Using evolution strategy for cooperative focused crawling on semantic web

Conventional focused crawling systems have difficulties on contextual information retrieval in semantic web environment. In order to deal with these problems, we propose a cooperative crawler platform based on evolution strategy to build semantic structure (i.e., local ontologies) of web spaces. Mainly, multiple crawlers can discover semantic instances (i.e., ontology fragments) from annotated resources in a web space, and a centralized meta-crawler can carry out incremental aggregation of the semantic instances sent by the multiple crawlers. To do this, we exploit similarity-based ontology matching algorithm for computing semantic fitness of a population, i.e., summation of all possible semantic similarities between the semantic instances. As a result, we could efficiently obtain the best mapping condition (i.e., maximizing the semantic fitness) of the estimated semantic structures. We have shown two significant contributions of this paper; (1) reconciling semantic conflicts between multiple crawlers, and (2) adapting to evolving semantic structures of web spaces over time.

A cooperative behavior learning control of multi-robot using trace information

The distributed autonomous robotic system has superiority of robustness and adaptability to dynamical environment, however, the system requires the cooperative behavior mutually for optimality of the system. The acquisition of action by reinforcement learning is known as one of the approaches when the multi-robot works with cooperation mutually for a complex task. This paper deals with the transporting problem of the multi-robot using Q-learning algorithm in the reinforcement learning. When a robot carries luggage, we regard it as that the robot leaves a trace to the own migrational path, which trace has feature of volatility, and then, the other robot can use the trace information to help the robot, which carries luggage. To solve these problems on multi-agent reinforcement learning, the learning control method using stress antibody allotment reward is used. Moreover, we propose the trace information of the robot to urge cooperative behavior of the multi-robot to carry luggage to a destination in this paper. The effectiveness of the proposed method is shown by simulation. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

A comprehensive evaluation of the methods for evolving a cooperative team

This article focuses on the techniques of evolutionary computation for generating players performing tasks cooperatively. However, in using evolutionary computation for generating players performing tasks cooperatively, one faces fundamental and difficult decisions, including the one regarding the so-called credit assignment problem. We believe that there are some correlations among design decisions, and therefore a comprehensive evaluation of them is essential. We first list three fundamental decisions and possible options in each decision in designing methods for evolving a cooperative team. We find that there are 18 typical combinations available. Then we describe the ultimately simplified soccer game played on a one-dimensional field as a testbed for a comprehensive evaluation for these 18 candidate methods. It has been shown that some methods perform well, while there are complex correlations among design decisions. Also, further analysis has shown that cooperative behavior can be evolved, and is a necessary requirement for the teams to perform well even in such a simple game. This work was presented in part at the 10th International Symposium on Artificial Life and Robotics, Oita, Japan, February 4–6, 2005     

A scaling law between the number of multirobots and their task performance

We researched the efficiency of cooperative behavior using interacting multirobots. In this paper, we assume simple robots with a drive system and the simplest means of interaction, and examine the collective behavior through the task of gathering pucks in a field. The efficiency of group behavior is evaluated by the relation between the number of robots and the task completion time. To evaluate the efficiency of group behavior, we measure the exponent β, which is obtained from the scaling relation between the task completion time and the number of robots. The effectiveness of group behavior is investigated for fractal distributions of pucks. We research their behavior for fractal distributions of pucks and find out that the optimum value of β depends on the dimension of the puck distributions. We also propose a simplified state transition diagram of the group to analyse their characteristics. These results enable us to describe the condition of the field by a variable in the state transition diagram. This work was presented, in part, at the Third International Symposium on Artificial Life and Robotics, Oita, Japan, January 19–21, 1998     

Cellular ants: A method to create collision free trajectories for a cooperative robot team

Creating collision-free trajectories for mobile robots, known as the path planning problem, is considered to be one of the basic problems in robotics. In case of multiple robotic systems, the complexity of such systems increases proportionally with the number of robots, due to the fact that all robots must act as one unit to complete one composite task, such as retaining a specific formation. The proposed path planner employs a combination of Cellular Automata (CA) and Ant Colony Optimization (ACO) techniques in order to create collision-free trajectories for every robot of a team while their formation is kept immutable. The method reacts with obstacle distribution changes and therefore can be used in dynamical or unknown environments, without the need of a priori knowledge of the space. The team is divided into subgroups and all the desired pathways are created with the combined use of a CA path planner and an ACO algorithm. In case of lack of pheromones, paths are created using the CA path planner. Compared to other methods, the proposed method can create accurate collision-free paths in real time with low complexity while the implemented system is completely autonomous. A simulation environment was created to test the effectiveness of the applied CA rules and ACO principles. Moreover, the proposed method was implemented in a system using a real world simulation environment, called Webots. The CA and ACO combined algorithm was applied to a team of multiple simulated robots without the interference of a central control. Simulation and experimental results indicate that accurate collision free paths could be created with low complexity, confirming the robustness of the method.     

Active leave behavior of members in a fault-tolerant group

With the fast development of network applications, there are more asynchronous distributed systems and more requirements for fault tolerance. Asynchrony means there is no upper bound for either message transfer or operation execution. Active replication is an effective means to enhance fault tolerant capability in distributed systems. A key component in a system is replicated and all the replicas make up of a fault-tolerant group. Members in such a group execute all client requests and then re…     

Learning in asynchronous discussion groups: a multilevel approach to study the influence of student, group and task characteristics

The research reported in the current article studies the impact of learning in asynchronous discussion groups on students' final exam scores and levels of knowledge construction. Multilevel analyses were applied to uncover the specific influence of student, group and task variables. The results indicate that the impact of student characteristics on both dependent variables is of higher significance than characteristics of the discussion group students are allocated to. With regard to levels of knowledge construction, task characteristics also appear to be of importance.

 With regard to final exam scores the analyses reveal a significant impact from student learning style, attitude towards task-based learning, the number of student contributions and the level of knowledge construction in these contributions. No significant group characteristics were observed.

 As to levels of knowledge construction, the analyses revealed that the amount of contributions and the attitude towards the online learning environment are significant predictors. The intensity of the interaction in a group had a significant impact. As to task characteristics, significant differences were found between consecutive themes. These disappeared when taking into account task complexity.     

The relative effects of positive interdependence and group processing on student achievement and attitude in online cooperative learning

This study investigated the effects of positive interdependence and group processing on student achievement and attitude in online learning. Students in three university courses received initial instruction about teamwork skills and cooperative learning and were randomly assigned to one of three treatment groups in each course. The “positive interdependence” and the “group processing” groups received subsequent associated skills training. The “no structure” control group received no additional training. Following the treatment, the “positive interdependence” groups had significantly higher achievement than the “group processing” or the “no structure” groups. There was no significant difference among any of the three groups on student attitude.     

A study of evolution strategy based cooperative behavior in collective agents

The following paper introduces an evolution strategy on the basis of cooperative behaviors in each group of agents. The evolution strategy helps each agent to be self-defendable and self-maintainable. To determine an optimal group behavior strategy under dynamically varying circumstances, agents in same group cooperate with each other. This proposed method use reinforcement learning, enhanced neural network, and artificial life. In the present paper, we apply two different reward models: reward model 1 and reward model 2. Each reward model is designed as considering the reinforcement or constraint of behaviors. In competition environments of agents, the behavior considered to be advantageous is reinforced as adding reward values. On the contrary, the behavior considered to be disadvantageous is constrained as subtracting the values. And we propose an enhanced neural network to add learning behavior of an artificial organism-level to artificial life simulation. In future, the system models and results described in this paper will be applied to the framework of healthcare systems that consists of biosensors, healthcare devices, and healthcare system.     

Empirical analysis of the evolution of a taxonomy for best practices

Taxonomies play an increasingly role in knowledge management, providing the basis on which to find and communicate knowledge, information and metrics. However, knowledge continues to evolve over time. As a result, taxonomies also need to continue to evolve.Two different evolved versions of a taxonomy for best practices, each based on the same original taxonomy were analyzed. This research investigated empirical approaches to trace the changes in the original taxonomy. In so doing, an approach using empirical findings to monitoring and anticipating taxonomy change is initiated. There were a number of findings, including a tendency to evolve to greater complexity.     

A control-design-based solution to robotic ecology: Autonomy of achieving cooperative behavior from a high-level astronaut command

In this paper, we propose a cooperative control strategy for a group of robotic vehicles to achieve the specified task issued from a high-level astronaut command. The problem is mathematically formulated as designing the cooperative control for a general class of multiple-input-multiple-output (MIMO) dynamical systems in canonical form with arbitrary but finite relative degrees such that the outputs of the overall system converge to the explicitly given steady state. The proposed cooperative control for individual vehicle only need to use the sensed and communicated outputs information from its local neighboring vehicles. No fixed leader and time-invariant communication networks are assumed among vehicles. Particularly, a set of less-restrictive conditions on the connectivity of the sensor/communication networks are established, under which it is rigorously proven by using the newly found nice properties of the convergence of sequences of row stochastic matrices that the cooperative objective of the overall system can be achieved. Simulation results for a group of vehicles achieving a target and surrounding a specified object in formation are provided to support the proposed approach in this paper. Jing Wang received his B.S. degree and Ph.D. degree in control theory and control engineering, both from Central South University of Technology, China, in 1992 and 1997, respectively. He was a Postdoctoral Research Fellow at the Institute of Computing Technology, Chinese Academy of Sciences, from 1997 to 1999, and at the National University of Singapore, Singapore, from 1999 to 2002. Since March 2002, he has been with School of Electrical and Computer Science of University of Central Florida and now is a research assistant professor. He is the co-recipient of Best Theoretical Paper Award in 2002 at the 4th World Congress on Intelligent Control and Automation, Shanghai, China. His current research interests include cooperative control of multi-robot systems, nonlinear controls, robot control and motion planning, trajectory optimization, and control applications. He is a Member of IEEE and AIAA. Zhihua Qu received his Ph.D. degree in electrical engineering from the Georgia Institute of Technology in 1990. Since then, he has been with the University of Central Florida. Currently, he is a Professor in the Department of Electrical and Computer Engineering. His main research interests are nonlinear systems, robust and adaptive control designs, and robotics. He has published a number of papers in these areas and is the author of two books, Robust Control of Nonlinear Uncertain Systems by Wiley Interscience and Robust Tracking Control of Robotic Manipulators by IEEE Press. He is presently serving as an Associate Editor for Automatica and for International Journal of Robotics and Automation. He is a senior member of IEEE. Curtis M. Ihlefeld has been an electronics engineer for the National Aeronautics and Space Administration at the Kennedy Space Center since 1989. He is currently a member of the Kennedy Space Center Applied Physics Lab and has performed embedded processor systems design and control systems design for numerous Kennedy Space Center laboratories including the NASA Analytical Chemistry Lab, Optical Instrumentation Lab, Transducers Lab, and Data Acquisition Lab. Current projects include a control system design for a Lunar chemistry experiment that searches for water on the moon’s surface, a control system design and image processing tool set for space shuttle engine compartment photography, and a control system and image processing tool set for a space shuttle window defect measurement system. Presently he is performing research in the control of electroactive polymers. He holds an MS degree in electrical engineering from the University of Central Florida, and the title of his thesis was Application of Lyapunov Based Sensor Fault Detection in a Reverse Water Gas Shift Generator. He has one published conference proceedings paper and one journal article in the area of nonlinear fault tolerant control. Richard A. Hull received his B.S. in Engineering Science and Mechanics from the University of Florida, 1972, his M.S. and Ph.D. in Electrical Engineering from the University of Central Florida in 1993 and 1996, respectively. He has served as a Guidance and Control System Engineer in the Aerospace Industry for over 30 years, working for Lockheed Martin, Coleman Aerospace, McDonnell Douglas, and Boeing companies. He is currently a Principal Engineer in the Advanced Concepts Business Unit of Science Applications International Corporation (SAIC). He was a former recipient of the U.S. Air Force Laboratory Graduate Fellowship in Guidance and Control, and formerly served as Vice-Chairman of the Lockheed Martin Corporate Technical Focus Group for Guidance, Navigation and Control. His expertise and experience includes synthesis, simulation and analysis of guidance and control systems for hypersonic interceptor missiles, exo-atmospheric space vehicles, supersonic turbo-jets, space launch vehicle rockets, and high performance fighter aircraft. He is also a principal investigator for research in nonlinear robust control design methods, cooperative control of multiple platforms, and genetic algorithm design methods for aerospace applications. He has authored or co-authored over twenty conference and journal articles in the fields of nonlinear or cooperative control. He is a member of Institute of Electrical and Electronics Engineers (IEEE), a senior member of American Institute of Aeronautics and Astronautics (AIAA) and a member of the IEEE Control System Society. He has served as an Associate Editor of the Conference Editorial Board for the IEEE Control System Society since 1998, and is an adjunct professor and member of the graduate advisory council in Electrical Engineering for the Florida Institute of Technology (FIT).     

Fault tolerant cooperative control for a class of nonlinear multi-agent systems

This paper studies the target aggregation problem for a class of nonlinear multi-agent systems with the time varying interconnection topology. The general neighboring rule-based linear cooperative protocol is developed and a sufficient aggregation condition is derived. Moreover, it is shown that in the presence of agent faults, the target point is still reached by adjusting some weights of the cooperative protocol without changing the structure of the topology. An unmanned aerial vehicle team example illustrates the efficiency of the proposed approach.     

A distributed motion coordination strategy for multiple nonholonomic mobile robots in cooperative hunting operations

This paper presents a distributed smooth time-varying feedback control law for coordinating motions of multiple nonholonomic mobile robots of the Hilare-type to capture/enclose a target by making troop formations. This motion coordination is a cooperative behavior for security against invaders in surveillance areas. Each robot in this control law has its own coordinate system and it senses a target/invader, other robots and obstacles, to achieve this cooperative behavior without making any collision. Each robot especially has a two-dimensional control input referred to as a “formation vector” and the formation is controllable by the vectors. The validity of this control law is supported by computer simulations.     

Finding the nucleolus of any n-person cooperative game by a single linear program

In this paper we show a new method for calculating the nucleolus by solving a unique minimization linear program with O(4ⁿ)$O(4^n)$ constraints whose coefficients belong to {−1,0,1}$\{-1,0,1\}$. We discuss the need of having all these constraints and empirically prove that they can be reduced to O(_kmax2ⁿ)$O(k_{\mathit{max}}{2}^n)$, where k_max is a positive integer comparable with the number of players. A computational experience shows the applicability of our method over (pseudo)random transferable utility cooperative games with up to 18 players.     

The roles of group potency and information and communication technologies in the relationship between task conflict and team effectiveness: A longitudinal study

The effects of task conflict on group effectiveness are far from conclusive. In order to further clarify this relationship, a contingency approach has been suggested. In this context, the present study examines the roles of group potency and information and communication technologies (ICTs) in the relationship between task conflict and team effectiveness. The study involved 44 groups of 4 members each, working in two communication media. Twenty-two groups worked in a face-to-face condition, and the other 22 groups worked in a computer-mediated communication condition. The groups developed a project during four weekly sessions over a one-month period. The results showed that group potency moderated the relationship between task conflict and group effectiveness. However, the communication medium did not play a moderator role in these relationships.     

The influence of solitary and cooperative violent video game play on aggressive and prosocial behavior

Research examining the issue of video game violence influences on aggression continues to be debated within the scientific community. Thus far, no consensus has been reached regarding the influence of such games. This study adds to the prior literature by examining how violent video games may promote prosocial or aggressive behavior when played either cooperatively or alone. Results indicated that violent content in video games had no influence on prosocial behavior, aggressive behavior, or self-perceptions of empathy. Playing cooperatively was associated with less aggressive behavior, whether games were violent or not.     

Coordination of cooperative advertising models in a one-manufacturer two-retailer supply chain system

This paper considers cooperative advertising issues of a monopolistic manufacturer with competing duopolistic retailers. Four possible game structures (or power configurations), i.e., Stackelberg–Cournot, Stackelberg–Collusion, Nash–Cournot and Nash–Collusion, are discussed. Under each of four game structures, we develop a decision model for the three partners to design the optimal cooperative advertising policies. Through a comparison among the four models, we reveal how cooperative advertising policies and profits of all participants are affected by various competitive behaviors, and then determine whether the partners have any incentives to transit to a different structure. Also presented in the paper are a centralized decision model and a proposed cost-sharing contract, which is able to achieve perfect coordination of the considered channel, where the utility of risk preference is used to determine the fraction of local advertising costs shared by the manufacturer.     

Finding a robust configuration for the AEDB information dissemination protocol for mobile ad hoc networks

The Adaptive Enhanced Distance Based Broadcasting Protocol, AEDB hereinafter, is an advanced adaptive protocol for information dissemination in mobile ad hoc networks (MANETs). It is based on the Distance Based broadcasting protocol, and it acts differently according to local information to minimize the energy and network use, while maximizing the coverage of the broadcasting process. As most of the existing communication protocols, AEDB relies on different thresholds for adapting its behavior to the environment. We propose in this work to look for configurations that induce a stable performance of the protocol in different networks by automatically fine tuning these thresholds thanks to the use of cooperative coevolutionary multi-objective evolutionary algorithms. Finding robust solutions for this problem is important because MANETs have a highly unpredictable and dynamic topology, features that have a strong influence on the performance of the protocol. Consequently, robust solutions that show a good performance under any circumstances are required. In this work, we define different fitness functions that measure robustness of solutions for better guiding the algorithm towards more robust solutions. They are: median, constrained, worst coverage, and worst hypervolume. Results show, that the two worst-case approaches perform better, not only in case of robustness but also in terms of accuracy of the reported AEDB configurations on a large set of networks.     

Relay selection for maximizing the number of successive transmission in cooperative networks

Relay selection has an important effect on improving the performance of cooperative communication system. However, the frequent operation of relay selection can bring enormous control message overhead, and then decrease the performance of cooperative communication. To reduce the relay selection frequency, in this paper, we propose a relay selection scheme to choose the best relay considering successive packets transmission. In this scheme, through the parameters of data transmission rate, the length of data packet and the estimated channel state information, the best relay is selected to maximize the number of successive packets transmission while guaranteeing the given outage probability. It is proven by numerical results that the proposed relay selection scheme can support the successive packets transmission in cooperative wireless networks and the maximum number of successive packets transmission is affected by the different network parameters, i.e. packet length, data transmission rate, signal to noise ratio and Doppler frequency. Compared to previous relay selection schemes by simulation results, it is shown that the proposed relay selection scheme can improve the throughput performance efficiently.