There is no doubt that the members of a society can influence each other and a minority of them may guide, in some situations, the whole society toward a particular opinion. However, the promoted opinion is not always desirable, and in some cases, it is desired to prevent its propagation in the network. This mission can be done with the help of informed agents that are common agents that act as hidden advertisers. In this article, we will discuss how many and which agents should be selected as informed agents in a way that the final opinions of agents satisfy some given constraints. In the line of solving this problem, the notion of equilibratability is considered, and the problem is formulated as minimizing the zero-norm of available solutions. Knowing that the mentioned problem is NP-hard, some relaxation methods are considered to solve this problem. The efficiency of the proposed methods is investigated for selecting the sparsest solution in a well-known graph. Finally, the required informed agents to resist a group of spreaders in some random graphs [generated with different methods, such as Erdös and Rényi (ER), Watts and Strogatz (WS), and Barabási and Albert (BA)] are compared, and some reasonable results are observed.

A considerable amount of research has been devoted to the proposition of scalable algorithms for influence maximization. A number of such scalable algorithms exploit the community structure of the network. Besides the community structure, real-world social networks possess a different property, known as the layer structure. In this article, we propose a method based on the layer structure to maximize the influence in huge networks. Conducting experiments on a number of real-world networks, we will show that our method outperforms the state-of-the-art algorithms by its time complexity while having similar or slightly better final influence spread. Furthermore, unlike its predecessors, our method is able to show a high entanglement between structure and dynamics by giving insight on the reason why different networks have two contrasting behaviors in their saturation. By “saturation,” we mean a state during the seed selection process after which adjoining new nodes to the initial set will have a negligible effect on increasing the influence spread. We will demonstrate that how our method can predict the saturation dynamics in the networks. This prediction can be used to identify the network structures that are more vulnerable to the fast spread of the rumors.

Email is a type of Web data which is produced in enormous quantities. It is beneficial to detect conversation threads contained in the email corpora for various applications, including discussion search, expert finding and even email clustering and classification. Conversation thread in email corpora can be defined as a cluster of exchanged emails among the same group of people by reply or forwarding on the same topic. According to this definition, we can define parent-child relation between emails, so email conversation threads seem to demonstrate tree structure. This paper presents a new approach based on genetic programming for reconstruction of conversation threads in emails data. This approach considers finding email conversation threads as an optimization problem, and exploits genetic programming to search intelligently in the space of possible solutions. Rather than several studies that have been conducted on this problem, this work concentrates on detecting accurate structure of conversation threads in high recall. This paper provides a comprehensive evaluation on the BC3 data set. Preliminary results suggest that our method provides acceptable precision and higher recall than existing methods.

Positive Influence Dominating Set (PIDS) has applications in Online Social Networks (OSN) such as Viral Marketing and College Drinking Problem. To many reasons finding Minimum PIDS (MPIDS) is very desirable. Beside, one of the most important features that distinguish the graph of OSN from other networks is Power-Law degree distribution. Unfortunately computing MPIDS in Power-Law graph is a NP-Complete problem. Recently, one greedy algorithm has been proposed in the literature for the PIDS problem with time complexity of O(n^3). In this paper, we propose a new greedy algorithm for PIDS which has outstanding time complexity of O(n^2). Theoretical analysis and simulation results are also presented to verify our approach's efficiency. The simulation results reveal that compared to other algorithm, our algorithm efficiently reduces the PIDS size.

A learning mechanism based on Powell's optimization algorithm is proposed to optimize walking behavior of a passivity based biped robot. To this end, a passivity-based biped robot has been simulated in MSC ADAMS and a control policy inspired from humanoid walking is adopted for a stable walking of the robot. Linear controllers try to control the joints of robot in each walking phase to implement the gait proposed by the control policy. Learning is employed using Powell's optimization algorithm to adjust the control parameters so that the robot enters to an optimum limit cycle in a finite time. The fitness function is defined to evaluate the robot's optimum behavior. The results are verified by simulations in SIMULINK+ADAMS.

In our previous works we had developed a framework for self-reconfiguration planning based on graph signature and graph edit-distance. The graph signature is a fast isomorphism test between different configurations and the graph edit-distance is a similarity metric. But the algorithm is not suitable for modules with symmetry. In this paper we improve the algorithm in order to deal with symmetric modules. Also, we present a new heuristic function to guide the search strategy by penalizing the solutions with more number of actions. The simulation results show the new algorithm not only deals with symmetric modules successfully but also finds better solutions in a shorter time.

This article presents a robust and heavy duty physical latching connection mechanism, which can be actuated with DC motors to actively connect and disconnect modular robot units. The special requirements include a lightweight and simple construction providing an active, strong, hermaphrodite, completely retractable connection mechanism with a 90 degree symmetry1and a no-energy consumption in the locked state. The mechanism volume is kept small to fit multiple copies into a single modular robot unit and to be used on as many faces of the robot unit as possible. This way several different lattice like modular robot structures are possible. The large selection for dock-able connection positions will likely simplify self-reconfiguration strategies. Tests with the implemented mechanism demonstrate its applicative potential for self-reconfiguring modular robots.

This project incorporates modular robots as building blocks for furniture that moves and self-reconfigures. The reconfiguration is done using dynamic connection / disconnection of modules and rotations of the degrees of freedom. This paper introduces a new approach to self-reconfiguration planning for modular robots based on the graph signature and the graph edit-distance. The method has been tested in simulation on two type of modules: YaMoR and M-TRAN. The simulation results shows interesting features of the approach, namely rapidly finding a near-optimal solution.

We report the faithful reproduction of the self-organized aggregation behavior of the German cockroach Blattella germanica with a group of robots. We describe the implementation of the biological model provided by Jeanson et al. in Alice robots, and we compare the behaviors of the cockroaches and the robots using the same experimental and analytical methodology. We show that the aggregation behavior of the German cockroach was successfully transferred to the Alice robot despite strong differences between robots and animals at the perceptual, actuatorial, and computational levels. This article highlights some of the major constraints one may encounter during such a work and proposes general principles to ensure that the behavioral model is accurately transferred to the artificial agents.

Using each other's knowledge and expertise in learning - what we call cooperation in learning- is one of the major existing methods to reduce the number of learning trials, which is quite crucial for real world applications. In situated systems, robots become expert in different areas due to being exposed to different situations and tasks. As a consequence, areas of expertise (AOE) of the other agents must be detected before using their knowledge, especially when the exchanged knowledge is not abstract, and simple information exchange might result in incorrect knowledge, which is the case for Q-learning agents. In this paper we introduce an approach for extraction of AOE of agents for cooperation in learning using their Q-tables. The evaluating robot uses a behavioral measure to evaluate itself, in order to find a set of states it is expert in. That set is used, then, along with a Q-table-based feature for extraction of areas of expertise of other robots by means of a classifier. Extracted areas are merged in the last stage. The proposed method is tested both in extensive simulations and in real world experiments using mobile robots. The results show effectiveness of the introduced approach, both in accurate extraction of areas of expertise and increasing the quality of the combined knowledge, even when, there are uncertainty and perceptual aliasing in the application and the robot

Decision trees, being human readable and hierarchically structured, provide a suitable mean to derive state-space abstraction and simplify the inclusion of the available knowledge for a reinforcement learning (RL) agent. In this paper, we address two approaches to combine and purify the available knowledge in the abstraction trees, stored among different RL agents in a multi-agent system, or among the decision trees learned by the same agent using different methods. Simulation results in nondeterministic football learning task provide strong evidences for enhancement in convergence rate and policy performance

In group-living animals, aggregation favours interactions as well as information exchanges between individuals, and allows thus the emergence of complex collective behaviors. In previous works, a model of a self-enhanced aggregation was deduced from experiments with the cockroach Blattella germanica. In this work, this model was implemented in micro-robots Alice and successfully reproduced the aggregation dynamics observed in a group of cockroaches. We showed that this aggregation process, based on a small set of simple behavioral rules and interactions among individuals, can be used by the group of robots to select collectively an aggregation site among two identical or different shelters. Moreover, we showed that the aggregation mechanism allows the robots as a group to "estimate" the size of each shelter during the collective decision-making process, a capacity which is not explicitly coded at the individual level but that simply emerges from the aggregation behaviour.

By using other agents' experiences and knowledge, a learning agent may learn faster, make fewer mistakes, and create some rules for unseen situations. These benefits would be gained if the learning agent can extract proper rules from the other agents' knowledge for its own requirements. One possible way to do this is to have the learner assign some expertness values (intelligence level values) to the other agents and use their knowledge accordingly. Some criteria to measure the expertness of the reinforcement learning agents are introduced. Also, a new cooperative learning method, called weighted strategy sharing (WSS) is presented. In this method, each agent measures the expertness of its teammates and assigns a weight to their knowledge and learns from them accordingly. The presented methods are tested on two Hunter-Prey systems. We consider that the agents are all learning from each other and compare them with those who cooperate only with the more expert ones. Also, the effect of communication noise, as a source of uncertainty, on the cooperative learning method is studied. Moreover, the Q-table of one of the cooperative agents is changed randomly and its effects on the presented methods are examined.

Cooperative learning in a multi-agent system can improve the learning quality and learning speed. The improvement can be gained if each agent detects the expert agents and uses their knowledge properly. In the paper, a cooperative learning method, called weighted strategy sharing (WSS) is introduced. Also some criteria are introduced to measure the expertness of agents. In WSS, based on the amount of its team-mate expertness, each agent assigns a weight to their knowledge. These weights are used in sharing knowledge among agents in our system. WSS and the expertness criteria are tested on two simulated hunter-prey problems and on object pushing systems.