Evolving team compositions by agent swapping

Optimizing collective behavior in multiagent systems requires algorithms to find not only appropriate individual behaviors but also a suitable composition of agents within a team. Over the last two decades, evolutionary methods have emerged as a promising approach for the design of agents and their compositions into teams. The choice of a crossover operator that facilitates the evolution of optimal team composition is recognized to be crucial, but so far, it has never been thoroughly quantified. Here, we highlight the limitations of two different crossover operators that exchange entire agents between teams: restricted agent swapping (RAS) that exchanges only corresponding agents between teams and free agent swapping (FAS) that allows an arbitrary exchange of agents. Our results show that RAS suffers from premature convergence, whereas FAS entails insufficient convergence. Consequently, in both cases, the exploration and exploitation aspects of the evolutionary algorithm are not well balanced resulting in the evolution of suboptimal team compositions. To overcome this problem, we propose combining the two methods. Our approach first applies FAS to explore the search space and then RAS to exploit it. This mixed approach is a much more efficient strategy for the evolution of team compositions compared to either strategy on its own. Our results suggest that such a mixed agent-swapping algorithm should always be preferred whenever the optimal composition of individuals in a multiagent system is unknown

Evolution of division of labor in artificial societies

Natural and artificial societies often divide the workload between specialized members. For example, an ant worker may preferentially perform one of many tasks such as brood rearing, foraging and nest maintenance. A robot from a rescue team may specialize in search, obstacle removal, or transportation. Such division of labor is considered crucial for efficient operation of multi-agent systems and has been studied from two perspectives. First, scientists address the "how" question seeking for mechanical explanations of division of labor. The focus has been put on behavioral and environmental factors and on task allocation algorithms leading to specialization. Second, scientists address the "why" question uncovering the origins of division of labor. The focus has been put on evolutionary pressures and optimization procedures giving rise to specialization. Studies have usually addressed one of these two questions in isolation, but for a full understanding of division of labor the explanation of the origins of specific mechanisms is necessary. Here, we rise to this challenge and study three major transitions related to division of labor. By means of theoretical analyses and evolutionary simulations, we construct a pathway from the occurrence of cooperation, through fixed castes, up to dynamic task allocation. First, we study conditions favoring the evolution of cooperation, as it opens the doors for the potentially following specialization. We demonstrate that these conditions are sensitive to the mechanisms of intra-specific selection (or "selection methods"). Next, we take an engineering perspective and we study division of labor at the genetic level in teams of artificial agents. We devise efficient algorithms to evolve fixed assignments of agents to castes (or "team compositions"). To this end, we propose a novel technique that exchanges agents between teams, which greatly eases the search for the optimal composition. Finally, we take a biological perspective and we study division of labor at the behavioral level in simulated ant colonies. We quantify the efficiency of task allocation algorithms, which have been used to explain specialization in social insects. We show that these algorithms fail to induce precise reallocation of the workforce in response to changes in the environment. We overcome this issue by modeling task allocation with artificial neural networks, which lead to near optimal colony performance. Overall, this work contributes both to biology and to engineering. We shed light on the evolution of cooperation and division of labor in social insects, and we show how to efficiently optimize teams of artificial agents. We resolve the encountered methodological issues and demonstrate the power of evolutionary simulations to address biological questions and to tackle engineering problems

Evolutionary robotics: model or design?

In this paper, I review recent work in evolutionary robotics (ER), and discuss the perspectives and future directions of the field. First, I propose to draw a crisp distinction between studies that exploit ER as a design methodology on the one hand, and studies that instead use ER as a modeling tool to better understand phenomena observed in biology. Such a distinction is not always that obvious in the literature, however. It is my conviction that ER would profit from an explicit commitment to one or the other approach. Indeed, I believe that the constraints imposed by the specific approach would guide the experimental design and the analysis of the results obtained, therefore reducing arbitrary choices and promoting the adoption of principled methods that are common practice in the target domain, be it within engineering or the life sciences. Additionally, this would improve dissemination and the impact of ER studies on other disciplines, leading to the establishment of ER as a valid tool either for design or modeling purposes

Cooperation in self-organized heterogeneous swarms

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

Methodology for adequate choice of calculation method in optimum design of energy efficient buildings

Pri optimalnom projektovanju energetski efikasnih zgrada neophodno zadovoljiti dva suprotstavljena zahteva – ostvariti najmanji mogući uticaj na okolinu po prihvatljivoj ceni izgradnje, konstrukcije, opreme i troškova održavanja tokom čitavog životnog veka objekta. Da bi se rešio ovaj težak kombinatorni problem, neophodno je primeniti odgovarajuću metodu optimizacije koja će za relativno kratko vreme pružiti dovoljno širok izbor mogućih alternativa. Cilj istraživanja prikazanog u ovoj disertaciji bio je da se ustanovi univerzalan kriterijum za izbor adekvatne proračunske metode u optimalnom projektovanju energetski efikasnih zgrada. Rezultati ukazuju na to da primenjena metodologija pruža detaljan uvid u sposobnost različitih metoda da sprovedu kvalitetnu eksploraciju i eksploataciju prostora pretrage, pri čemu omogućava i podrobnu analizu dobijenog Pareto fronta. Pareto front dobijen u prikazanom numeričkom primeru sastoji se od tri jasno uočljive zone, od kojih dve sadrže rešenja u skladu s jednom od suprotstavljenih funkcija cilja (minimalna cena, odnosno minimalan uticaj na okolinu), dok treća zona predstavlja kompromisna međurešenja. Primenom prikazanog pristupa projektant će moći da razmotri različite, u manjoj ili većoj meri zadovoljavajuće mogućnosti kako bi odabrao optimalno rešenje koje će u dovoljnoj meri zadovoljiti i ekonomski i ekološki kriterijum