Swarm Intelligence W13: From Aggregation and Segregation to Structure Building

Transcription

1 Swarm Intelligence W13: From Aggregation and Segregation to Structure Building

2 Stigmergy Quantitative Qualitative Outline Distributed building in: natural systems artificial virtual systems artificial real systems

3 Collective Building in Natural Systems

4 Example of Wasp Nest Guy Theraulaz

5 Example of Wasp Nest (sagittal cut) Guy Theraulaz

6 Further Examples of Wasp Nests Guy Theraulaz

7 Further Examples of Wasp Nests Pascal Goetgheluck

8 Not only Wasps: Termite Nest are also Impressive m high - thermoregulated Masson

9 Coordination Mechanisms for the Building Activity

10 Hyphotheses for Collective Building Mechanisms The plan Environmental template Stigmergy

11 The Plan Hypothesis? Scott Camazine

12 Hyphotheses for Collective Building Mechanisms The plan Environmental template Stigmergy

13 The Environmental Template Hypothesis The building plan pre-exists in the environment under the form of spatial heterogeneities. The social insect activity only outlines these pre-existing environmental template. Environmental changes performed by insects play a minimal role in the building activity itself. This is a form of centralized information seen from the building insects perspective There are several forms of environmental template: gradients naturally existing in the environment (humidity, temperature ) chemical gradients generated by one or more (static) individuals of the colony

14 Ex.: Nest Structure in the Ant Acantholepis Custodiens Eggs T Larvae Pupae 3.00 a.m p.m.

15 Ex.: Termite Nest built around the Queen

16 Hyphotheses for Collective Building Mechanisms The plan Environmental template - centralized Stigmergy

17 Stigmergy Definition It defines a class of mechanisms exploited by social insects to coordinate and control their activity via indirect interactions and stimulant signs left in the environment. Response R 1 R 2 R 3 R 4 R 5 Stimulus S 1 S 2 S 3 S 4 S 5 Stop time Stigmergic mechanisms can be classified in two different categories: quantitative (or continuous) stigmergy and qualitative (or discrete) stigmergy

18 Stigmergy The role of the two different stigmergic mechanisms in collective building: 1 2 Sequence of stimuli and answers quantitatively different Positive feedback and self-organized spatial pattern Sequence of stimuli and answers qualitatively different

19 Quantitative Stigmergy Features The successive stimuli quantitatively differentiate in their amplitude and merely modify the answer probability of other individuals Examples : mass recruitment in ants dead ant aggregation in ant cemetery pillar building in termites

20 Quantitative Stigmergy Pillar building in termites Spatial distribution of insects and their building activity are locally controlled by the pheromone density. Termites impregnate with pheromones the building material Pheromones diffuse in the environment Individuals carrying ground bullets follow the chemical gradient; they climb towards the highest pheromonal concentration and drop there their bullets Material drop rate is proportional to the number of active insects in the local region (positive feedback)

21 Simulation of Termite Nest Building: Pillar Bootstrapping

22 Simulation of Termite Nest Building: Pillar Bootstrapping

23 Stigmergy The role of the two different stigmergic mechanisms in collective building 1 Sequence of stimuli and answers quantitatively different 2 Sequence of stimuli and answers qualitatively different Self-assembly process

24 Qualitative Stigmergy Features Successive stimuli are qualitatively different. This process generates a self-assembly dynamics. A B C S 1 S 2 S 3 time Note: Here insects transport bricks that self-assemble; in other systems the elements are active and can selfassemble themselves

25 Summary of Collective Building Mechanisms in Natural Systems 1 The plan 2 3 Environmental template - centralized Stigmergy - distributed Blend!

26 Nest Building in Polist Wasps

27 Nest Building in Polist Wasps Guy Theraulaz

28 Nest Building in Polist Wasps Bootstrapping a nest: anchoring to a support Guy Theraulaz

29 Nest Building in Polist Wasps Organization of the building activity It is indirectly carried out via the different, local configurations a wasp can find in the nest A probability of deposing a new cell is associated to each configuration

30 Nest Building in Polist Wasps Potential sites for building S 2 S 1 S 2 S 1 S 1 S 2 S 1 S 3 S 1 S 2 S 1 S 1

31 Nest Building in Polist Wasps Probability of creating a new cell given the configuration of neighboring cells 1,0 0,8 0,6 0,4 0,2 0, Number of adjacent cell walls

32 Simulation of the nest Building Process

33 A Swarm on a 3D Lattice: The Nest Simulator A swarm of nest builders agent features Reactive actions Random movements on a 3D lattice, no trajectories No embodiment (1agent = 1 single cell), no interference (however 2 agents cannot occupy the same cell) n agents working together means n actions (not necessarily n dropped bricks) before next iteration starts; all n agents see the same overall nest configuration at a given iteration Agents team is homogeneous No global plan of the whole building Local perception of the environment

34 The Nest Simulator Definition of agent s neighborhood for hexagonal cells The neighborhood of each agent is defined as the 20 cells around it (7 above, 6 around, and 7 below). z + 1 z + 1 z z - 1 z z X A X Neighborhood Cell contents

35 The Nest Simulator Distributed nest building: several sites are active simultaneously

36 The Nest Simulator Examples of building rules Z+1 z Building process is irreversible (no cell can be removed) Deposit rules can be strictly deterministic or probabilistic Z-1

37 Example of Rule Set Example of a rule set (Polist wasps, 7 rules)

38 Example of Nest Structure Obtained structures (Polists wasps, 7 rules) With deterministic rules With probabilistic rules

39 Looking for Stable Nest Architectures What kind of nest architectures can we build with the same method? What are the rules to implement in order to obtain stable architectures? An architecture is considered stable when several runs of a simulation with the same rule set (but different random generator seed) generate architectures with the same global structure. Mechanisms of stigmergic coordination reduce the number of stable architectures.

40 Examples of Stable Architectures Agelaia (13 rules)

41 Examples of Stable Architectures Parachatergus (21 rules) Vespa (13 rules) Stelopolybia (12 rules)

42 Examples of Stable Architectures Chatergus (39 rules) Artificial Nest Structure (35 rules)

43 Looking for Stable Nest Architectures How to build a stable architecture? The building process is carried out in successive steps. The current local configuration generate a stimulus different from that of the previous and of the successive configuration (qualitative stigmergy). Use of «bottleneck» rules for repeated pattern (ex. floor-to-floor coordination) Only this type of building algorithms generate coherent architectures. The whole set of these algorithms generates a limited number of nest shapes.

44 Reverse Engineering a Nest Structure using EC Techniques Fitness function: weighted sum of space filling and pattern replication arbitrary criteria based on 17 human observers who were asked to evaluate the amount of structure in a set of 29 different patterns, [Bonabeau 2000]) Biased evolution: Probabilistic templates (reduction of stimulating configurations containing a large number of bricks). Start microrule. No diagonal deposits (space not filled). Problems: Fitness function: Functional? Esthetical? Biological plausible? Mathematical definition? Disruptive effect of crossover; preservation of variable length blocks of rules (sequence of microrules needed for coordinated algorithms).

45 Collective Building in Embedded Artificial Systems

46 2D Structure Building using Kheperas Robot behavior Fully autonomous, reactive, noncommunicating, non-adapting behavior (Discrete) quantitative stigmergy less important than in Beckers et al 1994: the bigger, the more stable the cluster big cluster (> 2) = number of manipulation sites as cluster of 2 seeds almost no difference between cluster incrementing and decrementing probabilities Qualitative stigmergy: 2 rules in interaction with cluster: Avoid if interaction with the cluster body Manipulate if interaction with cluster tips 1robot state: loaded, unloaded

47 Flowchart of the Individual Behavior Start Look for seeds Object detected? N N Y Carrying a seed? Y N Pick up the seed and decrement cluster s size Obstacle? Obstacle? Y Y N Obstacle avoidance Drop the seed and increment cluster s size

48 Probabilistic Model Start Look for seeds N Object detected? Probots +Pwalls +Pclusters (kt) Carrying a seed? 1 - [Probots +Pwalls + Pclusters (kt)] Y N Obstacle? Y Y Obstacle? Pick up the seed and decrement cluster s size Drop the seed and increment cluster s size Obstacle avoidance

49 Probabilistic Model Incrementing probabilities Decrementing probabilities Perimeters are relevant for computing the cluster modifying probabilities: robot turns on the spot for object distinction before approaching the cluster!

50 Model: Aggregation Dynamics d i (kt) = decr_geom_probability i *p_find i (kt) c i (kt) = incr_geom_probability i *p_find i (kt) p_find i (kt) = finding probability of all the cluster of size i If n = number of seeds -> macroscopic model of environment with n nonlinearly coupled ODE (n for each possible cluster size) + robot states

51 Model: Explanations and Predictions Single cluster? The model predicted yes and in how much time! Number of clusters (inter-distance between seeds = 1seed) monotonically decreases if: Probability to create a NEW cluster of 1 seed in the middle of the arena is equal to zero No hard partitioning of the arena (robot homogeneously mix clusters) Cluster are not broken in two parts by removing one seed in the middle

52 Khepera Aggregation Experiments Embodied simulation (Webots) Real robots (Khepera) Webots2 ± 10% white noise on all sensor and actuators Perfectly homogeneous team Kinematic simulator Electrical floor: continuous power supply in any position and orientation Heterogeneities among teammates and components Inaccuracies in acting and sensing Dynamics (e.g., friction) plays a role

53 Results (till single cluster) 3 robots real robots (5 runs), Webots (10 runs), probabilistic model (100 runs) [Martinoli, Ijspeert, Mondada, 1999] Mean size of clusters Size of the biggest cluster Number of clusters

54 Example of arising 2D Structures Noise in S&A and poor navigation capabilities do not allow for precise, controllable structure building Webots Real robots

55 Conclusion

56 Take Home Messages Both types of stigmergy (quantitative and qualitative) play a crucial role in natural distributed building processes Not only self-organization and stigmergy but also environmental templates can be used for coordinating swarm systems activities Local rules for quantitative stigmergy can be deterministic or probabilistic, with major impact on structure compactness Very simple hardwired interaction geometries between agents and object to manipulated can generate interesting structures Once again microscopic (realistic and simplified) and macroscopic models as well as computational techniques (e.g. EC) can help in understanding, designing, and optimizing real distributed building systems

57 Additional Literature Week 13 Papers: Bonabeau E., Guérin S., Snyers D., Kuntz P. and Theraulaz G., Three- Dimensional Architectures grown by Simple Stigmergic agents. Biosystems, 56(1): 13-32, Agassounon W., Martinoli A., and Easton K., Macroscopic Modeling of Aggregation Experiments using Embodied Agents in Teams of Constant and Time-Varying Sizes. Autonomous Robots, special issue on Swarm Robotics, Dorigo M. and Sahin E., editors, 17(2-3): , Martinoli A., Ijspeert A. J., and Mondada F., Understanding Collective Aggregation Mechanisms: From Probabilistic Modelling to Experiments with Real Robots. Special issue on Distributed Autonomous Robotic Systems, Dillmann R., Lüth T., Dario P., and Wörn H., editors, Robotics and Autonomous Systems, 29(1): 51-63, Martinoli A., Ijspeert A. J., and Gambardella L. M., A Probabilistic Model for Understanding and Comparing Collective Aggregation Mechanisms. In Floreano D., Mondada F., and Nicoud J.-D., editors, Proc. of the Fifth Europ. Conf. on Artificial Life, September 1999, Lausanne, Switzerland. Lectures Notes in Artificial Intelligence (1999), Vol. 1674, pp

58 Course Conclusion

59 Take Home Messages Swarm-Intelligent Systems (SISs) shows natural, artificial-virtual, and artificial-real forms SISs are self-organized systems; selforganization forms between natural and artificial systems might differ SISs not only exploit self-organization but also stigmergy, templates, local intelligence, local communication for coordinating the activities of their units and improving swarm performance

60 Take Home Messages Artificial and natural SISs differ quite a bit in their HW substrate and formal models proposed for natural systems can often be applied in a straightforward way in virtual/computational scenarios but with more care in the embedded/real world (Formal) analysis and synthesis methods for SISs are still in their infancy Swarm Intelligence is one form of Collective Intelligence/Distributed Intelligence and its definition and perimeter boundaries are continuously evolving

61 The end: Thanks for your attention over the whole course!