Yaochu Jin

1
From Interactive Evolutionary Algorithms
toAgent-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

2
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
Interactive EA
3
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 high-
• performance regions

4
Current Applications of IEAs (II)

• Evolutionary Computer Graphics
• Evolutionary Music (GenJam 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

5
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)

6
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

7
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

8
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)

Centralised Agents
9
Agent-Based Systems (III)

• Heterogeneous non-communicating MAS (HNC-MAS)
• Heterogeneous communicating MAS (HC-MAS)

HNC-MAS
HC-MAS
10
Agent-Based Systems (IV)

• System Architectures
• Facilitators (Federation Multi-Agent Architecture)

11
Agent-Based Systems (V)

• Mediator-Centric Federation Architecture

• Autonomous Agent Approach

12
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
13
Design Tools

• General
• C
• Java

• Specialised
• Agent Building Shell
• Voyager
• ZEUS (BT)

14
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

15
Agent-based Design Optimisation First Step
16
Agent-based Design Optimisation Next Step
17
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.