Cooperation in self-organized heterogeneous swarms is a phenomenon from nature with many applications in autonomous robots. I specifically analyzed the problem of auto-regulated team formation in multi-agent systems and several strategies to learn socially how to make multi-objective decisions. To this end I proposed new multi-objective ranking relations and analyzed their properties theoretically and within multi-objective metaheuristics. The results showed that simple decision mechanism suffice to build effective teams of heterogeneous agents and that diversity in groups is not a problem but can increase the efficiency of multi-agent systems.

Cooperation in self-organized heterogeneous swarms is a phenomenon from nature with many applications in autonomous robots. I specifically analyzed the problem of auto-regulated team formation in multi-agent systems and several strategies to learn socially how to make multi-objective decisions. To this end I proposed new multi-objective ranking relations and analyzed their properties theoretically and within multi-objective metaheuristics. The results showed that simple decision mechanism suffice to build effective teams of heterogeneous agents and that diversity in groups is not a problem but can increase the efficiency of multi-agent systems.

Groups of starlings can form impressive shapes as they travel northward together in the springtime. This is among a group of natural phenomena based on swarm behaviour. The research field of artificial intelligence in computer science, particularly the areas of robotics and image processing, has in recent decades given increasing attention to the underlying structures. The behaviour of these intelligent swarms has opened new approaches for face detection as well. G. Beni and J. Wang coined the term “swarm intelligence” to describe this type of group behaviour. In this context, intelligence describes the ability to solve complex problems. The objective of this project is to automatically find exactly one face on a photo or video material by means of swarm intelligence. The process developed for this purpose consists of a combination of various known structures, which are then adapted to the task of face detection. To illustrate the result, a 3D hat shape is placed on top of the face using an example application program.:1 Introduction 1.1 Face Detection 1.2 Swarm Intelligence and Particle Swarm Optimisation Fundamentals 3 Face Detection by Means of Particle Swarm Optimisation 3.1 Swarms and Particles 3.2 Behaviour Patterns 3.2.1 Opportunism 3.2.2 Avoidance 3.2.3 Other Behaviour Patterns 3.3 Stop Criterion 3.4 Calculation of the Solution 3.5 Example Application 4 Summary and Outlook

Das Zusammenleben in Gruppen ist im Tierreich ein weit verbreitetes Phänomen. Einer der Vorteile des Gruppenlebens könnte die sogenannte „Schwarmintelligenz“ sein, das heißt die Fähigkeit von Gruppen kognitive Probleme zu lösen, die die Problemlösekompetenz einzelner Individuen übersteigt. In der vorliegenden Dissertation untersuchte ich, ob die Gruppengröße beim Menschen und bei Fischen mit einer verbesserten Entscheidungsfindung einhergeht. Beim Menschen analysierte ich zunächst das Abschneiden von Einzelpersonen, die später als Teil einer Gruppe getestet wurden, in einfachen Einschätzungsaufgaben sowie komplizierteren Satz-Rekonstruktionstests. Meine Frage war, ob es Individuen in Gruppen gelingt bessere Entscheidungen zutreffen als das einem durchschnittlichen Individuum der Gruppe alleine möglich wäre und ob Gruppen sogar die Leistung ihres besten Mitglieds in den individuellen Tests überbieten könnten. Tatsächlich konnte ich zeigen, dass Gruppen die Leistung des besten Mitglieds übertreffen, wenn die Problemstellung für Einzelpersonen zu komplex ist oder sich häufig wiederholt. Weiterhin gelang mir zu zeigen, dass Gruppen von Menschen bei einer simulierten Prädationssituation, ähnlich wie es bereits für andere Tierarten beschrieben wurde, anhand von so genannten „Quorum“-Regeln durch non-verbale Kommunikation entscheiden, ob sie bleiben oder flüchten. Dabei dienen einfache Bewegungsmuster als Schlüsselreiz. Individuen einer Gruppe erhöhen durch diesen Mechanismus gleichzeitig ihre echt positiven und verringern ihre falsch positiven Entscheidungen. Beim Guppy, einem Süßwasserfisch aus Trinidad, untersuchte ich in deren natürlichem Habitat, ob die Fähigkeit einzelner Individuen zwischen einer genießbaren und einer ungenießbaren Futterquelle zu unterscheiden, mit der Gruppengröße ansteigt. Meine Ergebnisse zeigen, dass Guppys mit größerer Wahrscheinlichkeit eine genießbare Futterquelle identifizierten, sobald sie Teil einer größeren Gruppe waren. / Group living is a widespread phenomenon. One of its assumed advantages is collective cognition, the ability of groups to solve cognitive problems that are beyond single individuals’ abilities. In this thesis, I investigated whether decision-making improves with group size in both humans and fish, thus using the strengths of each system. In humans, I tested individual performance in simple quantity estimation tasks and a more difficult sentence reconstruction task first alone and then as part of a group. My question was whether groups were able to improve not only on average individual decisions, but also to beat their best members. Indeed, when a given problem is recurrent or too complex for individuals, groups were able to outperform their best members in different contexts. Furthermore, I showed that in a simulated predation experiment, groups of humans decided to stay or to escape using quorum thresholds based on movement behaviour without verbal communication, as has been shown in other animals. This simple movement mechanism allowed individuals in groups to simultaneously increase true positives and decrease false positives. In the guppy, a freshwater fish from Trinidad, I tested in their natural environment whether individuals’ ability to distinguish between an edible and a non-edible food item increases with group size. My results indicate that guppies had better chances to identify the edible food item when part of bigger groups. By investigating several populations with different ecological backgrounds, in particular differing in predation levels, I found that, despite a lower sampling activity in high predation habitats, predation did not affect the improvement of decisions in groups.