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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 University of Missouri - Rolla Rolla, MO 65409 - 0370 http://www.umr.edu/~dagli [email protected] ADVIS '04 1 Presentation Outline Engineering Systems of the Future What is Artificial Life? Artificial Life in Engineering Concluding Remarks ADVIS '04 2 Recent Market Changes Total Globalization Increasing Production Pace Decreasing Production Cycle Times Migration From Mass Production to Mass Customization ADVIS '04 3 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 ADVIS '04 4 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. ADVIS '04 5 Autonomous Units ADVIS '04 6 Autonomous Engineered Entity ADVIS '04 7 Autonomous Engineered Enterprises ADVIS '04 8 Evolutionary Color Images: Karl Sims ADVIS '04 9 Evolutionary Color Images: Karl Sims ADVIS '04 10 “Trajectories” of Research into Distributed Systems Distributed Artificial Intelligenc e Swarm Intelligence & Synthetic Ecosystems Multiagent Systems Population Biology& Ecological Modeling Artificial Life ADVIS '04 11 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 ADVIS '04 12 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) ADVIS '04 13 Adaptive Autonomous Agents Agent: A system that tries to fulfill a set of goals in a complex, dynamic environment. Environment: Adopted from http://www.rt.el.utwente.nl/agent/ It can sense the environment through its sensors and act upon the environment using its actuators. Modeling Adaptive Autonomous Agents, Pattie Maes ADVIS '04 14 Adaptive Autonomous Agents Goal: An agents goal can take many different forms: End Goals, particular states the agent tries to achieve Adopted from http://www.rt.el.utwente.nl/agent/ Selective reinforcement or reward that the agent attempts to maximize Internal needs or motivations that the agent has to keep within certain viability zones. Modeling Adaptive Autonomous Agents, Pattie Maes ADVIS '04 15 Agent Autonomous Goal-directed Autonomous actions are directed towards the achievement of defined tasks Intelligent Capable of effective independent action Ability to learn and adapt Cooperate Cooperate with other agents to perform a task ADVIS '04 16 Agent Types Cooperate Collaborative Learning Agents Learn Smart Agents Autonomous Interface agents Collaborative Agents ADVIS '04 17 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 ADVIS '04 18 Artificial Life Techniques Agent-based modeling Evolutionary programming Genetic algorithms Distributed artificial intelligence Swarm intelligence ADVIS '04 19 Artificial Problem Solvers: Agent-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 ADVIS '04 20 If <cond> Then <action1> Artificial world Else <action2> Inanimate agents Observer Animate agents Data Organizations of agents ADVIS '04 Courtesy of Lars-Erik Cederman 21 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 ADVIS '04 22 Flow Management Source: www.helbing.org ADVIS '04 23 Artificial BIOWAR detectionprivacy Agent Location, Demographic & Social Network Characteristics Disease Model Geographic Topology Model Environmental Lethality Communication Technology Model Agent Model Move Spatially Exposed What If Scenario Move Information Contracts Manifests Reports Disease Symptoms Shared BSS Database NEDSS Compliant Daily Community Level Reports Impact Analysis ADVIS '04 Courtesy of K. Carley, A. Yahja, B. Kaminsky 24 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. ADVIS '04 25 Evolving Neural Networks To develop a hybrid intelligent system – Evolving Neural Networks (ENNs) – that can be used in data mining, especially in classification problems. ADVIS '04 26 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 ADVIS '04 27 Optimizing a NN architecture Using GA Genetic Algorithms chromosomes Evalutation of neural network performance fitness function: f(x) Translation into neural networks Training neural networks ADVIS '04 28 Ensemble of ENNs Final Decision GA Combining Module GA Evolving NN 1 Evolving NN 2 Evolving NN n Feature Selection Features ADVIS '04 29 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 ADVIS '04 30 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). ADVIS '04 31 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 ADVIS '04 32 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” ADVIS '04 33 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 shorttime performance of forecasting can be obtained ADVIS '04 34 Supervised Learning Assisted Reinforcement Learning Prediction Architecture for Time-Series ADVIS '04 35 Stock Price Prediction ADVIS '04 36 Adaptive Model Evolution ADVIS '04 37 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. ADVIS '04 38 Cooperative Behaviour & path Planning for Autonomous Robots Using Evolutionary Algorithm & Fuzzy Clustering ADVIS '04 39 Alice ADVIS '04 40 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 ADVIS '04 41 Artificial Problem Solvers: Mobile Agents Wired network Technical specs Troop positions Wireless Network Orders and memos George Cybenko and Bob Gray Thayer School of Engineering Dartmouth College george.cybenko,robert.gray}@dartmouth.edu ADVIS '04 42 Static & Mobile Agents Developed for Small Unit Operations Sensor Report Sent Threat identified and Alert 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 ADVIS '04 43 Artificial Problem Solvers: Mobile Agents Continuous data collection NSF 1998 KDI Project Operational simulation 1 Intermittent data collection Mobile agents link weakly coupled distributed components. Data and simulation cloud Operational simulation 2 Unexpected (such as emergency relief) uses George Cybenko and Bob Gray Thayer School of Engineering Dartmouth College {george.cybenko,robert.gray}@dartmouth.edu ADVIS '04 44 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 ADVIS '04 45 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]- ADVIS '04 46 Swarming Characteristics Entities share common goal Local Interaction s Autonomy of units Self Organizatio n Simple rules or units Stigmergy Distribute d Large number or efficient size Pulsing of force Flexible and robust Swarming ADVIS '04 47 Emergent- Self assembled Nest Courtesy of Bonabeau ADVIS '04 48 Ant Colony Optimization 1. Straight Pheromone Trail 2. Obstacle Introduced 3. Two Options are Explored 4. Shortest Path Dominates ADVIS '04 49 Routing in Communication Networks ADVIS '04 50 Future Combat Systems Courtesy of Riggs J. ADVIS '04 51 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 ADVIS '04 52 PSO Initialization: Positions and velocities Courtesy of Maurice Clerk ADVIS '04 53 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 ADVIS '04 54 Artificial Problem Solvers: Synthetic 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] ADVIS '04 55 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. ADVIS '04 56 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. ADVIS '04 57 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 ADVIS '04 58 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) ADVIS '04 59