THE EFFECT OF LABORATORY COLONY CONDITION ON THE TROPHALLACTIC INTERACTIONS OF CAMPONOTUS VAGUS (HYMENOPTERA: FORMICIDAE)

In this study, our aim was to give a detailed evaluation of the trophallactic interactions among foragers of Camponotus vagus in outside-nest situation, and to find out which properties of trophallaxis may have a role in the information-flow among workers about current nutritional state of the colony. Measured parameters were the number and frequency of trophallactic interactions, the duration of trophallaxes, the frequency of different duration interactions, and the number and size-class of workers participating in trophallactic events outside the nest. Experiments were conducted both with starved and satiated colonies to examine the effect of feeding condition. We demonstrated that the dynamics of trophallaxis is a stable parameter; it was independent from the starvation level. Similarly,the number of partners and the size class distribution of the different sized worker pairs did not differ between the two colony states. Starvation level influenced both the frequency and mean duration of trophallactic interactions. The frequency-distribution of the duration of trophallactic events showed an exponential trend, i.e., the short term interactions were more frequent than the prolonged ones in both colony states. However, the rate of these two distinguished types of trophallaxis was different in the case of the two colony conditions. Different rates of the short term and prolonged interactions may provide information about the current nutritional requirements of the colony, enhancing the speed and efficiency of colony responses to feeding stress. Frequent short term trophallaxis may not only contribute to a high level of cooperation during retrieval of food among foragers, but also maintain the integration of colony members even outside their nest

Using power-law properties of social groups for cloud defense and community detection

The power-law distribution can be used to describe various aspects of social group behavior. For mussels, sociobiological research has shown that the Lévy walk best describes their self-organizing movement strategy. A mussel\u27s step length is drawn from a power-law distribution, and its direction is drawn from a uniform distribution. In the area of social networks, theories such as preferential attachment seek to explain why the degree distribution tends to be scale-free. The aim of this dissertation is to glean insight from these works to help solve problems in two domains: cloud computing systems and community detection. Privacy and security are two areas of concern for cloud systems. Recent research has provided evidence indicating how a malicious user could perform co-residence profiling and public to private IP mapping to target and exploit customers which share physical resources. This work proposes a defense strategy, in part inspired by mussel self-organization, that relies on user account and workload clustering to mitigate co-residence profiling. To obfuscate the public to private IP map, clusters are managed and accessed by account proxies. This work also describes a set of capabilities and attack paths an attacker needs to execute for targeted co-residence, and presents arguments to show how the defense strategy disrupts the critical steps in the attack path for most cases. Further, it performs a risk assessment to determine the likelihood an individual user will be victimized, given that a successful non-directed exploit has occurred. Results suggest that while possible, this event is highly unlikely. As for community detection, several algorithms have been proposed. Most of these, however, share similar disadvantages. Some algorithms require apriori information, such as threshold values or the desired number of communities, while others are computationally expensive. A third category of algorithms suffer from a combination of the two. This work proposes a greedy community detection heuristic which exploits the scale-free properties of social networks. It hypothesizes that highly connected nodes, or hubs, form the basic building blocks of communities. A detection technique that explores these characteristics remains largely unexplored throughout recent literature. To show its effectiveness, the algorithm is tested on commonly used real network data sets. In most cases, it classifies nodes into communities which coincide with their respective known structures. Unlike other implementations, the proposed heuristic is computationally inexpensive, deterministic, and does not require apriori information

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