DAMAN (Directed Assembly in Multi-agent Networks)

1. DAMAN(Directed Assembly in Multi-agent Networks) Principal Investigators Mikhail Prokopenko, Geoff Poulton, Phil Valencia (ICTC), Lech Wieczorek (CIP) Other Contributors Peter Wang, Vadim Gerasimov, Ying Guo, Jiaming Li, Geoff James (ICTC) Geoff Poulton

2. Aim of DAMAN • To find general methods for the design and control of large complex and intelligent networks: • Sensor networks • Self-assembly networks • Communications networks • Power distribution networks. • Part of the GREMLab collaborative project • Similar, if broader, aims • Included support from ICT Emerging science • Initial Foci • Robust sensor networks for the Ageless Aerospace Vehicle (NASA) • Directed self-assembly of intelligent particles • First meso-scale, leading to nanoscale Geoff Poulton

3. Large Networks: Why is it hard to find general rules to make them do what we want? • Networks which are sufficiently large and connected are usually complex. • Exhibit emergent behavior, which is… • Hard to predict • Even harder to model successfully • Finding general design rules is not easy. Geoff Poulton

4. DAMAN Project History – The Good News • DAMAN began formally in January, 2003 • Very successful project • GREMLab collaboration worked well • Enthusiastic team • All milestones exceeded • 15 publications (to June 04) • including one book chapter • Two more submitted, plus another book chapter in preparation. Geoff Poulton

5. DAMAN Project History – The Bad News However… • Large changes to Emerging Science in 2003 • Existing ES funds largely distributed to Divisions • ICT Centre decision • Combine all existing ES funds • Call for competitive bids for new projects • No advantage for existing projects • Unfortunately DAMAN was not selected by this process • No coherent reasons given for the decision • In consequence, DAMAN ceased operations on 30 June. Geoff Poulton

6. Progress Geoff Poulton

7. Main Design Approach -Top-Down/Bottom-Up (TDBU) Solution Space (Desirable Goals) Achievable Goals Maybe emergent Intermediate “Entities” Emergent behaviour emergent behaviour (Complex) Network – Base Components Geoff Poulton

8. 1. Ageless Aerospace Vehicle (AAV) • CONCEPT DEMONSTRATOR • Hexagonal structure, 48 x 1mm Al panels • 192 tiles, 768 piezoelectric sensors NASA CONCEPT TILE SIMULATED MULTI-AGENT SKIN Geoff Poulton

9. AAV Achievements • Impact Boundaries • Local agent response to damage producing emergent behaviour • Stable, reportable • Initiates next stage: diagnosis, reporting, action (eg. repair, mitigation) • Metrics to evolve boundaries • Entropy-based • Reward stability (temporal) and uniformity (spatial) • Impact networks • Link regions of damage on the surface • Useful in diagnosis • Local, ant-based algorithm Geoff Poulton

10. Demo – Impact Boundary Formation Geoff Poulton

11. Example Goal: Rectangular rings of any size GA or other Intermediate “Entities” – Enzymes GA emergent behaviour 2-D Mesoblock Agents 2. Directed Self-assembly - 2D Mesoblocks + • Sides +, - or 0 • Opposites stick • State machine can change signs • On stick or unstick STATE MACHINE - 0 0 Change Sense An analogue of nano- or molecular scale self-assembly Geoff Poulton

12. Some Examples – a Single-block “Virus” • Block polarities (+1 +1 +1 +1) • Single internal rule: • (-1 -1 -1 -1) (1 0 0 0)  (+1 +1 +1 +1) E E E Geoff Poulton

13. 3-block Self-replicating Enzyme • Enzyme reproduces itself as well as forming another 6-block structure Finish Start Geoff Poulton

14. “Rectangle Factory” Example • Much more complex • Capable of self-assembling rectangles of variable size and shape • Matlab version only at present Initial - enzyme Final – enzyme + product Geoff Poulton

15. Conclusions • DAMAN was an exciting, relevant and successful project, unfortunately cut short. • Work continues within GREMLab • More targeted to the needs of existing ICT projects Geoff Poulton