Exploring the effects of trust,task interdependence and virtualness on knowledge sharing in teams

The sharing of knowledge within teams is critical to team functioning. However, working with team members who are in different locations (i.e. in virtual teams) may introduce communication challenges and reduce opportunities for rich interactions, potentially affecting knowledge sharing and its outcomes. Therefore, using questionnaire‐based data, this study examined the potential effects of different aspects of virtuality on a knowledge‐sharing model. Social exchange theory was used to develop a model relating trust to knowledge sharing and knowledge sharing to team effectiveness. The moderating effects of virtuality and task interdependence on these relationships were examined. A strong positive relationship was found between trust and knowledge sharing for all types of teams (local, hybrid and distributed), but the relationship was stronger when task interdependence was low, supporting the position that trust is more critical in weak structural situations. Knowledge sharing was positively associated with team effectiveness outcomes; however, this relationship was moderated by team imbalance and hybrid structures, such that the relationship between sharing and effectiveness was weaker. Organizations should therefore avoid creating unbalanced or hybrid virtual teams.     

Collaborative learning in mobile work

Abstract  Moving towards more communication intensive organisations, where work tends to be mobile, understanding how to support learning in such work becomes increasingly important. This paper reports on a study of a customer relations team, where work is performed co-located, distributed as well as mobile. Collaborative learning within in this team is explored so as to inform the design of IT support. In the results four instances of collaborative learning important in the studied team were identified: walking into collaborative learning, travelling to meetings, articulating practice and sharing without articulating. These issues are discussed and how they affect the design of collaborative learning activities for mobile knowledge workers.     

面向机器学习的分布式并行计算关键技术及应用

当前机器学习等算法的计算、迭代过程日趋复杂, 充足的算力是保障人工智能应用落地效果的关键。本文首先提出一种适应倾斜数据的分布式异构环境下的任务时空调度算法,有效提升机器学习模型训练等任务的平均效率;其次,提出分布式异构环境下高效的资源管理系统与节能调度算法,实现分布式异构环境下基于动态预测的跨域计算资源迁移及电压/频率的动态调节,节省了系统的整体能耗;然后构建了适应于机器学习/深度学习算法迭代的分布式异构优化环境,提出了面向机器学习/图迭代算法的分布式并行优化基本方法。最后,本文研发了面向领域应用的智能分析系统,并在制造、交通、教育、医疗等领域推广应用,解决了在高效数据采集、存储、清洗、融合与智能分析等过程中普遍存在的性能瓶颈问题。     

Multi-goal Q-learning of cooperative teams

This paper studies a multi-goal Q-learning algorithm of cooperative teams. Member of the cooperative teams is simulated by an agent. In the virtual cooperative team, agents adapt its knowledge according to cooperative principles. The multi-goal Q-learning algorithm is approached to the multiple learning goals. In the virtual team, agents learn what knowledge to adopt and how much to learn (choosing learning radius). The learning radius is interpreted in Section 3.1. Five basic experiments are manipulated proving the validity of the multi-goal Q-learning algorithm. It is found that the learning algorithm causes agents to converge to optimal actions, based on agents’ continually updated cognitive maps of how actions influence learning goals. It is also proved that the learning algorithm is beneficial to the multiple goals. Furthermore, the paper analyzes how sensitive the learning performance is affected by the parameter values of the learning algorithm.     

异质多移动机器人协同技术研究的进展

随着移动机器人应用的领域和范围的不断扩展，多移动机器人由于其单个机器人无法比拟的优越性已经越来越受到重视．从体系结构、协作与协调、协作环境感知与定位、重构及机器学习几个重要课题对多移动机器人协同技术进行了综述，尤其侧重于各种技术如何处理和包容团队中的异质性，并分析了本领域中的研究难点问题，最后展望了异质多移动机器人研究的前景与发展趋势．     

Double-fold localized multiple matrix learning machine with Universum

Matrix learning, multiple-view learning, Universum learning, and local learning are four hot spots of present research. Matrix learning aims to design feasible machines to process matrix patterns directly. Multiple-view learning takes pattern information from multiple aspects, i.e., multiple-view information into account. Universum learning can reflect priori knowledge about application domain and improve classification performances. A good local learning approach is important to the finding of local structures and pattern information. Our previous proposed learning machine, double-fold localized multiple matrix learning machine is a one with multiple-view information, local structures, and matrix learning. But this machine does not take Universum learning into account. Thus, this paper proposes a double-fold localized multiple matrix learning machine with Universum (Uni-DLMMLM) so as to improve the performance of a learning machine. Experimental results have validated that Uni-DLMMLM (1) makes full use of the domain knowledge of whole data distribution as well as inherits the advantages of matrix learning; (2) combines Universum learning with matrix learning so as to capture more global knowledge; (3) has a good ability to process different kinds of data sets; (4) has a superior classification performance and leads to a low empirical generation risk bound.     

Learning to Share Meaning in a Multi-Agent System

The development of the semantic Web will require agents to use common domain ontologies to facilitate communication of conceptual knowledge. However, the proliferation of domain ontologies may also result in conflicts between the meanings assigned to the various terms. That is, agents with diverse ontologies may use different terms to refer to the same meaning or the same term to refer to different meanings. Agents will need a method for learning and translating similar semantic concepts between diverse ontologies. Only until recently have researchers diverged from the last decade's common ontology paradigm to a paradigm involving agents that can share knowledge using diverse ontologies. This paper describes how we address this agent knowledge sharing problem of how agents deal with diverse ontologies by introducing a methodology and algorithms for multi-agent knowledge sharing and learning in a peer-to-peer setting. We demonstrate how this approach will enable multi-agent systems to assist groups of people in locating, translating, and sharing knowledge using our Distributed Ontology Gathering Group Integration Environment (DOGGIE) and describe our proof-of-concept experiments. DOGGIE synthesizes agent communication, machine learning, and reasoning for information sharing in the Web domain.     

Cooperative Multi-Agent Learning: The State of the Art

Cooperative multi-agent systems (MAS) are ones in which several agents attempt, through their interaction, to jointly solve tasks or to maximize utility. Due to the interactions among the agents, multi-agent problem complexity can rise rapidly with the number of agents or their behavioral sophistication. The challenge this presents to the task of programming solutions to MAS problems has spawned increasing interest in machine learning techniques to automate the search and optimization process. We provide a broad survey of the cooperative multi-agent learning literature. Previous surveys of this area have largely focused on issues common to specific subareas (for example, reinforcement learning, RL or robotics). In this survey we attempt to draw from multi-agent learning work in a spectrum of areas, including RL, evolutionary computation, game theory, complex systems, agent modeling, and robotics. We find that this broad view leads to a division of the work into two categories, each with its own special issues: applying a single learner to discover joint solutions to multi-agent problems (team learning), or using multiple simultaneous learners, often one per agent (concurrent learning). Additionally, we discuss direct and indirect communication in connection with learning, plus open issues in task decomposition, scalability, and adaptive dynamics. We conclude with a presentation of multi-agent learning problem domains, and a list of multi-agent learning resources.     

Automated Assistants for Analyzing Team Behaviors

Multi-agent teamwork is critical in a large number of agent applications, including training, education, virtual enterprises and collective robotics. The complex interactions of agents in a team as well as with other agents make it extremely difficult for human developers to understand and analyze agent-team behavior. It has thus become increasingly important to develop tools that can help humans analyze, evaluate, and understand team behaviors. However, the problem of automated team analysis is largely unaddressed in previous work. In this article, we identify several key constraints faced by team analysts. Most fundamentally, multiple types of models of team behavior are necessary to analyze different granularities of team events, including agent actions, interactions, and global performance. In addition, effective ways of presenting the analysis to humans is critical and the presentation techniques depend on the model being presented. Finally, analysis should be independent of underlying team architecture and implementation.We also demonstrate an approach to addressing these constraints by building an automated team analyst called ISAAC for post-hoc, off-line agent-team analysis. ISAAC acquires multiple, heterogeneous team models via machine learning over teams' external behavior traces, where the specific learning techniques are tailored to the particular model learned. Additionally, ISAAC employs multiple presentation techniques that can aid human understanding of the analyses. ISAAC also provides feedback on team improvement in two novel ways: (i) It supports principled what-if reasoning about possible agent improvements; (ii) It allows the user to compare different teams based on their patterns of interactions. This paper presents ISAAC's general conceptual framework, motivating its design, as well as its concrete application in two domains: (i) RoboCup Soccer; (ii) software agent teams participating in a simulated evacuation scenario. In the RoboCup domain, ISAAC was used prior to and during the RoboCup '99 tournament, and was awarded the RoboCup Scientific Challenge Award. In the evacuation domain, ISAAC was used to analyze patterns of message exchanges among software agents, illustrating the generality of ISAAC's techniques. We present detailed algorithms and experimental results from ISAAC's application.     

Trust and knowledge sharing in diverse global virtual teams

Global virtual teams (GVTs) allow organizations to improve productivity, procure global knowledge, and transfer best practice information instantaneously among team members. GVTs rely heavily on IT and have little face-to-face interaction, thereby increasing problems resulting from geographic barriers, time language, and cultural differences, and inter-personal relationships. The purpose of our study was to design a normative framework that would assist organizations in understanding the relationship between diversity, mutual trust, and knowledge sharing among GVTs, with additional focus on understanding the moderating impact of collaborative technology and task characteristics. Empirical data was collected from 58 GVTs and analyzed using a Hierarchical Multiple Regression technique. Results showed that in GVTs, deep level diversity has a more significant relationship with team processes of mutual trust and knowledge sharing than visible functional level diversity. This relationship is moderated by the collaborative capabilities of available technology and levels of interdependence of the task. Furthermore, knowledge sharing and mutual trust mediate the relationship between diversity levels and team effectiveness.     

Group online adaptive learning

Sharing information among multiple learning agents can accelerate learning. It could be particularly useful if learners operate in continuously changing environments, because a learner could benefit from previous experience of another learner to adapt to their new environment. Such group-adaptive learning has numerous applications, from predicting financial time-series, through content recommendation systems, to visual understanding for adaptive autonomous agents. Here we address the problem in the context of online adaptive learning. We formally define the learning settings of Group Online Adaptive Learning and derive an algorithm named Shared Online Adaptive Learning (SOAL) to address it. SOAL avoids explicitly modeling changes or their dynamics, and instead shares information continuously. The key idea is that learners share a common small pool of experts, which they can use in a weighted adaptive way. We define group adaptive regret and prove that SOAL maintains known bounds on the adaptive regret obtained for single adaptive learners. Furthermore, it quickly adapts when learning tasks are related to each other. We demonstrate the benefits of the approach for two domains: vision and text. First, in the visual domain, we study a visual navigation task where a robot learns to navigate based on outdoor video scenes. We show how navigation can improve when knowledge from other robots in related scenes is available. Second, in the text domain, we create a new dataset for the task of assigning submitted papers to relevant editors. This is, inherently, an adaptive learning task due to the dynamic nature of research fields evolving in time. We show how learning to assign editors improves when knowledge from other editors is available. Together, these results demonstrate the benefits for sharing information across learners in concurrently changing environments.

     

LDA merging and splitting with applications to multiagent cooperative learning and system alteration

To adapt linear discriminant analysis (LDA) to real-world applications, there is a pressing need to equip it with an incremental learning ability to integrate knowledge presented by one-pass data streams, a functionality to join multiple LDA models to make the knowledge sharing between independent learning agents more efficient, and a forgetting functionality to avoid reconstruction of the overall discriminant eigenspace caused by some irregular changes. To this end, we introduce two adaptive LDA learning methods: LDA merging and LDA splitting. These provide the benefits of ability of online learning with one-pass data streams, retained class separability identical to the batch learning method, high efficiency for knowledge sharing due to condensed knowledge representation by the eigenspace model, and more preferable time and storage costs than traditional approaches under common application conditions. These properties are validated by experiments on a benchmark face image data set. By a case study on the application of the proposed method to multiagent cooperative learning and system alternation of a face recognition system, we further clarified the adaptability of the proposed methods to complex dynamic learning tasks.     

知识网格环境下基于TMs的协同认知

协同认知,形式上表现为问题解决环境下拥有各自知识的实体,根据一定的学习规则在个体认知的基础上,协同完成某项认知任务。协同认知的参与者可能处于不同地点,因而分布式环境下的知识共享显得尤为重要。本文通过对网格环境下Topic Maps（TMs）这一知识表示手段的应用扩展,对网格环境下的知识表示和知识传播进行了研究,提出了有别于传统网络查询的查询机制。在对协同认知的元学习及群学习进行分析与定义的基础上,将机器处理和心智学习有机结合,构造了一个面向知识网格环境的协同认知学习机制,并相应地给出了一个基于TMs的认知实例。     

Working Together Apart? Building a Knowledge‐Sharing Culture for Global Virtual Teams

A new impetus for greater knowledge‐sharing among team members needs to be emphasized due to the emergence of a significant new form of working known as ‘global virtual teams’. As information and communication technologies permeate every aspect of organizational life and impact the way teams communicate, work and structure relationships, global virtual teams require innovative communication and learning capabilities for different team members to effectively work together across cultural, organizational and geographical boundaries. Whereas information technology‐facilitated communication processes rely on technologically advanced systems to succeed, the ability to create a knowledge‐sharing culture within a global virtual team rests on the existence (and maintenance) of intra‐team respect, mutual trust, reciprocity and positive individual and group relationships. Thus, some of the inherent questions we address in our paper are: (1) what are the cross‐cultural challenges faced by global virtual teams?; (2) how do organizations develop a knowledge sharing culture to promote effective organizational learning among culturally‐diverse team members? and; (3) what are some of the practices that can help maximize the performance of global virtual teams? We conclude by examining ways that global virtual teams can be more effectively managed in order to reach their potential in this new interconnected world and put forward suggestions for further research.     

Exploiting Spatial Locality and Heterogeneity of Agents for Search and Rescue Teamwork*

We propose coordination mechanisms for multiple heterogeneous physical agents that operate in city‐scale disaster scenarios, where they need to find and rescue people and extinguish fires. Large‐scale disasters are characterized by limited and unreliable communications; dangerous events that may disable agents; uncertainty about the location, duration, and type of tasks; and stringent temporal constraints on task completion times. In our approach, agents form teams with other agents that are in the same geographical area. Our algorithms either yield stable teams formed up front and never change, fluid teams where agents can change teams as need arises, or teams that restrict the types of agents that can belong to the same team. We compare our teaming algorithms against a baseline algorithm in which agents operate independently of others and two state‐of‐the‐art coordination mechanisms. Our algorithms are tested in city‐scale disaster simulations using the RoboCup Rescue simulator. Our experiments with different city maps show that, in general, forming teams leads to increased task completion and, specifically, that our teaming method that restricts the types of agents in a team outperforms the other methods.     

Adaptive task assignment for multiple mobile robots via swarm intelligence approach

This paper describes an adaptive task assignment method for a team of fully distributed mobile robots with initially identical functionalities in unknown task environments. A hierarchical assignment architecture is established for each individual robot. In the higher hierarchy, we employ a simple self-reinforcement learning model inspired by the behavior of social insects to differentiate the initially identical robots into “specialists” of different task types, resulting in stable and flexible division of labor; on the other hand, in dealing with the cooperation problem of the robots engaged in the same type of task, Ant System algorithm is adopted to organize low-level task assignment. To avoid using a centralized component, a “local blackboard” communication mechanism is utilized for knowledge sharing. The proposed method allows the robot team members to adapt themselves to the unknown dynamic environments, respond flexibly to the environmental perturbations and robustly to the modifications in the team arising from mechanical failure. The effectiveness of the presented method is validated in two different task domains: a cooperative concurrent foraging task and a cooperative collection task.     

Effects of task complexity on individual creativity through knowledge interaction: A comparison of temporary and permanent teams

Using the interactionist’s perspective of creativity, this paper proposes a new research model of creativity manifestation to explore how factors affecting individual creativity depend on team characteristics. We investigated the antecedents of creativity in the literature—task complexity, team member exchange, and knowledge sharing—and then examined the relationships and differences between temporary and permanent teams. To maximize practical implications, we studied two team types like project task force (PTF) and research and development (R&D) teams in the Information and Communication Technology (ICT) industry in Korea, where strong creativity is required for team performance. PTF teams operate with a clear mission to be completed on a deadline, while R&D teams create scientific enhancements for existing products. The proposed structural model was tested empirically with cross-sectional data from 289 professionals from the two team types. Results indicated that, in the case of PTF teams, task complexity had an indirect relationship with individual complexity through knowledge interaction among team members, while for R&D teams, task complexity was directly associated with individual creativity, and indirectly associated with the creativity through team member exchange. Thus, team characteristics must be considered together with task complexity and knowledge interactions in order to achieve team goals more effectively by maximizing each member’s creativity.     

Distributed Learning for Planning Under Uncertainty Problems with Heterogeneous Teams

This paper considers the problem of multiagent sequential decision making under uncertainty and incomplete knowledge of the state transition model. A distributed learning framework, where each agent learns an individual model and shares the results with the team, is proposed. The challenges associated with this approach include choosing the model representation for each agent and how to effectively share these representations under limited communication. A decentralized extension of the model learning scheme based on the Incremental Feature Dependency Discovery (Dec-iFDD) is presented to address the distributed learning problem. The representation selection problem is solved by leveraging iFDD’s property of adjusting the model complexity based on the observed data. The model sharing problem is addressed by having each agent rank the features of their representation based on the model reduction error and broadcast the most relevant features to their teammates. The algorithm is tested on the multi-agent block building and the persistent search and track missions. The results show that the proposed distributed learning scheme is particularly useful in heterogeneous learning setting, where each agent learns significantly different models. We show through large-scale planning under uncertainty simulations and flight experiments with state-dependent actuator and fuel-burn- rate uncertainty that our planning approach can outperform planners that do not account for heterogeneity between agents.     

PetroKG: Construction and Application of Knowledge Graph in Upstream Area of PetroChina

There is a large amount of heterogeneous data distributed in various sources in the upstream of PetroChina. These data can be valuable assets if we can fully use them. Meanwhile, the knowledge graph, as a new emerging technique, provides a way to integrate multi-source heterogeneous data. In this paper, we present one application of the knowledge graph in the upstream of PetroChina. Specifically, we first construct a knowledge graph from both structured and unstructured data with multiple NLP (natural language progressing) methods. Then, we introduce two typical knowledge graph powered applications and show the benefit that the knowledge graph brings to these applications:compared with the traditional machine learning approach, the well log interpretation method powered by knowledge graph shows more than 7.69% improvement of accuracy.