This paper presents the design and implementation of an automated multi-phase facilitation agent based on LLM to realize inclusive facilitation and efficient use of a large language model (LLM) to facilitate realistic discussions. Large-scale discussion support systems have been studied and implemented very widely since they enable a lot of people to discuss remotely and within 24 hours and 7 days. Furthermore, automated facilitation artificial intelligence (AI) agents have been realized since they can efficiently facilitate large-scale discussions. For example, D-Agree is a large-scale discussion support system where an automated facilitation AI agent facilitates discussion among people. Since the current automated facilitation agent was designed following the structure of the issue-based information system (IBIS) and the IBIS-based agent has been proven that it has superior performance. However, there are several problems that need to be addressed with the IBIS-based agent. In this paper, we focus on the following three problems: 1) The IBIS-based agent was designed to only promote other participants' posts by replying to existing posts accordingly, lacking the consideration of different behaviours taken by participants with diverse characteristics, leading to a result that sometimes the discussion is not sufficient. 2) The facilitation messages generated by the IBIS-based agent were not natural enough, leading to consequences that the participants were not sufficiently promoted and the participants did not follow the flow to discuss a topic. 3) Since the IBIS-based agent is not combined with LLM, designing the control of LLM is necessary. Thus, to solve the problems mentioned above, the design of a phase-based facilitation framework is proposed in this paper. Specifically, we propose two significant designs: One is the design for a multi-phase facilitation agent created based on the framework to address problem 1); The other one is the design for the combination with LLM to address problem 2) and 3). Particularly, the language model called “GPT-3.5” is used for the combination by using corresponding APIs from OPENAI. Furthermore, we demonstrate the improvement of our facilitation agent framework by presenting the evaluations and a case study. Besides, we present the difference between our framework and LangChain which has generic features to utilize LLMs.

Despite the increasing use of conversational artificial intelligence (AI) in online discussion environments, few studies explore the application of AI as a facilitator in forming problem-solving debates and influencing opinions in cross-venue scenarios, particularly in diverse and war-ravaged countries. This study aims to investigate the impact of AI on enhancing participant engagement and collaborative problem-solving in online-mediated discussion environments, especially in diverse and heterogeneous discussion settings, such as the five cities in Afghanistan. We seek to assess the extent to which AI participation in online conversations succeeds by examining the depth of discussions and participants' contributions, comparing discussions facilitated by AI with those not facilitated by AI across different venues. The results are discussed with respect to forming and changing opinions with and without AI-mediated communication. The findings indicate that the number of opinions generated in AI-facilitated discussions significantly differs from discussions without AI support. Additionally, statistical analyses reveal quantitative disparities in online discourse sentiments when conversational AI is present compared to when it is absent. These findings contribute to a better understanding of the role of AI-mediated discussions and offer several practical and social implications, paving the way for future developments and improvements.

Considering the growth of the IoT network, there is a demand for a decentralized solution. Incorporating the blockchain technology will eliminate the challenges faced in centralized solutions, such as i) high infrastructure, ii) maintenance cost, iii) lack of transparency, iv) privacy, and v) data tampering. Blockchain-based IoT network allows businesses to access and share the IoT data within their organization without a central authority. Data in the blockchain are stored as blocks, which should be validated and added to the chain, for this consensus mechanism plays a significant role. However, existing methods are not designed for IoT applications and lack features like i) decentralization, ii) scalability, iii) throughput, iv) faster convergence, and v) network overhead. Moreover, current blockchain frameworks failed to support resource-constrained IoT applications. In this paper, we proposed a new consensus method (WoG) and a lightweight blockchain framework (iLEDGER), mainly for resource-constrained IoT applications in a permissioned environment. The proposed work is tested in an application that tracks the assets using IoT devices (Raspberry Pi 4 and RFID). Furthermore, the proposed consensus method is analyzed against benign failures, and performance parameters such as CPU usage, memory usage, throughput, transaction execution time, and block generation time are compared with state-of-the-art methods.

This paper proposes a switched pinning control method with a multi-rating mechanism for vehicle platoons. The platoons are expressed as multi-agent systems consisting of mass-damper systems in which pinning agents receive target velocities from external devices (ex. intelligent traffic signals). We construct model predictive control (MPC) algorithm that switches pinning agents via mixed-integer quadratic programmings (MIQP) problems. The optimization rate is determined according to the convergence rate to the target velocities and the inter-vehicular distances. This multi-rating mechanism can reduce the computational load caused by iterative calculation. Numerical results demonstrate that our method has a reduction effect on the string instability by selecting the pinning agents to minimize errors of the inter-vehicular distances to the target distances.

This paper proposes a distributed observer on a sensor network, where communication on the network is randomly performed. This work is a natural extension of Kalman consensus filter approach to the cases involving random communication. In both bidirectional and unidirectional communication cases, gain conditions that guarantee improvement of estimation error convergence compared to the case with no communication are obtained. The obtained conditions are more practical than those of previous studies and give appropriate cooperative gains for a given communication probability. The effectiveness of the proposed method is confirmed by computer simulations.

Diffusion least-mean-square (LMS) is a method to estimate and track an unknown parameter at multiple nodes in a network. When the unknown vector has sparsity, the sparse promoting version of diffusion LMS, which utilizes a sparse regularization term in the cost function, is known to show better convergence performance than that of the original diffusion LMS. This paper proposes a novel choice of the coefficients involved in the updates of sparse diffusion LMS using the idea of message propagation. Moreover, we optimize the proposed coefficients with respect to mean-square-deviation at the steady-state. Simulation results demonstrate that the proposed method outperforms conventional methods in terms of the convergence performance.

This paper presents a large-angle imaging algorithm based on a dynamic scattering model for inverse synthetic aperture radar (ISAR). In this way, more information can be presented in an ISAR image than an ordinary RD image. The proposed model describes the scattering characteristics of ISAR target varying with different observation angles. Based on this model, feature points in each sub-image of the ISAR targets are extracted and matched using the scale-invariant feature transform (SIFT) and random sample consensus (RANSAC) algorithms. Using these feature points, high-precision rotation angles are obtained via joint estimation, which makes it possible to achieve a large angle imaging using the back-projection algorithm. Simulation results verifies the validity of the proposed method.

This paper proposes an adaptive call-occurrence probability (COP) setting method for a slotted-ALOHA based consensus problem. Individual agents in the focused consensus problem control themselves in a distributed manner based on the partial information of overall control system which can be received only from the neighbor agents. In order to realize a reliable consensus problem based on wireless communications, we have to consider several constraints caused by the natures of wireless communications such as communication error, coverage, capacity, multi-user interference, half-duplex and so on. This work first investigates the impacts of wireless communication constraints, especially communication coverage, half-duplex, and multiple-access interference constraints, on the quality of control. To mitigate the impact of multiple-access constraint, we propose an adaptive COP setting method that changes the COP corresponding to the states of communication and control. The proposed adaptive COP based slotted-ALOHA needs the information about the number of neighbor agents at its own and neighbor agents, but can still work in a distributed manner. Computer simulations show that the proposed system can achieve better convergence performance compared to the case with the fixed COP based system.

Pinning control of multi-agent systems is a method that the external control input is added to some agents (pinning nodes), e.g., leaders. By the external control input, consensus to a certain target value and faster consensus are achieved. In this paper, we propose a new method of self-triggered predictive pinning control for the consensus problem. Self-triggered control is a method that both the control input and the next update time are calculated. Using self-triggered control, it is expected that the communication cost can be reduced. First, a new finite-time optimal control problem used in self-triggered control is formulated, and its solution method is derived. Next, as an on-line algorithm, two methods, i.e., the multi-hop communication-based method and the observer-based method are proposed. Finally, numerical examples are presented.

We propose a distributed blind equalization method for wireless sensor networks, in which a source sends data and each node performs time-domain equalization to estimate the data from a received signal that is affected by inter-symbol interference. The equalization can be performed distributively based on the mutually referenced equalization principle. Even if the nodes in the network are not fully connected to each other, the average consensus technique enables us to perform the equalization of all channels.

The output feedback consensus problem of lower triangular nonlinear systems under a directed network with a switching topology is studied. It is assumed that every possible network topology contains a directed spanning tree. The proposed design method utilizes a high gain approach to compensate for triangular nonlinearity and to remove the restriction imposed on dwell time. Compared to the previous research, it is shown that the proposed control method can achieve the output feedback consensus of lower triangular nonlinear systems even in the presence of an arbitrarily small average dwell time. A numerical example is given to illustrate the effectiveness of the proposed design method.

In this paper, based on the policy of model predictive control, a new method of predictive pinning control is proposed for the consensus problem of multi-agent systems. Pinning control is a method that the external control input is added to some agents (pinning nodes), e.g., leaders. By the external control input, consensus to a certain target value (not the average of the initial states) and faster consensus are achieved. In the proposed method, the external control input is calculated by the controller node connected to only pinning nodes. Since the states of all agents are required in calculation of the external control input, communication delays must be considered. The proposed algorithm includes not only calculation of the external control input but also delay compensation. The effectiveness of the proposed method is presented by a numerical example.

The output feedback consensus problem of nonlinear multi-agent systems under a directed network with a time varying communication delay is studied. In order to deal with this problem, the dynamic output feedback controller with an additional low gain parameter that compensates for the effect of nonlinearity and a communication delay is proposed. Also, it is shown that under some assumptions, the proposed controller can always solve the output feedback consensus problem even in the presence of an arbitrarily large communication delay.

Considering that different people are different in their linguistic preference and in order to determine the consensus state when using Computing with Words (CWW) for supporting consensus decision making, this paper first proposes an interval composite scale based 2-tuple linguistic model, which realizes the process of translation from word to interval numerical and the process of retranslation from interval numerical to word. Second, this paper proposes an interval composite scale based personalized individual semantics model (ICS-PISM), which can provide different linguistic representation models for different decision-makers. Finally, this paper proposes a consensus decision making model with ICS-PISM, which includes a semantic translation and retranslation phase during decision process and determines the consensus state of the whole decision process. These models proposed take into full consideration that human language contains vague expressions and usually real-world preferences are uncertain, and provide efficient computation models to support consensus decision making.

We present a hierarchical replicated state machine (H-RSM) and its corresponding consensus protocol D-Paxos for replication across multiple data centers in the cloud. Our H-RSM is based on the idea of parallel processing and aims to improve resource utilization. We detail D-Paxos and theoretically prove that D-Paxos implements an H-RSM. With batching and logical pipelining, D-Paxos efficiently utilizes the idle time caused by high-latency message transmission in a wide-area network and available bandwidth in a local-area network. Experiments show that D-Paxos provides higher throughput and better scalability than other Paxos variants for replication across multiple data centers. To predict the optimal batch sizes when D-Paxos reaches its maximum throughput, an analytical model is developed theoretically and validated experimentally.

This paper investigates the consensus problem of heterogeneous uncertain multi-agent systems with jointly connected topology, where the considered systems are composed of linear first-order, second-order dynamics and nonlinear Euler-Lagrange (EL) dynamics. The consensus protocol is designed to converge the position and velocity states of the linear and nonlinear heterogeneous multi-agent systems under joint connected topology, and then the adaptive consensus protocol is also proposed for heterogeneous multi-agent systems with unknown parameters in the EL dynamics under jointly connected topology. Stability analysis for piecewise continuous functions induced by the jointly connection is presented based on Lyapunov function and Cauchy's convergence criteria. Finally, some simulation results are provided to verify the effectiveness of the proposed methods.

This letter deals with the consensus problem of multi-agent systems, which are composed of feedforward nonlinear systems under a directed network with a communication time delay. In order to solve this problem, a new consensus protocol with a low gain parameter is proposed. Moreover, it is shown that under some sufficient conditions, the proposed protocol can solve the consensus problem of nonlinear multi-agent systems even in the presence of an arbitrarily large communication delay. An illustrative example is presented to verify the validity of the proposed approach.

This letter investigates the consensus problem for an undirected network of high-order integrators with an arbitrarily large communication delay. A consensus protocol with the low gain parameter that can eliminate an effect of time delay on the consensus problem is proposed newly. Moreover, it is proved that under some sufficient conditions, it can solve the consensus problem in the presence of an arbitrarily large communication delay. A simulation example is presented to verify the validness of the proposed design.

Headway irregularity not only increases average passenger waiting time but also causes additional energy consumption and more delay time. A real-time headway control model is proposed to maintain headway regularity in railway networks by adjusting the travel time on each segment for each train. The adjustment of travel time is based on a consensus algorithm. In the proposed consensus algorithm, the control law is obtained by solving the Riccati equation. The minimum running time on a segment is also considered. The computation time of the proposed method is analyzed and the analysis results show that it can satisfy the requirement on real-time operation. The proposed model is tested and the consensus trend of headways can be observed through simulation. The simulation results also demonstrate that the average passenger waiting time decreases from 52 to 50 seconds/passenger. Additionally, the delay time is reduced by 6.5% at least and energy consumption can be reduced by 0.1% at most after using the proposed method.

We propose a fast and resource-efficient agreement protocol on a request set, which is used to realize Byzantine fault tolerant server replication. Although most existing randomized protocols for Byzantine agreement exploit a modular approach, that is, a combination of agreement on a bit value and a reduction of request set values to the bit values, our protocol directly solves the multi-valued agreement problem for request sets. We introduce a novel coin tossing scheme to select a candidate of an agreed request set randomly. This coin toss allows our protocol to reduce resource consumption and to attain faster response time than the existing representative protocols.