群智能在多智能体系统中的应用研究进展

群智能算法是受群居性昆虫群体的集体行为启发而设计的分布式问题求解方法,将它应用到多智能体系统,旨在提高系统的鲁棒性、灵活性和自适应性。以群智能在多智能体系统中的应用为线索,首先介绍群智能的核心机制,然后从多智能体系统通信机制、协作技术、学习问题及体系结构建立这几个方面总结群智能理论在多智能体系统中的已有工作。最后分析和讨论了群智能方法在多智能体系统应用中存在的问题,并提出今后的工作展望。     

Limited engagement of SMEs with social media: A structuration and sensemaking perspective

Despite the worldwide rapidly growing social media use, previous surveys have shown an adoption rate below average among SMEs (small and medium-sized enterprises). The problem of why and how SMEs engage with social media has received scant attention. Informed by structuration and sensemaking theories, this qualitative inquiry attempts to close this gap by scrutinizing cognitive and collective pre-adoption aspects. Differently perceived technology features, the structural context of the ecosystem, and the triggering conditions as well as their role for individual and collective understanding and decision-making activities form the building blocks of a processual framework and the derivation of further research avenues.     

一种基于个体评估能力的Stigmergic群体搭建机制

群体搭建任务研究一群机器人如何以分散控制的方式并行搭建出具有几何形状的砖块结构.相对于群机器人学中的其他任务,群体搭建研究特别注重搭建过程的时空协调.作为一种以环境为媒介进行间接交互的时空协调机制,Stigmergy被认为更具一般性与通用性.传统方法通过同时感知多邻域信息或利用结构对称性来制定复杂规则,但其并不十分适用于非模块结构以及序列结构.本文针对此类结构提出了基于个体评估能力的Stigmergic群体搭建时空协调机制,该协调机制可通过赋予个体感知环境、评估系统当前状态的能力来简化对搭建规则的依赖,即个体利用简单搭建规则即可完成群体搭建任务,实现群体搭建过程时空的协调.仿真实验结果表明提出的机制是可行且有效的.     

Collective Intentions and the Maintenance of Social Practices

This paper studies social practices and the dynamics of their maintenance in precise mathematical and logical terms. Roughly, social practices (such shaking hands, going regularly to sauna, eating a certain kind of food at Easter, etc.) are recurrent collective activities based on collective attitudes (shared we-attitudes), and the central kind of social practice under study here, is one based on collective intention. Social practices are the building blocks of human societies, and also of artificial ones. In this paper, we present a mathematical model representing the dynamics of social practices, and some properties of the process are subjected to detailed mathematical study.     

RoboCup Rescue as multiagent task allocation among teams: experiments with task interdependencies

This paper addresses distributed task allocation among teams of agents in a RoboCup Rescue scenario. We are primarily concerned with testing different mechanisms that formalize issues underlying implicit coordination among teams of agents. These mechanisms are developed, implemented, and evaluated using two algorithms: Swarm-GAP and LA-DCOP. The latter bases task allocation on a comparison between an agent’s capability to perform a task and the capability demanded by this task. Swarm-GAP is a probabilistic approach in which an agent selects a task using a model inspired by task allocation among social insects. Both algorithms were also compared to another one that allocates tasks in a greedy way. Departing from previous works that tackle task allocation in the rescue scenario only among fire brigades, here we consider the various actors in the RoboCup Rescue, a step forward in the direction of realizing the concept of extreme teams. Tasks are allocated to teams of agents without explicit negotiation and using only local information. Our results show that the performance of Swarm-GAP and LA-DCOP are similar and that they outperform a greedy strategy. Also, it is possible to see that using more sophisticated mechanisms for task selection does pay off in terms of score.     

Learning model for adaptive behaviors as an organized group of swarm robots

This paper describes a novel organizational learning model for multiple adaptive robots. In this model, robots acquire their own appropriate functions through local interactions among their neighbors, and get out of deadlock situations without explicit control mechanisms or communication methods. Robots also complete given tasks by forming an organizational structure, and improve their organizational performance. We focus on the emergent processes of collective behaviors in multiple robots, and discuss how to control these behaviors with only local evaluation functions, rather than with a centralized control system. Intensive simulations of truss construction by multiple robots gave the following experimental results: (1) robots in our model acquire their own appropriate functions and get out of deadlock situations without explicit control mechanisms or communication methods; (2) robots form an organizational structure which completes given tasks in fewer steps than are needed with a centralized control mechanism. This work was presented, in part, at the Second International Symposium on Artificial Life and Robotics, Oita, Japan, February 18–20, 1997     

Robophysical study of excavation in confined environments

Soil-dwelling social insects build complex nests. Nest excavation is performed by multiple animals simultaneously and is governed by local interactions of the workers with other nest-mates and their surroundings. To investigate collective confined excavation challenges, we built groups of robotic excavators capable of performing hours of autonomous tunnel excavation in a model cohesive granular medium. Excavator behavior was governed by a simple set of rules triggered by interactions with the surrounding environment and other robots. The rate of tunnel growth and energetic costs of excavation were measured for groups of different numbers operating in wide and narrow tunnels. To extend the results to systems with large numbers of robots, we developed a cellular automata model. Experiments and simulations showed that in sufficiently wide tunnels an increase in the size of the excavating group increased the excavation rates without a significant increase in the energy consumption per robot. A decrease in the tunnel width resulted in a decrease in the excavation rates and increase in the energetic costs of excavation. We attribute this effect to the emergence of multiple time-consuming interactions (clogs) among excavating robots in the confined spaces. Although in all situations clogs were resolvable, clog resolution took longer in the systems with larger number of robots and narrower tunnels. We expect that our robotic system can be used to investigate the behavior of social insects in confined spaces as well as inspire more sophisticated search-and-rescue robotics.     

Social presence,trust, and social commerce purchase intention: An empirical research

Lacking the presence of human and social elements is claimed one major weakness that is hindering the growth of e-commerce. The emergence of social commerce might help ameliorate this situation. Social commerce is a new evolution of e-commerce that combines the commercial and social activities by deploying social technologies into e-commerce sites. Social commerce reintroduces the social aspect of shopping to e-commerce, increasing the degree of social presences in online environment. Drawing upon the social presence theory, this study theorizes the nature of social aspect in online SC marketplace by proposing a set of three social presence variables. These variables are then hypothesized to have positive impacts on trusting beliefs which in turn result in online purchase behaviors. The research model is examined via data collected from a typical e-commerce site in China. Our findings suggest that social presence factors grounded in social technologies contribute significantly to the building of the trustworthy online exchanging relationships. In doing so, this paper confirms the positive role of social aspect in shaping online purchase behaviors, providing a theoretical evidence for the fusion of social and commercial activities. Finally, this paper introduces a new perspective of e-commerce and calls more attention to this new phenomenon of social commerce.     

RepCIDN: A Reputation-based Collaborative Intrusion Detection Network to Lessen the Impact of Malicious Alarms

Distributed and coordinated attacks in computer networks are causing considerable economic losses worldwide in recent years. This is mainly due to the transition of attackers’ operational patterns towards a more sophisticated and more global behavior. This fact is leading current intrusion detection systems to be more likely to generate false alarms. In this context, this paper describes the design of a collaborative intrusion detection network (CIDN) that is capable of building and sharing collective knowledge about isolated alarms in order to efficiently and accurately detect distributed attacks. It has been also strengthened with a reputation mechanism aimed to improve the detection coverage by dropping false or bogus alarms that arise from malicious or misbehaving nodes. This model will enable a CIDN to detect malicious behaviors according to the trustworthiness of the alarm issuers, calculated from previous interactions with the system. Experimental results will finally demonstrate how entities are gradually isolated as their behavior worsens throughout the time.     

The Impact of Cross-Addiction on Information Sharing Behaviors on Social Networking Sites

ABSTRACT

There is a rise in the interest among researchers to understand how addiction to social networking sites (SNSs) influences the use of SNS for information sharing. Much less attention has been placed on the interactive effect of multiple addictions (i.e., cross-addiction) on information sharing behaviors on SNSs. In this study, we examine the interaction effect of SNS addiction and alcohol use on two characteristic sharing behaviors on SNSs, self-promotion, and peer promotion of alcohol use. This study contributes to theory and practice as it builds a model that integrates social cognitive theory and normative social influence to explain mechanisms through which cross-addiction shapes information sharing behaviors on SNSs. The proposed model is estimated using data from a sample of college students in a North American university. The results support the theorized relationships between cross-addiction and SNS use to promote addiction-related activities. Implications of the findings for theory and practice are discussed.     

Teamwork in Self-Organized Robot Colonies

Swarm robotics draws inspiration from decentralized self-organizing biological systems in general and from the collective behavior of social insects in particular. In social insect colonies, many tasks are performed by higher order group or team entities, whose task-solving capacities transcend those of the individual participants. In this paper, we investigate the emergence of such higher order entities. We report on an experimental study in which a team of physical robots performs a foraging task. The robots are “identical” in hardware and control. They make little use of memory and take actions purely on the basis of local information.      

网络集群行为初探

本文通过分析网络集群行为的特征、原因以及与其相关的法律规定,以求对其有初步认识,并对构建网络集群行为的社会控制机制提出自己的看法。     

A planning system for robot construction tasks

This paper describes BUILD, a computer program which generates plans for building specified structures out of simple objects such as toy blocks. A powerful heuristic control structure enables BUILD to use a number of sophisticated construction techniques in its plans. Among these are the incorporation of pre-existing structure into the final design, pre-assembly of movable sub-structures on the table, and the use of extra blocks as temporary supports and counterweights in the course of the construction.BUILD does its planning in a modeled 3-space in which blocks of various shapes and sizes can be represented in any orientation and location. The modeling system can maintain several world models at once, and contains modules for displaying states, testing them for inter-object contact and collision, and for checking the stability of complex structures involving frictional forces.Suggestions are included for the extension of BUILD-like systems to other domains. Also discussed are the merits of BUILD's implementation language, conniver, for this type of problem solving.     

State-space support for path-based reward variables

Many sophisticated formalisms exist for specifying complex system behaviors, but methods for specifying performance and dependability variables have remained quite primitive. To cope with this problem, modelers often must augment system models with extra state information and event types to support particular variables. This often leads to models that are non-intuitive, and must be changed to support different variables. To address this problem, we extend the array of performance measures that may be derived from a given system model, by developing new performance measure specification and model construction techniques. Specifically, we introduce a class of path-based reward variables, and show how various performance measures may be specified using these variables. Path-based reward variables extend the previous work with reward structures to allow rewards to be accumulated based on sequences of states and transitions. To maintain the relevant history, we introduce the concept of a path automaton, whose state transitions are based on the system model state and transitions. Furthermore, we present a new procedure for constructing state spaces and the associated transition rate matrices that support path-based reward variables. Our new procedure takes advantage of the path automaton to allow a single system model to be used as the basis of multiple performance measures that would otherwise require separate models or a single more complicated model.     

Applying social bookmarking to collective information searching (CIS): An analysis of behavioral pattern and peer interaction for co-exploring quality online resources

With the advancement of Web 2.0 applications, this study aims to advocate that social bookmarking (SB) applications could support mutual exchange of finding information in a manner of collective information searching (CIS). A social bookmarking system, namely ‘WeShare,’ was developed, and conducted with 127 junior high school students for performing the given assignment in this study. The participants’ activities of collecting and reviewing relevant information were traced by log data for later analysis. To initially unveil the participants’ behaviors in the use of social bookmarking for co-exploring the Internet resources, this study proposed some quantitative indicators to represent students’ personal contributions (‘Bookmarks from the Internet,’ ‘Bookmarks from WeShare,’ ‘Annotations on personal bookmarks,’ ‘Comments on others’ bookmarks’) and peer feedback (‘The number of bookmarks collected by peers,’ ‘The number of bookmarks commented on by peers,’ ‘The number of comments from peers’). By the method of cluster analysis, some behavioral patterns regarding how participants collectively search the Internet by use of WeShare were identified. Furthermore, the findings suggest that personal contributions to citing and commenting on peers’ bookmarks are important to the advancement of collective information searching activities for finding quality information on the Internet.     

A cooperative control model for multiagent-based material handling systems

This paper presents an Artificial Immune System (AIS)-based model for cooperative control of multiagent systems. This cooperative control model describes collective behaviors of autonomous agents, known as the AIS agents that are exemplified by the regulated activities performed by individual agents under the computation paradigm of Artificial Immune System. The regulations and emergence of agent behaviors are derived from the immune threshold measures that determine those activities performed by the AIS agents at an individual level. These threshold measures together with the collective behavioral model defined the cooperative control of the AIS-based control framework under which AIS agents behave and act strategically according to the changing environment. The cooperative control model is presented under the three domains, namely exploration, achievement and cooperation domains where AIS agents operate. In this research, we implemented the proposed cooperative control model with a case study of automated material handling with a group of AIS agents that cooperate to achieve the defined tasks.     

Understanding and Reconstruction of the Mobiligence of Insects Employing Multiscale Biological Approaches and Robotics

Adaptability, i.e., the ability to behave in accordance with a ceaselessly changing environment, is a defining feature of animals, including insects, and is a necessary attribute in robotics. Insects display a range of sophisticated behaviors in response to their environments based on the processing of a simple nervous system. Insects are uniquely suited for multidisciplinary studies of the brain involving a combined approach at several levels, from molecules and single neurons to neural networks and behavior. Furthermore, insects can be adapted for use with a wide variety of methodological approaches, from molecular genetics, electrophysiology and imaging to computational tools and robotics. Thus, insects are an excellent model taxon for understanding adaptive control in biological systems. In this review, the general features of the insect brain and multiscale approaches for understanding the neural basis of their behavior are introduced. As an example of adaptive behavior in insects, odor–source orientation behavior in silkmoths and the feasibility of a behavioral strategy based on their neural system, with implementation in robots, is described. Finally, we present novel approaches using an insect–machine hybrid, which will enhance our ability to evaluate and understand adaptive behavior.