To extract structured knowledge from unstructured text sources we need to understand the semantic relationships between entities. State-of-the-art relation extraction techniques take advantage of the abundance of data on the web. However, in domains with sparse data such as social networks which ha ve limited occurrences of entities and relationship patterns, bootstrapping techniques and pattern detection methods are inefficient and inaccurate. In this paper, we introduce REDS, a Relation Extraction approach based on Distant Supervision. REDS extracts the named entities from text documents and assigns a fingerprint to each potential relationship among the named entities. Then, it queries a knowledge repository for similar matches to each fingerprint. An assessor uses the query results and the data statistics to measure the validity of the relationships corresponding to the queried fingerprints, and labels each potential relationship with the predicted type. In addition to handling the relation extraction in presence of data sparsity, REDS uses an information retrieval framework that makes it scalable and capable of dealing with noisy data. We implement and test REDS on a non-English historical archive consisting of unstructured notarial acts and structured civil registers; By means of manual evaluations REDS achieves precision of 0.90.

Entity Resolution (ER) is the process of identifying references referring to the same entity from one or more data sources. In the ER process, most existing approaches exploit the content information of references, categorized as content-based ER, or additionally consider linkage information among references, categorized as context-based ER. However, in new applications of ER, such as in the genealogical domain, the very limited linkage information among references results in a disjoint graph in which the existing content-/context-based ER techniques have very limited applicability. Therefore, in this paper we propose first, to use the homophily principle for augmentation of the original input graph by connecting the potential similar references, and second, to use a Random Walk based approach to consider contextual information available for each reference in the augmented graph. We evaluate the proposed method by applying it to a large genealogical dataset and we succeed to predict 420,000 reference matches with precision 92% and discover six novel and informative patterns among them which can not be detected in the original disjoint graph.

In this paper, a prestige-based evolution process is introduced, which provides a formal framework for the study of linear hierarchies seen in human societies. Due to the deterministic characteristics of the proposed model, we are capable of determining equilibria in closed form. Surprisingly, these stationary points recover the power-law degree distribution as the shared property of the resulting hierarchal networks, explaining the prevalence of hierarchies in societies. This result sheds light on the evolutionary advantages of hierarchies.

This paper presents a theoretical as well as empirical study on the evolution of cooperation on complex social networks, following the continuous action iterated prisoner's dilemma (CAIPD) model. In particular, convergence to network-wide agreement is proven for both evolutionary networks with fixed interaction dynamics, as well as for coevolutionary networks where these dynamics change over time. Moreover, an extension to the CAIPD model is proposed that allows to model influence on the evolution of cooperation in social networks. As such, this work contributes to a better understanding of behavioral change on social networks, and provides a first step towards their active control.

In this paper we investigate coordination principles inspired by the behaviour of honeybees and ants for coordination purposes in multi-robot systems. Specifically, we study the problem instances of bee-inspired robot Foraging and ant-inspired robot Coverage, where Foraging is the problem of exploring the environment in search of food or provisions and Coverage is the problem of deploying a robotic swarm in the environment with the task of maximising the sensor coverage of the environment. To effectively and efficiently solve both problems, distributed multi-robot coordination is required. For the first problem we investigate a bee-inspired solution method. The second problem is studied using a stigmergic approach. In an extensive set of experiments we first study the feasibility of the proposed multi-robot coordination for robotic swarms with extended resources and discuss the benefits and limitations of using these swarms. Furthermore, as the downsizing in swarm robotics becomes increasingly important with ongoing miniaturization in various applications, the feasibility of the proposed coordination techniques for robotic swarms with limited resources is studied in detail; the practical requirements for overcoming the limitations of these swarms are introduced and the main need to incorporate these robots in real world experiments is discussed.

The evolution of cooperation in social networks, and the emergence of these networks using simple rules of attachment, have both been studied extensively although mostly in separation. In real-world scenarios, however, these two fields are typically intertwined, where individuals’ behavior affect the structural emergence of the network and vice versa. Although much progress has been made in understanding each of the aforementioned fields, many joint characteristics are still unrevealed. In this paper we propose the Simultaneous Emergence and Evolution (SEE) model, aiming at unifying the study of these two fields. The SEE model combines the continuous action prisoner’s dilemma (modeling the evolution of cooperation) with preferential attachment (used to model network emergence), enabling the simultaneous study of both structural emergence and behavioral evolution of social networks. A set of empirical experiments show that the SEE model is capable of generating realistic complex networks, while at the same time allowing for the study of the impact of initial conditions on the evolution of cooperation.

We study the evolution of cooperation in social networks, aiming in particular at ways of influencing the behavior in such networks using methods and techniques from optimal control theory. This is of importance to many scenarios where politicians or policy makers strive to push consensus on some topic that may seem suboptimal from individuals' perspectives. To this end, we employ the Continuous Action Iterated Prisoner's Dilemma (CAIPD) as model for the interactions in a social network. This model describes how neighboring nodes influence each other, and in effect determines how different strategies may spread through the network. We extend this model, incorporating a mechanism for external influence on the behavior of individual nodes. Next we prove reachability of an arbitrary network-wide agreement using the Lyapunov's Direct Method. Based on the theory of Linear-Quadratic Trackers we propose a step-wise iterative control algorithm, and show the effectiveness of the proposed controller in various Small World and Scale Free social networks.

This paper a novel adaptive interval type-2 fuzzy neural network (FNN) controller is proposed to synchronize chaotic systems with training data corrupted by noise or rule uncertainties involving external disturbances. Adaptive interval type-2 FNN control scheme and sliding mode approach are incorporated to deal with the synchronization of non-identical chaotic systems. In the meantime, the Lyapunov stability theorem has been used to testify the asymptotic stability of the chaotic systems, based on the adaptive fuzzy sliding mode control. The chattering phenomena in the control efforts can be reduced and the stability analysis of the proposed control scheme will be guaranteed in the sense that all the states and signals are uniformly bounded and the external disturbance on the synchronization error can be attenuated. The simulation example is included to confirm validity and performance of the advocated design methodology.