ADVIS '041 Artificial Life: How can it impact engineering practices of the future? Cihan H. Dagli Smart Engineering Systems Laboratory Engineering Management.

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Artificial Life: How can it impact engineering practices of the

future? Cihan H. DagliCihan H. Dagli

Smart Engineering Systems LaboratorySmart Engineering Systems Laboratory

Engineering Management DepartmentEngineering Management Department

University of Missouri - RollaUniversity of Missouri - Rolla

Rolla, MO 65409 - 0370Rolla, MO 65409 - 0370http://www.umr.edu/~daglihttp://www.umr.edu/~dagli

[email protected] [email protected]

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Presentation Outline

Engineering Systems of the FutureEngineering Systems of the Future What is Artificial Life?What is Artificial Life? Artificial Life in EngineeringArtificial Life in Engineering Concluding RemarksConcluding Remarks

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Recent Market Changes

Total GlobalizationTotal Globalization Increasing Production PaceIncreasing Production Pace Decreasing Production Cycle TimesDecreasing Production Cycle Times Migration From Mass Production to Mass Migration From Mass Production to Mass

CustomizationCustomization

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Engineering Systems of the Future

Immediate Respond to Market ChangesImmediate Respond to Market Changes More Sensitive to Customer NeedsMore Sensitive to Customer Needs Migration from Central to Distributed Migration from Central to Distributed

ControlControl Autonomous and Cooperating Production Autonomous and Cooperating Production

Units Units

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Smart Systems

The term “smart” indicates physical The term “smart” indicates physical systems that can interact with their systems that can interact with their environment and adapt to changes environment and adapt to changes through self-awareness and perceived through self-awareness and perceived models of the world, based on quantitative models of the world, based on quantitative and qualitative information.and qualitative information.

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Autonomous Units

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Autonomous Engineered Entity

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Autonomous Engineered Enterprises

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Evolutionary Color Images: Karl Sims

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Evolutionary Color Images: Karl Sims

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“Trajectories” of Research into Distributed Systems

System Behavior & Analysis

System Behavior & Analysis

System Design

System Design

Swarm Swarm Intelligence & Intelligence &

Synthetic Synthetic EcosystemsEcosystems

Artificial Artificial LifeLife

Multi-Multi-agent agent

SystemsSystems

Distributed Distributed Artificial Artificial

IntelligencIntelligencee

Population Population Biology& Biology&

Ecological Ecological ModelingModeling

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What is Artificial Life?

A Perspective:A Perspective: It is a way of imitating Nature in order to solve It is a way of imitating Nature in order to solve

engineering problems. engineering problems. It includes simulation and emulation of living It includes simulation and emulation of living

systems like plants or animals. systems like plants or animals. It tries to achieve a new understanding of It tries to achieve a new understanding of

living systems, and of what is life.living systems, and of what is life.

http://kal-el.ugr.es/pitis.html

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A Definition:A Definition:Artificial life is a field of study devoted to Artificial life is a field of study devoted to understanding life by attempting to abstract understanding life by attempting to abstract the fundamental dynamical principals the fundamental dynamical principals underlying biological phenomena, and underlying biological phenomena, and recreating these dynamics in other physical recreating these dynamics in other physical media – such as computers – making them media – such as computers – making them accessible to new kinds of experimental accessible to new kinds of experimental manipulation and testing.manipulation and testing.(by Christopher G. Langton, from the preface to the Proceedings of the Workshop on Artificial Life,

February 1990, Santa Fe, New Mexico)

What is Artificial Life?

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Adaptive Autonomous Agents

Agent:Agent: A system that tries to fulfill a A system that tries to fulfill a set of goals in a complex, dynamic set of goals in a complex, dynamic environment.environment.

Environment:Environment: It can sense the environment through It can sense the environment through its sensors and act upon the its sensors and act upon the environment using its actuators.environment using its actuators.

Adopted from http://www.rt.el.utwente.nl/agent/

Modeling Adaptive Autonomous Agents, Pattie Maes

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Goal:Goal:An agents goal can take many An agents goal can take many different forms:different forms: End Goals, particular states the End Goals, particular states the agent tries to achieveagent tries to achieve Selective reinforcement or reward that the Selective reinforcement or reward that the

agent attempts to maximizeagent attempts to maximize Internal needs or motivations that the agent Internal needs or motivations that the agent

has to keep within certain viability zones.has to keep within certain viability zones.

Adopted from http://www.rt.el.utwente.nl/agent/

Modeling Adaptive Autonomous Agents, Pattie Maes

Adaptive Autonomous Agents

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Agent

AutonomousAutonomous Capable of effective independent actionCapable of effective independent action

Goal-directedGoal-directed Autonomous actions are directed towards the Autonomous actions are directed towards the

achievement of defined tasksachievement of defined tasks IntelligentIntelligent

Ability to learn and adaptAbility to learn and adapt CooperateCooperate

Cooperate with other agents to perform a taskCooperate with other agents to perform a task

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Agent Types

Cooperate Learn

Autonomous

Collaborative Learning Agents

Smart Agents

Interface agentsCollaborative Agents

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Emergent Phenomena

Emergent phenomena are those in Emergent phenomena are those in which even perfect knowledge and which even perfect knowledge and understanding may give us no understanding may give us no predictive information. In them the predictive information. In them the optimal means of prediction is optimal means of prediction is simulation. simulation. (Vince Darley, 1994)(Vince Darley, 1994)

The whole is greater than the sum of The whole is greater than the sum of the partsthe parts

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Artificial Life Techniques

Agent-based modelingAgent-based modeling Evolutionary programmingEvolutionary programming Genetic algorithmsGenetic algorithms Distributed artificial intelligenceDistributed artificial intelligence Swarm intelligenceSwarm intelligence

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Artificial Problem Solvers:Agent-based Modeling

Computational method where a system is Computational method where a system is modeled as a collection of autonomous modeled as a collection of autonomous decision-making entities that interact in decision-making entities that interact in non-trivial ways.non-trivial ways.

Bottom-up modelingBottom-up modeling Artificial social systems Artificial social systems

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Organizations of agents

Animate agents

Data

Artificial world

Observer

Inanimate agents

If

<cond>

Then

<action1>

Else

<action2>

Courtesy of Lars-Erik Cederman

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Areas of Application

Flow management: evacuation, traffic, Flow management: evacuation, traffic, supermarketsupermarket

Markets: stock market, electronic auctions, Markets: stock market, electronic auctions, ISP marketISP market

Organizations: operational risk, Organizations: operational risk, organizational designorganizational design

Diffusion: diffusion of innovation, adoption Diffusion: diffusion of innovation, adoption dynamics dynamics

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Flow Management

Source: www.helbing.org

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ExposedContracts

DiseaseReports

MoveSpatially

MoveInformation

Agent Location,Demographic

& Social NetworkCharacteristics

Disease Model

Agent Model

DailyCommunity Level

Reports

SharedBSSDatabase

NEDSSCompliant

Geographic Topology

ModelEnvironmental

Lethality

Manifests Symptoms

detectionprivacy

What If ScenarioImpact Analysis

Communication Technology

Model

Courtesy of K. Carley, A. Yahja, B. Kaminsky

Artificial BIOWAR

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Artificial Problem Solvers: Algorithms

Artificial Life tools have led to development Artificial Life tools have led to development of many interesting algorithms that often of many interesting algorithms that often perform better than classical algorithms perform better than classical algorithms within a shorter time. within a shorter time.

These algorithms generally contain explicit These algorithms generally contain explicit or implicit parallelism.or implicit parallelism.

They resort to distributed agents, or to They resort to distributed agents, or to evolutionary algorithms, or often to both.evolutionary algorithms, or often to both.

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Evolving Neural Networks

To develop a hybrid intelligent system – To develop a hybrid intelligent system – Evolving Neural Networks (ENNs) – that Evolving Neural Networks (ENNs) – that can be used in data mining, especially in can be used in data mining, especially in classification problems.classification problems.

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Evolving Neural Networks

Employs computational intelligence Employs computational intelligence methodologiesmethodologies Neural Networks & Genetic AlgorithmsNeural Networks & Genetic Algorithms

Genetic algorithms have been applied to Genetic algorithms have been applied to automatic generation of neural networksautomatic generation of neural networks Feature selectionFeature selection Adaptable topologyAdaptable topology Customized tasksCustomized tasks Ensemble methodEnsemble method

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Optimizing a NN architecture Using GA

Genetic Algorithms

chromosomes

Translation into neuralnetworks

Training neural networks

Evalutation of neuralnetwork performance

f(x)fitness function:

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Feature Selection

Evolving NN 1 Evolving NN 2 Evolving NN n

Features

Final Decision

Combining ModuleGA

GA

Ensemble of ENNs

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Ensemble of ENNs

ENNs meet the major requirements of a ENNs meet the major requirements of a data mining tooldata mining tool Smart architectureSmart architecture

GA GA Self-adaptable structure Self-adaptable structure PerformancePerformance

Ensemble method Ensemble method Accuracy Accuracy Low complexity Low complexity Efficiency Efficiency

User interactionUser interaction Objective function Objective function Customized classification Customized classification

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Artificial Problem Solvers: Reinforcement Learning Methods

Focus on the rational decision-making process Focus on the rational decision-making process under uncertain environmentsunder uncertain environments

Agent can generate a series of actions to Agent can generate a series of actions to influence the evolution of a stochastic dynamic influence the evolution of a stochastic dynamic systemsystem

Underlying control problem is often modeled Underlying control problem is often modeled as a Markov Decision Process (MDP).as a Markov Decision Process (MDP).

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Reinforcement Learning Methods

What to be learned Mapping from situations to actionsMapping from situations to actions Maximizes a scalar reward or reinforcement signalMaximizes a scalar reward or reinforcement signal

Learning Does not need to be told which actions to takeDoes not need to be told which actions to take Must discover which actions yield most reward by Must discover which actions yield most reward by

tryingtrying

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Adaptive Critic Design (ACD)

The neural control design philosophy The neural control design philosophy Algorithms are intermediate between Algorithms are intermediate between

Direct Reinforcement and Value Function Direct Reinforcement and Value Function methods, in that the “critic” learns a value methods, in that the “critic” learns a value function which is then used to update the function which is then used to update the parameters of the “actor”parameters of the “actor”

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Need for Online Hybrid Prediction Model Derived from ACD

Fundamental drawbacks of supervised Fundamental drawbacks of supervised learning-based prediction modellearning-based prediction model

Uncertain volatility in real world call for Uncertain volatility in real world call for adaptive model adaptive model

Reinforcement learning philosophy is Reinforcement learning philosophy is suitable tool especially when the short-suitable tool especially when the short-time performance of forecasting can be time performance of forecasting can be obtainedobtained

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Supervised Learning Assisted Reinforcement Learning Prediction

Architecture for Time-Series

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Stock Price Prediction

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Adaptive Model Evolution

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Artificial Problem Solvers: Robotics

Many robotic systems are currently Many robotic systems are currently being developed in the spirit of artificial being developed in the spirit of artificial life. They are devoted to harvesting, life. They are devoted to harvesting, mining, ecological sampling etc.mining, ecological sampling etc.

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Cooperative Behaviour & path Planning for Autonomous Robots Using Evolutionary

Algorithm & Fuzzy Clustering

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Alice

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Artificial Problem Solvers: Evolvable Systems

Different categories depending on the Different categories depending on the complexity and purpose:complexity and purpose:

Artificial Life Artificial Life Evolvable Hardware (EHW)Evolvable Hardware (EHW)

analoganalog digital (FPGAs)digital (FPGAs) Hardware design using evolutionHardware design using evolution

Evolutionary Robotics Evolutionary Robotics Evolving controllers for a purpose Evolving controllers for a purpose Co-evolution of robot populationsCo-evolution of robot populations

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Artificial Problem Solvers:Mobile Agents

George Cybenko and Bob Gray Thayer School of Engineering Dartmouth College George Cybenko and Bob Gray Thayer School of Engineering Dartmouth College george.cybenko,robert.gray}@dartmouth.edugeorge.cybenko,robert.gray}@dartmouth.edu

Orders and memos

WirelessNetwork

Technicalspecs

Trooppositions

Wired network

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Static & Mobile Agents Developed for Small Unit Operations

Courtesy of McGrath et alCourtesy of McGrath et al

Objectives:Objectives:• Gather information from sensor reportsGather information from sensor reports• Infer additional information from object ontologyInfer additional information from object ontology• Determine the degree of threat via fuzzy logic inference engineDetermine the degree of threat via fuzzy logic inference engine• Determine recent nearby alerts using clusteringDetermine recent nearby alerts using clustering• Intelligent “push” of relevant threat data via GrapevineIntelligent “push” of relevant threat data via Grapevine

Analysis agentAnalysis agent

Sensor FieldSensor Field

Sensor Report SentSensor Report Sent Threat identified and Alert sentThreat identified and Alert sent

GrapevineGrapevine

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George Cybenko and Bob Gray Thayer School of Engineering Dartmouth College George Cybenko and Bob Gray Thayer School of Engineering Dartmouth College {george.cybenko,robert.gray}@dartmouth.edu{george.cybenko,robert.gray}@dartmouth.edu

Data and simulation cloud

NSF 1998KDIProject

Mobile agentslink weakly

coupleddistributed

components.

Continuous datacollection

Intermittent data

collection

Operational simulation 2

Operational simulation 1

Unexpected (such as emergency relief) uses

Artificial Problem Solvers: Mobile Agents

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Multi Agent Co-operative Area Coverage using GA

Multi Robot SystemMulti Robot System Cover Predetermined Area (Go over every Cover Predetermined Area (Go over every

square inch)square inch) Boundaries MarkedBoundaries Marked Minimize Time and hence Energy EfficientMinimize Time and hence Energy Efficient

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Artificial Problem Solvers: Swarm Intelligence

““Any attempt to design algorithms or distributed Any attempt to design algorithms or distributed problem-solving devices inspired by the collective problem-solving devices inspired by the collective behavior of social insect colonies and other behavior of social insect colonies and other animal societies.“animal societies.“

-[Bonabeau et al., 1999]--[Bonabeau et al., 1999]-

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Swarming Characteristics

Entities share common goal

Local Interaction

s

Self Organizatio

n

Autonomy of units

Stigmergy Simple rules or units

Distributed

Large number or efficient

size

Pulsing of force

Flexible and robust

Swarming

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Emergent- Self assembled Nest

Courtesy of BonabeauCourtesy of Bonabeau

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Ant Colony Optimization

1. Straight Pheromone Trail 2. Obstacle Introduced

3. Two Options are Explored 4. Shortest Path Dominates

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Routing in Communication Networks

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Future Combat Systems

Courtesy of Riggs J. Courtesy of Riggs J.

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Particle Swarm Optimization

Original intent was to simulate the choreography of a bird flock

Best strategy to find the food is to follow the bird which is nearest to the food

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PSO Initialization: Positions and velocities

Courtesy of Maurice Clerk

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Particle Swarm Optimization

Global optimum

Courtesy of Maurice Clerk

•The best solution (fitness) particle has achieved so far (pbest)•The best value obtained so far by any particle in the population (gbest)

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Artificial Problem Solvers:Synthetic Ecosystems

The synthetic ecosystems approach The synthetic ecosystems approach applies swarm intelligence to the design of applies swarm intelligence to the design of multi-agent systems.multi-agent systems.

The main concern of research into The main concern of research into synthetic ecosystems is to provide synthetic ecosystems is to provide practical engineering guidelines to design practical engineering guidelines to design systems of industrial strengthsystems of industrial strength

[Parunak, 1997] [Parunak et al., 1998][Parunak, 1997] [Parunak et al., 1998]

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Distributed Architectures for Manufacturing

Holonic SystemsHolonic Systems A whole individual and a part at the same timeA whole individual and a part at the same time ““An autonomous and cooperative building block of a An autonomous and cooperative building block of a

manufacturing system for transforming, transporting, manufacturing system for transforming, transporting, storing and/or validating information and physical storing and/or validating information and physical objects”objects”

[Christensen, 1994][Christensen, 1994] A manufacturing holon comprises a control part and A manufacturing holon comprises a control part and

an optional physical processing part. Multiple holons an optional physical processing part. Multiple holons may dynamically aggregate into a single (higher-level) may dynamically aggregate into a single (higher-level) holon. holon.

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Distributed Architectures for Manufacturing

The application of the holonic concept to The application of the holonic concept to the manufacturing domain is expected to the manufacturing domain is expected to yield systems of autonomous, cooperating yield systems of autonomous, cooperating entities that self-organize to achieve the entities that self-organize to achieve the current production goals.current production goals.

Such systems meet the requirements of Such systems meet the requirements of tomorrow's manufacturing control tomorrow's manufacturing control systems. systems.

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Concluding Remarks

Artificial Life is impacting engineering Artificial Life is impacting engineering systems through Agent-Based systems through Agent-Based architecturesarchitectures

Current Impact Areas:Current Impact Areas: Enterprise Integration and Supply Chain Enterprise Integration and Supply Chain

ManagementManagement Design and Manufacturability AssessmentsDesign and Manufacturability Assessments Enterprise Planning, Scheduling and ControlEnterprise Planning, Scheduling and Control

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Current Impact Areas:Current Impact Areas: Dynamic System ReconfigurationDynamic System Reconfiguration Factory Control ArchitecturesFactory Control Architectures Holonic Manufacturing SystemsHolonic Manufacturing Systems Distributed Dynamic SchedulingDistributed Dynamic Scheduling Commercial scheduling, routing, and force allocation Commercial scheduling, routing, and force allocation

problems problems Use of swarm networks to control swarm Unmanned Use of swarm networks to control swarm Unmanned

Aerial Vehicles (UAV), or undersea vehicles (UGV)Aerial Vehicles (UAV), or undersea vehicles (UGV)

Concluding Remarks