From Interactive Evolutionary Algorithms to Agentbased Evolutionary Design

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From Interactive Evolutionary Algorithms to Agent-based Evolutionary Design • Interactive Evolutionary Algorithm – When

From Interactive Evolutionary Algorithms to Agent-based Evolutionary Design • Interactive Evolutionary Algorithm – When and How – Current Applications of IEAs – Requirements and Remaining Problems • Agent-based Systems – A Brief Introduction – Current Applications of ABSs • Agent-based Design Optimisation: Some Ideas Yaochu Jin FTR/HRE-D August, 2000 1

Interactive Evolutionary Algorithm • When to use IEAs – No objective function is explicitly/mathematically

Interactive Evolutionary Algorithm • When to use IEAs – No objective function is explicitly/mathematically available – Multiple criteria decision-making/optimisation – Task decomposition for large-scale problems • How Conventional EA Yaochu Jin FTR/HRE-D August, 2000 2 Interactive EA

Current Applications of IEAs (I) • Interactive Evolving of a 8 -legged Robot (Gruan

Current Applications of IEAs (I) • Interactive Evolving of a 8 -legged Robot (Gruan et al ) – Syntactic constraints – Problem decomposition – Hardwire of fitness • Interactive Multi-criteria Decision-Making (Tanino et al) – Identify satisfactory and unsatisfactory solutions – Input desired level for each objective – Provide the worst allowable value for each object • Interactive Evolutionary Design Systems (Parmee et al) – On-line preferences, constraints – Dynamic problem decomposition – Identification of highperformance regions Yaochu Jin FTR/HRE-D August, 2000 3

Current Applications of IEAs (II) • Evolutionary Computer Graphics • Evolutionary Music (Gen. Jam:

Current Applications of IEAs (II) • Evolutionary Computer Graphics • Evolutionary Music (Gen. Jam: GA for generating Jazz solo) • Speech Processing for Hearing Aid (adjusting filter parameters) • Virtual Reality Control of an Arm Wresting Robot • Fashion Design • Layout Design (Web page, GUI display design) • Engineering Design (cars, concrete arc dam, suspension bridge) • Knowledge Acquisition and Data Mining Yaochu Jin FTR/HRE-D August, 2000 4

Requirements and Remaining Problems • Requirements – Smaller population – Fast convergence – Capable

Requirements and Remaining Problems • Requirements – Smaller population – Fast convergence – Capable of combining quantitative and qualitative evaluations • Remaining Problems – How to make the arduous task of the human evaluator easier a) Human evaluation is done in every N generations (as evolution control), the rest is done using an approximate model b) Improving the Interface – How to better co-ordinate and control different components of an IEA (Problem decomposition, knowledge incorporation, preferences for multiple objectives, constraints etc) Yaochu Jin FTR/HRE-D August, 2000 5

What is an Agent? An autonomous agent is a system situated within and part

What is an Agent? An autonomous agent is a system situated within and part of an environment that senses environment and acts on it over time, in pursuit of its own agenda and so as to effect what it senses in the future. (Franklin and Graesser, 1996) An agent should have the capability: • to communicate and • to learn There are • Biological agents • Robotic agents • Computational agents Yaochu Jin FTR/HRE-D August, 2000 6

Agent-Based Systems (I) • When Do We Need Agent-Based Systems – – – Different

Agent-Based Systems (I) • When Do We Need Agent-Based Systems – – – Different components with different (possibly conflicting) goals Parallelism Robustness Scalability An approach to Intelligence • What is Agent-Based systems Yaochu Jin FTR/HRE-D August, 2000 7

Agent-Based Systems (II) • Important Issues – Agent structure (degree of heterogeneity, reactive/deliberative, benevolent/competitive,

Agent-Based Systems (II) • Important Issues – Agent structure (degree of heterogeneity, reactive/deliberative, benevolent/competitive, etc. ) – System architecture (communication protocols etc. ) – Learning (reinforcement learning, learn from others, e. g. stigmergy, modelling of others state, evolving) • Agent Structures – Homogeneous non-communicating MAS (Centralised Agents) Yaochu Jin FTR/HRE-D August, 2000 Centralised Agents 8

Agent-Based Systems (III) – Heterogeneous non-communicating MAS (HNC-MAS) – Heterogeneous communicating MAS (HC-MAS) HNC-MAS

Agent-Based Systems (III) – Heterogeneous non-communicating MAS (HNC-MAS) – Heterogeneous communicating MAS (HC-MAS) HNC-MAS Yaochu Jin FTR/HRE-D August, 2000 HC-MAS 9

Agent-Based Systems (IV) • System Architectures – Facilitators (Federation Multi-Agent Architecture) Yaochu Jin FTR/HRE-D

Agent-Based Systems (IV) • System Architectures – Facilitators (Federation Multi-Agent Architecture) Yaochu Jin FTR/HRE-D August, 2000 10

Agent-Based Systems (V) – Mediator-Centric Federation Architecture – Autonomous Agent Approach Yaochu Jin FTR/HRE-D

Agent-Based Systems (V) – Mediator-Centric Federation Architecture – Autonomous Agent Approach Yaochu Jin FTR/HRE-D August, 2000 11

Current Applications OF ABSs • • Software Design Planning and Scheduling in Manufacturing Air

Current Applications OF ABSs • • Software Design Planning and Scheduling in Manufacturing Air Traffic Control Robotics – Robot leg control, robot joint (multiple arm) control – Multiple robots • Economic Systems and E-Commence (negotiation etc. )* • Engineering Design • Electric Power Systems * A special issue on “Agent-based Modeling of Evolutionary Economic Systems” will appear on IEEE TEC Yaochu Jin FTR/HRE-D August, 2000 12

Design Tools • General – C++ – Java Yaochu Jin FTR/HRE-D August, 2000 •

Design Tools • General – C++ – Java Yaochu Jin FTR/HRE-D August, 2000 • Specialised – Agent Building Shell – Voyager – ZEUS (BT) 13

What can ABSs bring about for design? • ABSs are capable of – –

What can ABSs bring about for design? • ABSs are capable of – – Automatic task decomposition Efficient knowledge incorporation and user interaction Handling distributed constraints Handling conflicting multiple criteria • Well-developed methodologies are available • More sophisticated design tools can be used • Possible application to robot behaviour control Yaochu Jin FTR/HRE-D August, 2000 14

Agent-based Design Optimisation: First Step Yaochu Jin FTR/HRE-D August, 2000 15

Agent-based Design Optimisation: First Step Yaochu Jin FTR/HRE-D August, 2000 15

Agent-based Design Optimisation: Next Step Yaochu Jin FTR/HRE-D August, 2000 16

Agent-based Design Optimisation: Next Step Yaochu Jin FTR/HRE-D August, 2000 16

Conclusion • Agent-based evolutionary design provides a more systematic approach to Design of Complex

Conclusion • Agent-based evolutionary design provides a more systematic approach to Design of Complex Systems • Expect to see papers on Agent-based structural design* * A project proposal is written by a professor at TU Darmstadt for agent-based structural design. No further information is available. * A recent survey paper on On-line Soft Computing Conference suggests that interactive and more systematic approach to incorporate qualitative knowledge will be one important trend for Evolutionary Engineering Design. Yaochu Jin FTR/HRE-D August, 2000 17