Artificial Life: How can it impact engineering

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Artificial Life How can it impact
engineering practices of the future?

• Cihan H. DagliSmart Engineering Systems
  Laboratory
• Engineering Management Department
• University of Missouri - Rolla
• Rolla, MO 65409 - 0370http//www.umr.edu/dagli
  dagli_at_umr.edu

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Presentation Outline

• Engineering Systems of the Future
• What is Artificial Life?
• Artificial Life in Engineering
• Concluding Remarks

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Recent Market Changes

• Total Globalization
• Increasing Production Pace
• Decreasing Production Cycle Times
• Migration From Mass Production to Mass
  Customization

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Engineering Systems of the Future

• Immediate Respond to Market Changes
• More Sensitive to Customer Needs
• Migration from Central to Distributed Control
• Autonomous and Cooperating Production Units

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Smart Systems

• The term smart indicates physical systems that
  can interact with their environment and adapt to
  changes through self-awareness and perceived
  models of the world, based on quantitative 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
Distributed Artificial Intelligence
System Design
Swarm Intelligence Synthetic Ecosystems
Multi-agent Systems
System Behavior Analysis
Population Biology Ecological Modeling
Artificial Life
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What is Artificial Life?

• A Perspective
• It is a way of imitating Nature in order to solve
  engineering problems.
• It includes simulation and emulation of living
  systems like plants or animals.
• It tries to achieve a new understanding of living
  systems, and of what is life.

http//kal-el.ugr.es/pitis.html
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What is Artificial Life?

• A Definition
• Artificial life is a field of study devoted to
  understanding life by attempting to abstract the
  fundamental dynamical principals underlying
  biological phenomena, and recreating these
  dynamics in other physical media such as
  computers making them accessible to new kinds
  of experimental 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)
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Adaptive Autonomous Agents

• Agent
• A system that tries to fulfill a
• set of goals in a complex, dynamic
• environment.
• Environment
• It can sense the environment through
• its sensors and act upon the
• environment using its actuators.

Adopted from http//www.rt.el.utwente.nl/agent/
Modeling Adaptive Autonomous Agents, Pattie Maes
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Adaptive Autonomous Agents

• Goal
• An agents goal can take many
• different forms
• End Goals, particular states the
• agent tries to achieve
• Selective reinforcement or reward that the agent
  attempts to maximize
• Internal needs or motivations that the agent has
  to keep within certain viability zones.

Adopted from http//www.rt.el.utwente.nl/agent/
Modeling Adaptive Autonomous Agents, Pattie Maes
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Agent

• Autonomous
• Capable of effective independent action
• Goal-directed
• Autonomous actions are directed towards the
  achievement of defined tasks
• Intelligent
• Ability to learn and adapt
• Cooperate
• Cooperate with other agents to perform a task

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Agent Types
Collaborative Learning Agents
Cooperate
Learn
Smart Agents
Autonomous
Interface agents
Collaborative Agents
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Emergent Phenomena

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

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Artificial Life Techniques

• Agent-based modeling
• Evolutionary programming
• Genetic algorithms
• Distributed artificial intelligence
• Swarm intelligence

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Artificial Problem SolversAgent-based Modeling

• Computational method where a system is modeled as
  a collection of autonomous decision-making
  entities that interact in non-trivial ways.
• Bottom-up modeling
• Artificial social systems

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If ltcondgt Then ltaction1gt Else ltaction2gt
Artificial world
Inanimate agents
Observer
Animate agents
Data
Organizations of agents
Courtesy of Lars-Erik Cederman
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Areas of Application

• Flow management evacuation, traffic, supermarket
• Markets stock market, electronic auctions, ISP
  market
• Organizations operational risk, organizational
  design
• Diffusion diffusion of innovation, adoption
  dynamics

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Flow Management
Source www.helbing.org
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Artificial BIOWAR
Courtesy of K. Carley, A. Yahja, B. Kaminsky
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Artificial Problem Solvers Algorithms

• Artificial Life tools have led to development of
  many interesting algorithms that often perform
  better than classical algorithms within a shorter
  time.
• These algorithms generally contain explicit or
  implicit parallelism.
• They resort to distributed agents, or to
  evolutionary algorithms, or often to both.

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Evolving Neural Networks

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

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Evolving Neural Networks

• Employs computational intelligence methodologies
• Neural Networks Genetic Algorithms
• Genetic algorithms have been applied to automatic
  generation of neural networks
• Feature selection
• Adaptable topology
• Customized tasks
• Ensemble method

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Optimizing a NN architecture Using GA
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Ensemble of ENNs
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Ensemble of ENNs

• ENNs meet the major requirements of a data mining
  tool
• Smart architecture
• GA ? Self-adaptable structure
• Performance
• Ensemble method ? Accuracy
• Low complexity ? Efficiency
• User interaction
• Objective function ? Customized classification

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Artificial Problem Solvers Reinforcement
Learning Methods

• Focus on the rational decision-making process
  under uncertain environments
• Agent can generate a series of actions to
  influence the evolution of a stochastic dynamic
  system
• Underlying control problem is often modeled as a
  Markov Decision Process (MDP).

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Reinforcement Learning Methods

• What to be learned
• Mapping from situations to actions
• Maximizes a scalar reward or reinforcement signal
• Learning
• Does not need to be told which actions to take
• Must discover which actions yield most reward by
  trying

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Adaptive Critic Design (ACD)

• The neural control design philosophy
• Algorithms are intermediate between Direct
  Reinforcement and Value Function methods, in that
  the critic learns a value function which is
  then used to update the parameters of the actor

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Need for Online Hybrid Prediction Model Derived
from ACD

• Fundamental drawbacks of supervised
  learning-based prediction model
• Uncertain volatility in real world call for
  adaptive model
• Reinforcement learning philosophy is suitable
  tool especially when the short-time performance
  of forecasting can be obtained

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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 being
  developed in the spirit of artificial life. They
  are devoted to harvesting, 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 complexity
  and purpose
• Artificial Life
• Evolvable Hardware (EHW)
• analog
• digital (FPGAs)
• Hardware design using evolution
• Evolutionary Robotics Evolving controllers
  for a purpose
• Co-evolution of robot populations

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Artificial Problem SolversMobile Agents

• George Cybenko and Bob Gray Thayer School of
  Engineering Dartmouth College george.cybenko,rober
  t.gray_at_dartmouth.edu

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Static Mobile Agents Developed for Small Unit
Operations
Threat identified and Alert sent
Sensor Report Sent
Grapevine
Analysis agent
Sensor Field

• Objectives
• Gather information from sensor reports
• Infer additional information from object
  ontology
• Determine the degree of threat via fuzzy logic
  inference engine
• Determine recent nearby alerts using clustering
• Intelligent push of relevant threat data via
  Grapevine

Courtesy of McGrath et al
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Artificial Problem Solvers Mobile Agents

• George Cybenko and Bob Gray Thayer School of
  Engineering Dartmouth College george.cybenko,robe
  rt.gray_at_dartmouth.edu

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Multi Agent Co-operative Area Coverage using GA

• Multi Robot System
• Cover Predetermined Area (Go over every square
  inch)
• Boundaries Marked
• Minimize Time and hence Energy Efficient

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Artificial Problem Solvers Swarm Intelligence

• Any attempt to design algorithms or distributed
  problem-solving devices inspired by the
  collective behavior of social insect colonies and
  other animal societies.
• -Bonabeau et al., 1999-

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Swarming Characteristics
Entities share common goal
Local Interactions
Autonomy of units
Self Organization
Simple rules or units
Stigmergy
Distributed
Large number or efficient size
Pulsing of force
Flexible and robust
Swarming
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Emergent- Self assembled Nest
Courtesy 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.
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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

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

Global optimum
Courtesy of Maurice Clerk
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Artificial Problem SolversSynthetic Ecosystems

• The synthetic ecosystems approach applies swarm
  intelligence to the design of multi-agent
  systems.
• The main concern of research into synthetic
  ecosystems is to provide practical engineering
  guidelines to design systems of industrial
  strength
• Parunak, 1997 Parunak et al., 1998

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Distributed Architectures for Manufacturing

• Holonic Systems
• A whole individual and a part at the same time
• An autonomous and cooperative building block of
  a manufacturing system for transforming,
  transporting, storing and/or validating
  information and physical objects
• Christensen, 1994
• A manufacturing holon comprises a control part
  and an optional physical processing part.
  Multiple holons may dynamically aggregate into a
  single (higher-level) holon.

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Distributed Architectures for Manufacturing

• The application of the holonic concept to the
  manufacturing domain is expected to yield systems
  of autonomous, cooperating entities that
  self-organize to achieve the current production
  goals.
• Such systems meet the requirements of tomorrow's
  manufacturing control systems.

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Concluding Remarks

• Artificial Life is impacting engineering systems
  through Agent-Based architectures
• Current Impact Areas
• Enterprise Integration and Supply Chain
  Management
• Design and Manufacturability Assessments
• Enterprise Planning, Scheduling and Control

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Concluding Remarks

• Current Impact Areas
• Dynamic System Reconfiguration
• Factory Control Architectures
• Holonic Manufacturing Systems
• Distributed Dynamic Scheduling
• Commercial scheduling, routing, and force
  allocation problems
• Use of swarm networks to control swarm Unmanned
  Aerial Vehicles (UAV), or undersea vehicles (UGV)