Asynchronous Evolution: Emergence of Signal-Based Swarming

Asynchronous Evolution: Emergence of Signal-Based Swarming Olaf Witkowski and Takashi Ikegami University of Tokyo, Japan [email protected] Abstract towards a common direction. The movement itself may differ from species to species. Since Reynolds boids, swarming behavior has often been re- For example, fish and insects swarm in three dimensions, produced in artificial models, but the conditions leading to whereas herds of sheep move only in two dimensions. More- its emergence are still subject to research, with candidates over, the collective motion can have quite diverse dynamics. ranging from obstacle avoidance to virtual leaders. In this While birds migrate in relatively ordered formations with paper, we present a multi-agent model in which individuals develop swarming using only their ability to listen to each constant velocity, fish schools change directions by align- others signals. Our model uses an original asynchronous ge- ing rapidly and keeping their distances, and insects swarms netic algorithm to evolve a population of agents controlled by move in a messy and random-looking way (Budrene et al. artificial neural networks, looking for an invisible resource 1991, Czirok´ et al. 1997, Shimoyama et al. 1996). in a 3D environment. The results demonstrate that agents Numerous evolutionary hypotheses have been proposed use the information exchanged between them via signaling to form temporary leader-follower relations allowing them to to explain swarming behavior across species. These include flock together. more efficient mating, good environment for learning, com- bined search for food resources, and reducing risks of pre- dation (Zaera et al., 1996). Partridge and Pitcher (1979) also Introduction mention energy saving in fish schools by reducing drag. In an effort to test the multiple theories, the past decades The ability of fish schools, insect swarms or starling mur- counted several experiments involving real animals, either murations (Figure 1) to shift shape as one and coordinate inside an experimental setup (Partridge, 1982; Ballerini their motion in space has been studied extensively because et al., 2008) or observed in their own ecological environ- of their implications for the evolution of social cognition, ment (Parrish and Edelstein-Keshet, 1999). Those experi- collective animal behavior and artificial life (Couzin 2009). ments present the inconvenience to be costly to reproduce. Furthermore, the colossal lapse of evolutionary time needed to evolve swarming, make it almost impossible to study the emergence of such behavior experimentally. Computer modeling has recently provided researchers with new, easier ways to test hypotheses on collective behavior. As it is well known, simulating individuals on ma- chines offers easy modification of setup conditions and parameters, tremendous data generation, full reproducibility of every experiment, and easier identification of the underlying dynamics of complex phenomena. From Reynolds’ boids to recent approaches Figure 1: Starling murmuration1 In a massively cited paper, Craig Reynolds (1987) intro- duces the boids model simulating 3D swarming of agents called boids controlled only by three simple rules: Swarming is the phenomenon in which a large number of individuals organize into a coordinated motion. Using only • Alignment: move in the same direction as neighbours the information at their disposition in the environment, they 1Copyright Walter Baxter and licensed for reuse under this Cre- are able to aggregate together, move en masse or migrate ative Commons Licence. • Cohesion: Remain close to neighbours Simulated agents move around in a three dimensional space, looking for a vital but invisible food resource ran- • Separation: Avoid collisions with neighbours domly distributed in the environment. The agents are emit- ting signals that can be perceived by other individuals’ sen- Various works have since then reproduced swarming be- sors within a certain radius. Both agent’s motion and signal- havior, often by the means of an explicitly coded set of rules. ing are controlled by an artificial neural network embedded For instance, Mataric (1992) proposes a generalization of in each agent, evolved over time by an asynchronous genetic Reynolds’ original model with an optimally weighted com- algorithm. Agents that consume enough food are enabled to bination of six basic interaction primitives2. Hartman & reproduce, whereas those whose energy drops to zero are Benes (2006) come up with yet another variant of the origi- removed from the simulation. nal model, by adding a complementary force to the align- During the first phase, we observe that agents progres- ment rule, that they call change of leadership. Unfortu- sively coordinate into clustered formations, which are pre- nately, in spite of the insight this kind of approach brings served through the second phase. Such patterns do not ap- into the dynamics of swarming, it shows little about the pres- pear in control experiments having the simulation start di- sures leading to its emergence. Many other approaches are rectly from the second phase, with the absence of resource based on informed agents or fixed leaders (Cucker & Huepe locations. If at any point the signaling is switched off, 2008, Su et al. 2009, Yu et al. 2010). the agents immediately stop swarming together. They start For that reason, experimenters attempted to simulate swarming again as soon as the communication is turned swarming without a fixed set of rules, rather by incorporat- back on. Furthermore, it is observed that simulations ing into each agent an artificial neural network brain that with signaling lead to agents gathering very closely around controls its movements. The swarming behavior is evolved food patches, whereas the control simulations with silented by copy with mutations of the chromosomes encoding the agents end up with them wandering around erratically. neural network parameters. By comparing the impact of dif- The main contribution of this work is to demonstrate that ferent selective pressures, this type of methodology eventu- collective motion can originate, without explicit central co- ally allows to analyze the evolutionary emergence of swarm- ordination, from the combination of a generic communica- ing. tion system and a simple resource gathering task. A spe- Tu and Terzopoulos (1994) have swarming emerge cific genetic algorithm with an asynchronous reproduction from the application of artificial pressures consisting of scheme is developed and used to evolve the agents’ neural hunger, libido and fear. Other experimenters have studied controllers. In addition, the search for resource is shown prey/predator systems to show the importance of sensory to improve from the agents clustering, eventually leading system and predator confusion in the evolution of swarm- to the agents gathering closely around goal areas. An in- ing in preys (Ward et al. 2001, Olson et al. 2013). depth analysis shows increasing information transfer be- In spite of many pressures hypothesized to produce tween agents throughout the learning phase, and the devel- swarming behavior, designed setups presented in the liter- opment of leader/follower relations that eventually push the ature are often complex and specific. Previous works typ- agents to organize into clustered formations. ically introduce models with very specific environments, The rest of the paper is organized as follows. The next where agents are given specialized sensors sensitive. While section describes the details of our model. Then simulation they are bringing valuable results to the community, one may settings and results are discussed, before finally drawing a wonder about systems with a simpler design. conclusion in the last section. In addition, even when studies focus on fish or insects that swarm in 3D (Ward et al. 2001) most keep their model in Model 2D. While the swarming can be considered to be similar in most cases, the mapping from 2D to 3D is found to be non- Agents in a 3D world trivial (Sayama 2012). Indeed, the addition of a third degree We simulate a group of agents moving around in a cubic, of freedom may enable agents to produce significantly dis- toroidal arena of 600×600×600. The agents rely on energy tinct and more complex behaviors. to survive. If at any point an agent’s energy drops to zero, it is immediately removed from the environment. The task for Signaling agents in a resource finding task the agents is to get as close as possible to a preset resource This paper studies the emergence of swarming in a popula- spot. By getting close to one of those spots, agents can gain tion of agents using a basic signaling system, while perform- more energy, allowing them to counterbalance the energy ing a simple resource gathering task. losses due to movement and signaling. An agent whose energy drops to zero is removed from the simulation. In this 2Namely, those primitives are collision avoidance, following, regard, the energy also represents each agent’s fitness, and dispersion, aggregation, homing and flocking. in this paper both terms are used interchangeably. The agent’s position is determined by three floating point coordinates between 0:0 and 600:0. Each agent is positioned randomly at the start of the simulation, and then moves at a fixed speed of 1 unit per iteration. The direction of motion is decided by two motors controlling Euler angles for pitch (i.e. elevation) and θ for yaw (i.e. heading). Communication among agents Every agent is also provided with one communication ac- tuator capable of sending signals with intensities (signals are encoded as floating point values ranging from 0:0 to 1:0), and six communication sensors allowing it to de- tect signals produced by other agents up to a distance of 100 from 6 directions, namely frontal (0; 1; 0), rear (0; −1; 0), left (1; 0; 0), right (−1; 0; 0), top (0; 0; 1) and bottom (0; 0; −1)). The communication sensors are imple- mented so that every source point in a 100-radius sphere around the agent is linked to one and only one of its sensors.

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