Agent-based management of support systems for distributed brainstorming

In this paper, we describe the design and the management of an agent-based system that supports distributed brainstorming activities. The support system is a highly coordinated IoT application composed of many locally installed interface devices, multimedia communication functions, and cloud functions that process application logic and store meeting data. The system is designed to support a variety of brainstorming sessions, so its functionalities must be modifiable and enable the system to be adapted to different environments and user requirements without any loss of performance. System accessibility should be also ensured from any location for any user. These constraints require a flexible and usable support system.We further discuss the aspects of flexibility and usability that are important in a support system for distributed brainstorming, from which we propose a conceptual schema for flexible and usable support systems. To realize this schema, we present a resource-oriented architecture that can modify the brainstorming support system’s structure and functions. Flexibility is achieved thanks to an agent-based system that manages resources and operates on them according to users’ requests.We also describe the system architecture, which is organized around a set of channels dedicated to different services proposed to the users. We present in detail a video channel that ensures user awareness during synchronized activities. We then conduct several experiments verifying the usability of important channels in the architecture and present the results of these experiments.Finally, we discuss experimental scenarios that show how the system owes its adaptability to management based on an agent organization that supports distributed brainstorming and other activities.     

Real-time administration of tool sharing and best matching to enhance assembly lines balanceability and flexibility

Collaboration has been found in previous studies on the design of assembly lines to be a useful mechanism. In this study, the focus is on a collaborative assembly (CA) framework, inspired by the design principles of CCT, the Collaborative Control Theory, to improve balanceability and flexibility of assembly lines through tool sharing (TS) among idle and bottleneck workstations. TS is widely practiced in advanced assembly facilities to reduce cost and improve consistency and standardization in assembly and in assembly-and-test utilities, relying often on real time control. The framework developed here addresses the systems design aspect of Mechatronics, covering the planning, execution, and control mechanisms. Planning includes assembly line balancing (ALB) and initial TS decisions, made continually by solving a bi-objective mixed-integer program (BOMIP). A collaborative multi-agent system (CMAS) enhanced with a TS-best matching (BM) protocol is developed to execute the plan, control the process, and modify the TS decisions, considering dynamic changes in the system’s operations. Experiments show that the new CA framework significantly outperforms classic approaches (i.e., ALB without TS-BM) in terms of cycle time, utilization of tools, and balanceability. In addition, the control mechanism is proven to augment the line’s flexibility against the inherent uncertainties of assembly processes, compared to the previously developed static CA frameworks.     

Structural health monitoring system based on multi-agent coordination and fusion for large structure

In practical applications of structural health monitoring technology, a large number of distributed sensors are usually adopted to monitor the big dimension structures and different kinds of damage. The monitored structures are usually divided into different sub-structures and monitored by different sensor sets. Under this situation, how to manage the distributed sensor set and fuse different methods to obtain a fast and accurate evaluation result is an important problem to be addressed deeply. In the paper, a multi-agent fusion and coordination system is presented to deal with the damage identification for the strain distribution and joint failure in the large structure. Firstly, the monitoring system is adopted to distributedly monitor two kinds of damages, and it self-judges whether the static load happens in the monitored sub-region, and focuses on the static load on the sub-region boundary to obtain the sensor network information with blackboard model. Then, the improved contract net protocol is used to dynamically distribute the damage evaluation module for monitoring two kinds of damage uninterruptedly. Lastly, a reliable assessment for the whole structure is given by combing various heterogeneous classifiers strengths with voting-based fusion. The proposed multi-agent system is illustrated through a large aerospace aluminum plate structure experiment. The result shows that the method can significantly improve the monitoring performance for the large-scale structure.     

Designing e-Coordinator for improved teams collaboration in graduation projects

The work presented in this paper describes the design for Graduation Project eCoordination System in our department. The proposed eCoordinator provides effective team–team, supervisor–team and coordinator–supervisors–teams collaboration. eCoordinator is an attribute based framework that serves evolving an object oriented software system into agent oriented software system. Nowadays, agent-oriented software development technologies have evolved rapidly; it is emerging as a new paradigm for constructing intelligent more autonomous software systems. Therefore, several methodologies are available and it is difficult to determine the most appropriate methodology for specific project within different domains. This is especially occurs when re-engineering current object oriented software system. The case study is going to be rebuilding a virtual graduation project coordinator in Information Technology Department at King Saud University.     

A bat-neural network multi-agent system (BNNMAS) for stock price prediction: Case study of DAX stock price

Creating an intelligent system that can accurately predict stock price in a robust way has always been a subject of great interest for many investors and financial analysts. Predicting future trends of financial markets is more remarkable these days especially after the recent global financial crisis. So traders who access to a powerful engine for extracting helpful information throw raw data can meet the success. In this paper we propose a new intelligent model in a multi-agent framework called bat-neural network multi-agent system (BNNMAS) to predict stock price. The model performs in a four layer multi-agent framework to predict eight years of DAX stock price in quarterly periods. The capability of BNNMAS is evaluated by applying both on fundamental and technical DAX stock price data and comparing the outcomes with the results of other methods such as genetic algorithm neural network (GANN) and some standard models like generalized regression neural network (GRNN), etc. The model tested for predicting DAX stock price a period of time that global financial crisis was faced to economics. The results show that BNNMAS significantly performs accurate and reliable, so it can be considered as a suitable tool for predicting stock price specially in a long term periods.     

Model checking temporal knowledge and commitments in multi-agent systems using reduction

Though modeling and verifying Multi-Agent Systems (MASs) have long been under study, there are still challenges when many different aspects need to be considered simultaneously. In fact, various frameworks have been carried out for modeling and verifying MASs with respect to knowledge and social commitments independently. However, considering them under the same framework still needs further investigation, particularly from the verification perspective. In this article, we present a new technique for model checking the logic of knowledge and commitments (CTLKC⁺). The proposed technique is fully-automatic and reduction-based in which we transform the problem of model checking CTLKC⁺ into the problem of model checking an existing logic of action called ARCTL. Concretely, we construct a set of transformation rules to formally reduce the CTLKC⁺ model into an ARCTL model and CTLKC⁺ formulae into ARCTL formulae to get benefit from the extended version of NuSMV symbolic model checker of ARCTL. Compared to a recent approach that reduces the problem of model checking CTLKC⁺ to another logic of action called GCTL¹, our technique has better scalability and efficiency. We also analyze the complexity of the proposed model checking technique. The results of this analysis reveal that the complexity of our reduction-based procedure is PSPACE-complete for local concurrent programs with respect to the size of these programs and the length of the formula being checked. From the time perspective, we prove that the complexity of the proposed approach is P-complete with regard to the size of the model and length of the formula, which makes it efficient. Finally, we implement our model checking approach on top of extended NuSMV and report verification results for the verification of the NetBill protocol, taken from business domain, against some desirable properties. The obtained results show the effectiveness of our model checking approach when the system scales up.     

Distributed self-tuning consensus and synchronization

The problem of self-tuning of coupling parameters in multi-agent systems is considered. Agent dynamics are described by a discrete-time double integrator with unknown input gain. Each agent locally tunes the strength of interaction with neighboring agents by using a normalized gradient algorithm (NGA). The tuning algorithm minimizes the square of the error between an individual agent’s state (velocity) and the one step delayed average of its own state and the states of its neighbors. Assuming that the network graph is strongly connected, it is proved that the sequence of coupling parameters is convergent and all velocities converge toward the same constant value.     

Cooperative preview tracking problem of discrete-time linear multi-agent systems: A distributed output regulation approach

We are concerned with the cooperative preview tracking problem of discrete-time linear multi-agent systems under the fixed directed acyclic communication topology. First, by introducing a preview-information-based auxiliary system and a type of distributed internal model, we establish an augmented system for each follower and formulate the cooperative preview tracking problem as a distributed output regulation (DOR) problem. Then, from the solvability of the DOR problem and the stabilizability of the augmented system, we present the sufficient conditions that guarantee the achievement of the cooperative tracking consensus. With this procedure, we finally propose a distributed dynamic feedback control law for the original problem with virtual regulated output integral and preview feedforward compensation.     

Leader-following consensus for a class of second-order nonlinear multi-agent systems

This paper deals with the leader-following consensus problem for a class of multi-agent systems with nonlinear dynamics and directed communication topology. The control input of the leader agent is assumed to be unknown to all follower agents. A distributed adaptive nonlinear control law is constructed using the relative state information between neighboring agents, which achieves leader-following consensus for any directed communication graph that contains a spanning tree with the root node being the leader agent. Compared with previous results, the nonlinear functions are not required to satisfy the globally Lipschitz or Lipschitz-like condition and the adaptive consensus protocol is in a distributed fashion. A numerical example is given to verify our proposed protocol.