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Supplementary material for the paper:

From Solitary to Collective Behaviours:
Decision Making and Cooperation

Vito Trianni, Christos Ampatzis, Anders Lyhne Christensen,
Elio Tuci, Marco Dorigo, and Stefano Nolfi

This page contains support material of the paper "From Solitary to Collective Behaviours: Decision Making and Cooperation" submitted to the 9th European Conference on Artificial Life (ECAL 2007). In particular, we provide a detailed description of the experimental setup, which was left out due to space limit. We also provide an extensive behavioural analysis of the obtained results, supported by videos of the evolved behaviours. Finally, we describe the behaviours produced by all controllers that make use of communication.

Behavioural Analysis

We make use of some videos to support the description of the obtained results. In each video, three s-bots move in the arena, in which the way out is either present of not . A green spot on top of the robot indicates that the robot is signalling, and the emitted tone can be perceived by all robots in the arena. All videos are mpeg and weigh about 4M.

The behaviours produced by the successful controllers were grouped in four classes, named U, B, M and C.

Class U

Class U= {C4,C6,C14,C17} encompasses the "unsuccessful" controllers, that is, those controllers that only result in partial solutions to the problem. These controllers produce appropriate searching behaviour when s-bots are in state S. However, when s-bots are placed in environment B they fail in systematically aggregating

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Class B

The class B={C1,C5,C8,C10,C16} encompasses controllers that produce a strategy named "bouncing" after the aggregation behaviour of the s-bots in state C, which search for each other by continuously bouncing off the circular band. In the following, we describe the behaviour produced by controller C10

This video shows that in environment A, all s-bots individually search for the way out. Notice that there is no communication between the s-bots, because they always emit a signal which consequently does not carry any information.

The switch from state S to state C is performed individually by each s-bot without any reference to the state of the other individuals. In environment B, aggregation is the result of a bouncing behaviour, as shown in this video: when s-bots are placed in environment B they cannot escape from the closed area delimited by the circular band, and therefore they sooner or later meet and aggregate.

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Class M

The class M={C3,C7,C11,C13,C19,C20} encompasses controllers that produce a strategy named "meeting", due to the fact that s-bots aggregate by encountering at a meeting point, which is normally close to the centre of the arena. Similarly to class B, controllers of this class do not make use of communication (exception made for C7 and C19, which are described later) and the decision to switch from state S to state C is performed individually. The main difference with class B controllers resides in the aggregation behaviour, that lets robots leave the band and move in circles close to the centre of the arena, waiting for the other s-bots to reach a similar position. In the following, we present the behaviour produced by controller C20

This video shows how the searching behaviour is systematically performed by the s-bots placed in environment A. Robots never signal, and therefore do not cooperate to solve the task.

In environment B, s-bots aggregate after having searched for the way out, as shown in the video. The switch from state S to state C is performed individually and we observe that an s-bot performs more than one loop on the circular band. This is probably a strategy shaped by evolution to ensure that few decision errors are made. Afterwards, s-bots leave the circular band and move towards the centre of the arena, where they meet with the other robots. This "meeting-point" strategy simplifies the aggregation of the s-bots, because they do not need to search much for the other individuals.

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Class C

The last class C={C2,C9,C12,C15,C18} is named "cooperative" because it encompasses controllers that produce communicative behaviours exploited for cooperation. Here, the perception of a signal triggers the way out searching behaviour. On the contrary, aggregation is possible only if there is no robot signalling in the arena. The switch from state S to state C is coordinately performed when all robots have spent enough time searching for the way out without finding it. On the contrary, if one robot finds the way out, it emits a signal that allows all other s-bots to remain in state S. In the following, we give a detailed description of the behaviour produced by controllerC18, showing how the use of communication can lead to robustness and efficiency.

Looking at the video, is it possible to notice how, also in this case, s-bots systematically search for the way out. The first s-bot that finds it starts moving away from the centre of the arena while emitting a sound signal. The other robots, instead, stop signalling after some time, but they anyway continue to search for the way out in response to the perceived signal. In other words, the s-bots that stop signalling indicate that they have spent enough time searching for the way out. Nevertheless, a perceived signal indicates either that the other s-bots are still searching, or that some s-bot already found the way out. In both cases, the s-bot does not switch to the aggregation behaviour. As a consequence, the behaviour is very robust because individual errors are limited by the possibility to share information.

On the contrary, when s-bots are placed in environment B, they all stop signalling after having searched for the way out for enough time. As soon as all s-bots stop signalling, the aggregation behaviour starts and s-bots efficiently aggregate. The absence of a signal is therefore the cue that is exploited by the s-bots to recognise that there is no way out. Notice that s-bots efficiently split the task of searching the way out among the members of the group: they initially move in different directions and individually cover approximately one third of the circular band. This is enough to ensure that collectively the way out can be systematically found. As a consequence, the system as a whole is more efficient because less time is necessary to search the whole circular band.

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