The increasing performance of large language models (LLMs) encourages research on using LLM-based multiagent systems (MAS) to simulate and predict human activities in the real world, especially in the context of negotiation simulation. Meanwhile, the consensus-reaching process (CRP) research has been developed to simulate people’s perception of consensus in practice, particularly the development of consensus models to address specific consensus-reaching issues. However, current LLM negotiation studies largely rely on prompts and outcome scores, offering limited guarantees on stability, salience-aware behaviour, or meaningful termination, whereas CRP provides explicit consensus indices, update logic, and stopping rules. Bridging these lines of work enables negotiation that is measurable rather than anecdotal and reliable rather than round-cap dependent. This paper argues that a bridge between these lines of work is timely and introduces a systematic framework to bring the language of consensus—quality, stability, fairness, and robustness—into LLM-based MAS, allowing different agent designs to be compared on common standards. We introduce and combine methods of the fuzzy logic theory to quantify people’s preferences. We also design a general workflow for LLM-based agents to reach potential consensus in two predefined CRP scenarios. Finally, we introduce and define several cross-paradigm metrics to evaluate the performance of three different agent designs. The experimental results indicate that incorporating consensus models improves stability and fairness. Overall, the paper reframes LLM negotiation as consensus-aware, stability-measurable negotiation, providing a practical bridge between CRP theory and LLM-based agents, and offering a reproducible toolkit for transparent, comparable assessment of multi-issue negotiation.

We investigate a practical yet underexplored variant of the influence maximization problem, in which the goal is to activate a given sequence of nested subgraphs as far outward as possible. A subgraph is considered activated if either it reaches a prescribed influence threshold within itself, or a larger subgraph containing it in the nested sequence is activated. We formally define this problem as Structure-Aware Influence Maximization (SAIM) and show that it is NP-hard and its objective function is non-submodular. To tackle SAIM, we propose a decomposition framework that reduces it to a sequence of subproblems, each called Target Subgraph Activation Probability Maximization (TSAPM) . For TSAPM, we propose two oracles: a Sandwich Approximation (SA) framework with data-dependent guarantees, and a heuristic algorithm tailored to the TSAPM objective. Combined with the SA-based oracle, the overall decomposition framework provides a solution-dependent approximation guarantee for the SAIM problem. Extensive experiments on six real-world datasets validate the effectiveness and efficiency of our proposed methods.

In multi-agent systems, agents with limited capabilities need to find a cooperation partner to accomplish complex tasks. Evaluating the trustworthiness of potential partners is vital in partner selection. Current approaches are mainly averaged-based, aggregating advisors’ information on partners. These methods have limitations, such as vulnerability to unfair rating attacks, and may be locally convergent that cannot always select the best partner. Therefore, we propose a ranking-based partner selection (RPS) mechanism, which clusters advisors into groups according to their ranking of trustees and gives recommendations based on groups. Besides, RPS is an online-learning method that can adjust model parameters based on feedback and evaluate the stability of advisors’ ranking behaviours. Experiments demonstrate that RPS performs better than state-of-the-art models in dealing with unfair rating attacks, especially when dishonest advisors are the majority.

Open market environments consist of a set of participants (vendors and consumers) that dynamically leave or join the market. As a result, the arising dynamism leads to uncertainties in supply and demand of the resources in these open markets. In specific, in such uncertain markets, vendors attempt to maximise their revenue by dynamically changing their selling prices according to the market demand. In this regard, an optimal resource allocation approach becomes immensely needed to optimise the selling prices based on the supply and demand of the resources in the open market. Therefore, optimal selling prices should maximise the revenue of vendors while protecting the utility of buyers. In this context, we propose a real-time pricing approach for resource allocation in open market environments. The proposed approach introduces a priority-based fairness mechanism to allocate the available resources in a reverse-auction paradigm. Finally, we compare the proposed approach with two state-of-the-art resource allocation approaches. The experimental results show that the proposed approach outperforms the other two resource allocation approaches in its ability to maximise the vendors’ revenue.

Abstract Aspect terms are opinion targets for people to express and understand opinions in reviews. Aspect terms extraction is an essential subtask in aspect-level sentiment analysis. To extract aspect terms from a sentence, existing methods mainly focus on context features generated by pre-trained models. However, these models either neglect the crucial implicit linguistic features, e.g., post-of-tag, head, and head dependency, or fail to explore sufficient valuable features for aspect term extraction, which lead to the deficiency in aspect term extraction task. To address the challenges, in this paper, we propose a novel and effective framework for aspect term extraction by integrating both contextual and linguistic features with the artificial bee colony-based feature selection method. Firstly, a novel variant of artificial bee colony is designed to identify the most valuable linguistic features to reduce the high sparsity and dimensionality of the raw dataset. Next, the selected features and context embeddings are integrated to improve the performance of aspect extraction. Finally, extensive experiments are conducted on real-world datasets, and the results exhibit that our proposed framework can outperform the competitive baselines. Compared with the latest baselines, the proposed framework achieves the comparatively higherF1 scores of 80.7%, 84.7%, 72.2%, and 74.8% on the four groups of datasets. Furthermore, the ablation study shows that the proposed method with the designed feature selection module significantly outperforms the method with the original artificial bee colony, having 4.15%, 4.4%, 4.4%, and 3.2% improvements inF1 score on all the four datasets, respectively.

In the era of advanced technologies, mobile devices are equipped with computing and sensing capabilities that gather excessive amounts of data. These amounts of data are suitable for training different learning models. Cooperated with Deep Learning (DL) advancements, these learning models empower numerous useful applications, e.g., image processing, speech recognition, healthcare, vehicular network, and many more. Traditionally, Machine Learning (ML) approaches require data to be centralised in cloud-based data-centres. However, this data is often large in quantity and privacy-sensitive, preventing logging into these data-centres for training the learning models. In turn, this results in critical issues of high latency and communication inefficiency. Recently, in light of new privacy legislation in many countries, the concept of Federated Learning (FL) has been introduced. In FL, mobile users are empowered to learn a global model by aggregating their local models without sharing the privacy-sensitive data. Usually, these mobile users have slow network connections to the data-centre where the global model is maintained. Moreover, in a complicated and extensive scale network, heterogeneous devices with various energy constraints are involved. This raises the challenge of communication cost when implementing FL at a large scale. To this end, in this research, we begin with the fundamentals of FL, and then we highlight the recent FL algorithms and evaluate their communication efficiency with detailed comparisons. Furthermore, we propose a set of solutions to alleviate the existing FL problems from a communication perspective and a privacy perspective.

Rehabilitation on a regular basis is essential to recover functional ability for patients with physical disabilities. However, performing correct movements without the therapist's guidance is considered to be a challenging problem. To this end, we propose an interactive system to support patients through rehabilitation, where a movement guidance agent is included. Our system recognizes and assists patients while engaging in rehabilitation activities. The proposed approach consists of three significant steps: (1) the estimation of patient movement through the camera; (2) the detection of patient movement; and (3) the detected movements, are then observed by an agent in order to instruct correct movement, if the movement was incorrectly performed. To this end, we propose an agent-based system that allows users to perform rehabilitation exercises anywhere with the proper guidance system. The proposed system is applied to track patient activities in real-time, with lightweight devices such as mobile phones.

Large-scale online discussion platforms are receiving great attention as potential next-generation methods for smart democratic citizen platforms. One of the studies clarified the critical problem faced by human facilitators caused by the difficulty of facilitating large-scale online discussions. In this demonstration, we present our current implementation of D-agree, a crowd-scale discussion support system based on an automated facilitation agent. We conducted a large-scale social experiment with Nagoya local government. The results demonstrate that the agent worked well compared with human facilitators.

Automated negotiations based on learning models have been widely applied in different domains of negotiation. Specifically, for resource allocation in decentralised open market environments with multiple vendors and multiple buyers. In such open market environments, there exists dynamically changing supply and demand of resources, with dynamic arrival of buyers in the market. Besides, each buyer has their own set of constraints, such as budget constraints, time constraints, etc. In this context, efficient negotiation policies should be capable of maintaining the equilibrium between the utilities of both the vendors and the buyers. In this research, we aim to design a mechanism for an optimal auction paradigm, considering the existence of interdependent undisclosed preferences of both, buyers and vendors. Therefore, learning-based negotiation models are immensely appropriate for such open market environments; wherein, self-interested autonomous vendors and buyers cooperate/compete to maximize their utilities based on their undisclosed preferences. Toward this end, we present our current proposal, the two-stage learning-based resource allocation mechanism, wherein utilities of vendors and buyers are optimised at each stage. We are aiming to compare our proposed learning-based resource allocation mechanism with two state-of-the-art bidding-based resource allocation mechanism, which are based on, fixed bidding policy (Samimi, Teimouri, and Mukhtar 2016) and demand-based bidding policy (Kong, Zhang, and Ye 2015). The comparison is to be done based on the overall performance of the open market environment and also based on the individual performances of vendors and buyers.

This paper presents a novel case-based reasoning (CBR) application to crowd-scale deliberation. We propose a CBR approach to facilitating online discussion for crowd-scale deliberation. The objective is to smooth the discussion by avoiding flaming and to efficiently achieve a consensus. Accordingly, several challenging issues are addressed, including case definition and its structure, case creation, and implementation by incorporating with COLLAGREE, a crowd-scale deliberation platform supporting online discussion with the help of facilitators. After introducing an overview of the crowd-scale deliberation and the COLLAGREE, the paper presents the details of the proposed CBR approach for facilitation of online discussion along with some preliminary results. The feasibility of CBR-based facilitation support for crowd-scale deliberations is demonstrated.

status: Published

This paper proposes a joint TDMA and CSMA-based traffic Adaptive Intelligent Hybrid Medium Access Control protocol (AI-HMAC) for low-power duty cycle Wireless Sensor Networks (WSNs). The objective of AI-HMAC is to modify the link scheduling and leveraging contention windows mechanism of IH-MAC and smooth the coordination among them by avoiding delay and to efficiently achieve energy consumption. Accordingly, AI-HMAC reintroduces the idea to combines the functionalities of CSMA, TDMA, broadcast scheduling and link scheduling in order to improve throughput and energy efficiency of protocol. It can operate under wide range of data traffic and choosing send/receive mechanism technique according to packet rate and loss with extending energy minimization techniques and bringing down energy consumption. Another advantage of AI-HMAC is to regulating initial contention window according flowing traffic asking rate between nodes in order to achieve high channel utilization under high traffic load without any energy compromising and to minimize certain delay probability that may cause with data collision. AI-HMAC doing this task through applying prioritized back-off scheme to make efficient time adaptive for node idle listening period during Back-off process. It adjusts contention for adapting traffic load with suitable duty cycle length in order to increase energy efficiency. And by leveraging and exploiting the concept of parallel transmission to minimize energy consumption during communication. The mathematical analytical expression of our protocol are presented to compute the energy consumption efficiency and the simulation showing how AI-HMAC efficiently manages compare evaluated protocols in terms of network life, energy consumption and latency.

This paper proposes a joint TDMA and CSMA-based traffic Adaptive Intelligent Hybrid Medium Access Control protocol (AI-HMAC) for low-power duty cycle Wireless Sensor Networks (WSNs). The objective of AI-HMAC is to modify the link scheduling and leveraging contention windows mechanism of IH-MAC and smooth the coordination among them by avoiding delay and to efficiently achieve energy consumption. Accordingly, AI-HMAC reintroduces the idea to combines the functionalities of CSMA, TDMA, broadcast scheduling and link scheduling in order to improve throughput and energy efficiency of protocol. It can operate under wide range of data traffic and choosing send/receive mechanism technique according to packet rate and loss with extending energy minimization techniques and bringing down energy consumption. Another advantage of AI-HMAC is to regulating initial contention window according flowing traffic asking rate between nodes in order to achieve high channel utilization under high traffic load without any energy compromising and to minimize certain delay probability that may cause with data collision. AI-HMAC doing this task through applying prioritized back-off scheme to make efficient time adaptive for node idle listening period during Back-off process. It adjusts contention for adapting traffic load with suitable duty cycle length in order to increase energy efficiency. And by leveraging and exploiting the concept of parallel transmission to minimize energy consumption during communication. The mathematical analytical expression of our protocol are presented to compute the energy consumption efficiency and the simulation showing how AI-HMAC efficiently manages compare evaluated protocols in terms of network life, energy consumption and latency.

Due to the high density of Wi‐Fi networks, especially in the unlicensed 2.4 GHz frequency band, channel assignment has become a critical duty for achieving a satisfactory user experience. Probably, the main peculiarity of Wi‐Fi networks is the partial overlap of the radio channels that can be used by access points. For that reason, a number of works avoid cochannel interferences by using only channels which are far enough from each other to have no interferences, the so‐called orthogonal channels. However, there is a range of choices between using the whole spectrum and using only orthogonal channels. In this work we evaluate the influence of the choice of channel set in realistic settings, using both optimization and heuristic approaches. Results show that the optimizer is not able to achieve better results when using the whole spectrum instead of restricting to only the orthogonal channels. In fact, the optimizer uses mainly the orthogonal channels when they are available, while the heuristics considered lose performance when more channels are available. We believe this insight will be useful to design new heuristics for Wi‐Fi channel assignment.

The annual International Automated Negotiating Agents Competition (ANAC) is used by the automated negotiation research community to benchmark and evaluate its work andto challenge itself. The benchmark problems and evaluation results and the protocols and strategies developed are available to the wider research community.

The stochastic cell transmission model (SCTM) is a macroscopic traffic simulation model of high accuracy. One of its advantages is that it can represent the uncertainty of the traffic states and the changing travel demand and supply conditions. However, it has been applied to freeways and simple networks having only one origin‐destination pair. In this article, we propose a modified stochastic cell transmission model (M‐SCTM) that applies the conventional SCTM to urban networks. In M‐SCTM, we introduce vehicle agents as well as their route choice behavior on an urban network, which is more applicable to complex urban networks. Additionally, M‐SCTM was applied to networks in which the turning ratio is not priorly set through a route search algorithm. The results show that M‐SCTM can conduct simulations with as much accuracy as SCTM. Furthermore, we verified the appropriate reproducibility of the simulations based on M‐SCTM and compared the estimated value and the measured value in terms of the travel time of each vehicle.

In the rivers area, it has become frequent problems with illegal dumping, flood, inundation, and damage. We need the support and understanding of the problems quickly. Future measures also require. River basin is a wide range. Only the river administrator is difficult to perform adequate monitor and response. River Information Management System was developed by the collaboration with the Ministry of Land, Infrastructure and Transport Chubu Regional Development Bureau Shonai River river office In order to strengthen the monitoring of the river. River Information Management System is composed of a Post Management System and River Information Applications. River Information Application is an application using the GPS function and camera functions of the smartphone. Post Management System is a system for managing the problems by river administrator.

In recent years, and landslides caused by flooding of the river due to heavy rain, the number of such flooding of houses has increased. For this reason, the number of casualties has increased due to flooding of rivers and canals. Therefore, the situational awareness of the rivers and canals in the event of a disaster is important. We are research on wireless sensor networks (WSNs) for the purpose of collecting information in a disaster situation. Also we have developed a ad-hoc water gauge system based on WSN. However, dedicated WSN can not be used for general purposes for the purpose of collecting the disaster information. Therefore, WSN aimed only use of disaster is not practical considering cost-effectiveness. We implemented a general-purpose WSN system for can be connected to the required sensors in the event of a disaster. In this paper, we describe challenges and implementation of WSN system that allows a diversity sensor connection.

Demand Response (DR) has been extensively studied as one of the important features of smart grid. The DR strategies can be grouped into two categories, one is incentive-based DR and the other is pricing-based DR. Our work focuses on DR involving both pricing factor and incentive factor using scoring rule. In the literature, several DR mechanisms have been proposed, however, most studies have not focused on the cooperation among consumers although it is important to devise an efficient and stable DR. In this paper, we propose a cooperative demand response mechanism by using a truthful allocation mechanism with scoring rule. The brief ideas of our model are the following: the consumers will be rewarded a discount on the price to measure up how well they predict demand shift. A reward mechanism is based on a strictly proper scoring rule. This mechanism is applied between consumer agents (CA) to Cooperative Demand Response System (CDRS) and Generation Company (GENCO). The proposed mechanism is tested on real data provided by Chubu Electric Power Company and we show that this mechanism is capable of reducing peak demand.

In recent years, and landslides caused by flooding of the river due to heavy rain, the number of such flooding of houses has increased. With regard to internal water damage, the structure and of the river, above sea level and, such as where a landfill, how completely prevent flood damage by internal water is difficult. At present, it is only possible addressing of Operating the drainage devices typified by car to pump submerged location. However, in order to quickly drained work as possible, it is necessary to understand the full flooding water by the water level data of the flooding point throughout. To that end we have done a prototype of a portable ad hoc simple water gauge for the purpose of installation in the field in collaboration with Nippon Koei Co., Ltd. In particular, in a portable of the wireless sensor networks, communication reliability is greatly influenced by the surrounding environmental factors by communication node movement. Therefore, differ from the fixed wireless sensor networks. It, the radio wave condition and buildings around which changes due to the movement, is the effect of such plants. Furthermore, features and issues required portable ad hoc simple water gauge system is operated in the actual disaster site, it is necessary to determine standing worker eyes for performing production. In this paper, it is possible to organize the task of ad hoc simple water gauge system that assumes the movement, was carried out implementation of portable wireless sensor networks.

There has been a great deal of interest about negotiations having interdependent issues and nonlinear utility spaces as they arise in many realistic situations. In this case, reaching a consensus among agents becomes more difficult as the search space and the complexity of the problem grow. Nevertheless, none of the proposed approaches tries to quantitatively assess the complexity of the scenarios in hand, or to exploit the topology of the utility space necessary to concretely tackle the complexity and the scaling issues. We address these points by adopting a representation that allows a modular decomposition of the issues and constraints by mapping the utility space into an issue-constraint hypergraph. Exploring the utility space reduces then to a message passing mechanism along the hyperedges by means of utility propagation. Adopting such representation paradigm will allow us to rigorously show how complexity arises in nonlinear scenarios. To this end, we use the concept of information entropy in order to measure the complexity of the hypergraph. Being able to assess complexity allows us to improve the message passing algorithm by adopting a low-complexity propagation scheme. We evaluated our model using parametrized random hyper- graphs, showing that it can optimally handle complex utility spaces while outperforming previous sampling approaches.

Most real‐world negotiation involves multiple interdependent issues, which makes an agent’s utility functions nonlinear. Traditional negotiation mechanisms, which were designed for linear utilities, do not fare well in nonlinear contexts. One of the main challenges in developing effective nonlinear negotiation protocols is scalability; they cannot find a high‐quality solution when there are many issues, due to computational intractability. One reasonable approach to reducing computational cost, while maintaining good quality outcomes, is to decompose the utility space into several largely independent subspaces. In this paper, we propose a method for decomposing a utility space based on every agent’s utility space. In addition, the mediator finds the contracts in each group based on the votes from all agents, and combines the contract in each issue‐group. This method allows good outcomes with greater scalability than the method without issue‐grouping. We demonstrate that our protocol, based on issue‐groups, has a higher optimality rate than previous efforts, and discuss the impact on the optimality of the negotiation outcomes.

There is an increasing interest in complex automated negotiations, where agents negotiate about multiple, interdependent issues and agent utility functions exhibit low autocorrelation. In these scenarios, the negotiation mechanisms used to find agreement solutions among agents tend to fail due to the complexity of agents’ preference spaces, and this tendency increases as the degree of autocorrelation decreases. In this paper, we propose an automated negotiation model specially tailored for highly uncorrelated utility spaces based on weighted constraints. The model relies on a mediated, auction‐based interaction protocol and a set of heuristic mechanisms for bidding and deal identification. To address the challenges raised by highly uncorrelated utility spaces, we propose to use a quality factor, which allows agents to balance utility and deal probability when placing their bids or when searching for agreement regions among these bids. Experiments show that the proposed negotiation model achieves high optimality results and low failure rates even in negotiation scenarios involving highly uncorrelated utility spaces, thus outperforming previous approaches.

The number of senior citizens living alone are increasing in Japan. Accordingly, the budget for social security is increasing. The percentage of burden for social security budgets reached 69.5% only for senior citizens recently, and will increase more and more. These budgets are consumed in mainly in the larger hospitals. Thus, recently “in-house” health care for senior citizens is gathering much attention in Japan. Various “home-care” products are increasingly developed and implemented to care the health of the senior citizens. However, these products are usually expensive and their self installation is very difficult. In this research, we developed a wireless sensor network system that realizes easy installation and easy operation. Our preliminary experiments demonstrate that our system can surely find some anomaly sensing information without any difficult installation procedures.

Multi-issue negotiation protocols are an important field of study because real-world negotiation problems are often complex and involve multiple issues. Although much previous work has only addressed linear utility function, that is, simple negotiations involving independent issues, recently, non-linear utility functions for complex negotiations involing interdependent issues have gained attention. Most studies, however, do not focus on the changes in utility space over time. In economic theory, it is often assumed that the utility function changes dynamically over time. It is important to seek the Pareto front, which refers to the set of Pareto optimal points, in negotiation problems. Therefore, in this paper we propose a complex utility space that changes over time and a negotiation mechanism in which the mediator takes the lead in negotiation based on the genetic algorithm (GA). The experimental results show that our approach is suitable for utility that dynamically changes over time, and finds and follows the Pareto front effectively.

Internet auctions are more than just electronic commerce; they have also become crucial for actual commerce. Because such activity occurs on the Internet, we need a way to determine people's reputations for such commerce. Thus reputation mechanisms a

Most real-world negotiation involves multiple interdependent issues, which makes an agent's utility functions complex. Traditional negotiation mechanisms, which were designed for linear utilities, do not fare well in nonlinear contexts. One of the main challenges in developing effective nonlinear negotiation protocols is scalability; it can be extremely difficult to find high-quality solutions when there are many issues, due to computational intractability. One reasonable approach to reducing computational cost, while maintaining good quality outcomes, is to decompose the contract space into several largely independent sub-spaces. In this paper, we propose a method for decomposing a contract space into sub-spaces based on the agent's utility functions. A mediator finds sub-contracts in each sub-space based on votes from the agents, and combines the sub-contracts to produce the final agreement. We demonstrate, experimentally, that our protocol allows high-optimality outcomes with greater scalability than previous efforts. We also address incentive compatibility issues. Any voting scheme introduces the potential for strategic non-truthful voting by the agents, and our method is no exception. For example, one of the agents may always vote truthfully, while the other exaggerates so that its votes are always "strong." It has been shown that this biases the negotiation outcomes to favor the exaggerator, at the cost of reduced social welfare. We employ the limitation of strong votes to the method of decomposing the contract space into several largely independent sub-spaces. We investigate whether and how this approach can be applied to the method of decomposing a contract space.

Combinatorial auctions, one of the most popular market mechanisms, have a huge effect on electronic markets and political strategies. Combinatorial auctions provide suitable mechanisms for efficient allocation of resources to self-interested attendee

Recently, the World Wide Web (WWW) has been attracting much attention as the predominant method of sharing information using multimedia contents. However, when users want to provide multimedia contents on the Web, they must learn how to use authoring software or how to write HTML documents. In this paper, we propose a Web information-sharing system called "Big Blackboard", which is based on a large Web page. The large Web page can act as a metaphor for real-world bulletin boards to realize not only a one-dimensional message board but also a two-dimensional message board on the Web. Any user can easily provide multimedia contents using unlimited layout through a Web browser without needing to know HTML. In the case of displaying on a large Web page, loading Web page contents onto a Web browser may require users to wait a long time. We propose a novel method that reduces transmission cost by only loading the contents within the scope at which a user is looking at an entire Web page. The experimental results demonstrate that our method can effectively reduce user waiting time for loading a Web page.