence develops through interaction with environment Situated in environment Environment is its best model 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 UNESCO Crossing the Chasm 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 UNESCO Crossing the Chasm 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 selfdetermination UNESCO Crossing the Chasm Embodiment Sensors channel Environment Intelligence core Actuators channel 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 UNESCO Crossing the Chasm 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 UNESCO Crossing the Chasm 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? UNESCO Crossing the Chasm 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 In this work 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. UNESCO Crossing the Chasm Pain-center and Goal Creation Dual pain level Pain increase Sensor (-) Simple Mechanism Creates hierarchy of values Leads to formulation of complex goals Reinforcement : • Pain increase • Pain decrease Forces exploration + (+) Environment (+) (-) Pain level Wall-E’s goal is to keep his plants from dying UNESCO Crossing the Chasm (-) - (+) Motor Pain decrease Excitation Primitive Goal Creation faucet refill garbage w. can sit on water tank Dual pain Dry soil UNESCO Crossing the Chasm + Pain Primitive level open 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 Sensory pathway Motor pathway (perception, sense) (action, reaction) faucet “water can” – sensory input to abstract pain w. can center Activation Stimulation Inhibition Reinforcement Echo Need Expectation UNESCO Crossing the Chasm open - Dry soil + Abstract pain water Dual pain Level II Level I + Pain Primitive Level Abstract Goal Hierarchy Sensory pathway (perception, sense) A hierarchy of abstract goals is created - they satisfy the lower level goals Motor pathway (action, reaction) tank refill - + faucet open - Activation Stimulation Inhibition Reinforcement Echo Need Expectation Dry soil UNESCO Crossing the Chasm Level II + w. can water - Level III Level I + Primitive Level GCS vs. Reinforcement Learning States Policy Desired action &state Pain States Critic Value Function action GCS Sensory pathway Action decision Motor pathway reward Environment Gate control Environment Action RL 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?” UNESCO Crossing the Chasm Goal Creation Experiment SENSORY MOTOR INCREASES DECREASES 1 water can water the plant moisture water in can 8 faucet open water in can water in tank 15 tank refill water in tank reservoir water 22 pipe open reservoir water lake water 29 rain fall lake water - PAIR # Sensory-motor pairs and their effect on the environment UNESCO Crossing the Chasm Results from GCS scheme Dry soil pain 4 2 0 0 100 200 400 500 600 300 400 500 600 300 400 500 600 300 400 500 600 300 400 500 600 300 No water in can pain 2 1 0 0 100 200 100 200 100 200 100 200 pain 2 1 0 0 1 pain No water in tank No water in reservoir 0.5 0 0 pain 4 No water in lake 2 0 0 UNESCO Crossing the Chasm GCS vs. Reinforcement Learning Averaged performance over 10 trials: GCS: Primitive pain pain 1 0.5 0 0 100 200 400 500 600 400 500 600 400 500 600 Lack of food RL: 1 pain 300 30 0.5 20 0 0 100 200 300 10 Lack of money 0.4 0 pain 0 100 200 300 0.2 Machine using GCS learns to control all abstract pains and 0 maintains the primitive pain 0 100 200 300 signal on400a low level 500 in Lack of bank savingsconditions. demanding environment 0.4 UNESCO Crossing the Chasm 600 Goal Creation Experiment Goal Scatter Plot 40 35 30 Goal ID 25 20 15 10 5 0 0 100 200 300 400 Discrete time 500 600 Action scatters in 5 CGS simulations UNESCO Crossing the Chasm Goal Creation Experiment Pain Pain Pain Pain Pain Primitive pain – dry soil 0.5 0 0.2 0.1 0 0.2 0.1 0 0.2 0.1 0 0.1 0.05 0 0 100 200 300 400 Lack of water in can 500 600 0 100 200 300 400 Lack of water in tank 500 600 0 100 200 300 400 Lack of water in reservoir 500 600 0 100 200 300 400 Lack of water in lake 500 600 0 100 200 300 Discrete time 500 600 400 The average pain signals in 100 CGS simulations UNESCO Crossing the Chasm Compare RL (TDF) and GCS Mean primitive pain Pp value as a function of the number of iterations. Dashed lines indicate moment when Pp is getting stable - green line for TDF - blue line for GCS. UNESCO Crossing the Chasm Compare RL (TDF) and GCS Comparison of execution time on log-log scale TD-Falcon green GCS blue Combined efficiency of GCS 1000 better than TDF 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 UNESCO Crossing the Chasm 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 UNESCO Crossing the Chasm ISAC, a Two-Armed Humanoid Robot Vanderbilt University Biomimetics and Bio-inspired Systems Mission Complexity Impact on Space Transportation, Space Science and Earth Science 2002 2010 2020 2030 Embryonics Self Assembled Array Space Transportation Memristors Biologically inspired aero-space systems Sensor Web Extremophiles Mars in situ life detector Skin and Bone Self healing structure and thermal protection systems Biological nanopore low resolution UNESCO Crossing the Chasm Brain-like computing Artificial nanopore high resolution DNA Computing Biological Mimicking Sounds like science fiction UNESCO Crossing the Chasm 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 Foresight Institute Questions? UNESCO Crossing the Chasm