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Artificial Life: How can it impact engineering

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Artificial Life: How can it impact engineering practices of the future? Cihan H. Dagli Smart Engineering Systems Laboratory Engineering Management Department – PowerPoint PPT presentation

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Title: Artificial Life: How can it impact engineering


1
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

2
Presentation Outline
  • Engineering Systems of the Future
  • What is Artificial Life?
  • Artificial Life in Engineering
  • Concluding Remarks

3
Recent Market Changes
  • Total Globalization
  • Increasing Production Pace
  • Decreasing Production Cycle Times
  • Migration From Mass Production to Mass
    Customization

4
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

5
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.

6
Autonomous Units
7
Autonomous Engineered Entity
8
Autonomous Engineered Enterprises
9
Evolutionary Color Images Karl Sims
10
Evolutionary Color Images Karl Sims
11
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
12
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
13
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)
14
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
15
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
16
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

17
Agent Types
Collaborative Learning Agents
Cooperate
Learn
Smart Agents
Autonomous
Interface agents
Collaborative Agents
18
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

19
Artificial Life Techniques
  • Agent-based modeling
  • Evolutionary programming
  • Genetic algorithms
  • Distributed artificial intelligence
  • Swarm intelligence

20
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

21
If ltcondgt Then ltaction1gt Else ltaction2gt
Artificial world
Inanimate agents
Observer
Animate agents
Data
Organizations of agents
Courtesy of Lars-Erik Cederman
22
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

23
Flow Management
Source www.helbing.org
24
Artificial BIOWAR
Courtesy of K. Carley, A. Yahja, B. Kaminsky
25
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.

26
Evolving Neural Networks
  • To develop a hybrid intelligent system Evolving
    Neural Networks (ENNs) that can be used in data
    mining, especially in classification problems.

27
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

28
Optimizing a NN architecture Using GA
29
Ensemble of ENNs
30
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

31
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).

32
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

33
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

34
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

35
Supervised Learning Assisted Reinforcement
Learning Prediction Architecture for Time-Series
36
Stock Price Prediction
37
Adaptive Model Evolution
38
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.

39
Cooperative Behaviour path Planning for
Autonomous Robots Using Evolutionary Algorithm
Fuzzy Clustering
40
Alice
41
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

42
Artificial Problem SolversMobile Agents
  • George Cybenko and Bob Gray Thayer School of
    Engineering Dartmouth College george.cybenko,rober
    t.gray_at_dartmouth.edu

43
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
44
Artificial Problem Solvers Mobile Agents
  • George Cybenko and Bob Gray Thayer School of
    Engineering Dartmouth College george.cybenko,robe
    rt.gray_at_dartmouth.edu

45
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

46
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-

47
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
48
Emergent- Self assembled Nest
Courtesy of Bonabeau
49
Ant Colony Optimization
1. Straight Pheromone Trail
2. Obstacle Introduced
3. Two Options are Explored
4. Shortest Path Dominates
50
Routing in Communication Networks
51
Future Combat Systems
Courtesy of Riggs J.
52
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

53
PSO Initialization Positions and velocities
Courtesy of Maurice Clerk
54
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
55
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

56
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.

57
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.

58
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

59
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)
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