The growing popularity of users in online social network gives a big opportunity for online auction. The famous Information Diffusion Mechanism (IDM) is an excellent method even meet the incentive compatibility and individual rationality. Although the existing auction in online social network has considered the buyers’ information which is not known by the seller, current mechanism still can not preserve the privacy information of users in online social network. In this paper, we propose a novel mechanism based on the IDM and differential privacy. Our mechanism can successfully process the auction and at the same time preserve clients’ price information from neighbours. We achieved these by adding virtual nodes to each node and Laplace noise for its price in the auction process. We also formulate this mechanism on the real network and the random network, scale-free network to show the feasibility and effectiveness of the proposed mechanism. The evaluation shows that the result of our methods only depend on the noise added to the agents. It is independent from the agents’ original price.

Agent-based scheduling refers to applying intelligent agents to autonomously allocate resources to jobs. Decentralized agent-based scheduling approaches have achieved good performance in open and dynamic environments because the relationships of agents are flexible. For new jobs and resources and unexpected events, decentralized agents can respond adaptively and flexibly. Besides, decentralized approaches are easy to be extended because there is no central control agent that limits the scalability. However, decentralized approaches might have low efficiency in large-scale environments because behaviors of agents may be self-interested and competitive, due to their local views during decision making. When interacting with a large number of agents, each agent may spend a considerable amount of time on failed attempts before reaching the final agreements with other agents. To improve the efficiency of decentralized agent-based scheduling approaches in large-scale environments, and to keep the flexibility and adaptability of decentralized agents for the decision-making on scheduling, this paper provides a new agent-based adaptive mechanism for efficient job scheduling. A new type of agent named host agent is introduced to coordinate self-interested behaviors of agents without participating in the decision making of agents during job scheduling. The proposed mechanism was developed in JADE and tested in open and large-scale environments. The experimental results indicate that the proposed mechanism is effective and efficient in open and large-scale environments.

The research of multiple negotiations considering issue interdependence across negotiations is considered as a complex research topic in agent negotiation. In the multiple negotiations scenario, an agent conducts multiple negotiations with opponents for different negotiation goals, and issues in a single negotiation might be interdependent with issues in other negotiations. Moreover, the utility functions involved in multiple negotiations might be nonlinear, e.g., the issues involved in multiple negotiations are discrete. Considering this research problem, the current work may not well handle multiple interdependent negotiations with complex utility functions, where issues involved in utility functions are discrete. Regarding utility functions involving discrete issues, an agent may not find an offer exactly satisfying its expected utility during the negotiation process. Furthermore, as sub-offers on issues in every single negotiation might be restricted by the interdependence relationships with issues in other negotiations, it is even harder for the agent to find an offer satisfying the expected utility and all involved issue interdependence at the same time, leading to a high failure rate of processing multiple negotiations as a final outcome. To resolve this challenge, this paper presents a negotiation model for multiple negotiations, where interdependence exists between discrete issues across multiple negotiations. By introducing the formal definition of “interdependence between discrete issues across negotiations”, the proposed negotiation model applies the multiple alternating offers protocol, the clustered negotiation procedure and the proposed negotiation strategy to handle multiple interdependent negotiations with discrete issues. In the proposed strategy, the “tolerance value” is introduced as an agent’s consideration to balance between the overall negotiation goal and the negotiation outcomes. The experimental results show that, 1) the proposed model well handles the multiple negotiations with interdependence between discrete issues, 2) the proposed approach is able to help agents in the decision-making process of proposing acceptable offers, 3) an agent can choose a proper “tolerance value” to balance between the success rate of multiple negotiations and its expected utility.

With advancements in technology, personal computing devices are better adapted for and further integrated into people’s lives and homes. The integration of technology into society also results in an increasing desire to control who and what has access to sensitive information, especially for vulnerable people including children and the elderly. With blockchain rise as a technology that can revolutionize the world, it is now possible to have an immutable audit trail of locational data over time. By controlling the process through inexpensive equipment in the home, it is possible to control whom has access to such personal data. This paper presents a block-chain based family security system for outdoor tracking and in-house monitoring of users’ activities via sensors to detect anomalies in users’ daily activities with the integration of Artificial Intelligence (AI). For outdoor tracking the locations of the consenting family members’ smart phones are logged and stored in a private blockchain which can be accessed through a node installed in the family home on a computer. The data for the whereabouts and daily activities of family members stays securely within the family unit and does not go to any third-party organizations. A Self-Organizing Maps (SOM) based smart contract is used for anomaly detection in users’ daily activities in a smart home, which notifies emergency contact or other family members in case of anomaly detection. The approach described in this paper contributes to the development of in-house data processing for outdoor tracking, and daily activities monitoring and prediction without any third-party hardware or software. The system is implemented at a small scale with one miner, two user nodes and several device nodes, as a proof of concept; the technical feasibility is discussed along with the limitations of the system. Further research will cover the integration of the system into a smart-home environment with additional sensors and multiple users, and ethical implementations of tracking, especially of vulnerable people, via the immutability of blockchain.

Artificial Intelligence is revolutionising our communication practices and the ways in which we interact with each other. This revolution does not only impact how we communicate, but it affects the nature of the partners with whom we communicate. Online discussion platforms now allow humans to communicate with artificial agents in the form of socialbots. Such agents have the potential to moderate online discussions and even manipulate and alter public opinions. In this paper, we propose to study this phenomenon using a constructed large-scale agent platform. At the heart of the platform lies an artificial agent that can moderate online discussions using argumentative messages. We investigate the influence of the agent on the evolution of an online debate involving human participants. The agent will dynamically react to their messages by moderating, supporting, or attacking their stances. We conducted two experiments to evaluate the platform while looking at the effects of the conversational agent. The first experiment is a large-scale discussion with 1076 citizens from Afghanistan discussing urban policy-making in the city of Kabul. The goal of the experiment was to increase the citizen involvement in implementing Sustainable Development Goals. The second experiment is a small-scale debate between a group of 16 students about globalisation and taxation in Myanmar. In the first experiment, we found that the agent improved the responsiveness of the participants and increased the number of identified ideas and issues. In the second experiment, we found that the agent polarised the debate by reinforcing the initial stances of the participant.

Service composition is an important and effective technique that enables atomic services to be combined together to forma more powerful service, i.e., a composite service. With the pervasiveness of the Internet and the proliferation of interconnected computing devices, it is essential that service composition embraces an adaptive service provisioning perspective. Reinforcement learning has emerged as a powerful tool to compose and adapt Web services in open and dynamic environments. However, the most common applications of reinforcement learning algorithms are relatively inefficient in their use of the interaction experience data, whichmay affect the stability of the learning process when deployed to cloud environments. In particular, they make just one learning update for each interaction experience. This paper introduces a novel approach that aims to achieve greater data efficiency by saving the experience data and using it in aggregate to make updates to the learned policy. The proposed approach devises an offline learning scheme for cloud service composition where the online learning task is transformed into a series of supervised learning tasks. A set of algorithms is proposed under this scheme in order to facilitate and empower efficient service composition in the cloud under various policies and different scenarios. The results of our experiments show the effectiveness of the proposed approach for composing and adapting cloud services, especially under dynamic environment settings, compared to their online learning counterparts.

Recommendation systems have been extensively studied over the last decade in various domains. It has been considered a powerful tool for assisting business owners in promoting sales and helping users with decision-making when given numerous choices. In this paper, we propose a novel Graph-based Context-Aware Recommendation Systems with Knowledge Graph to analyse and predict users’ behaviours, i.e., making recommendations based on historical events and their implicit associations. The model incorporates contextual information extracted from both users’ historical behaviours and events relations, where the contexts have been modelled as knowledge graphs. By leveraging the advantages offered from the knowledge graph, events dependencies and their subtle relations can be established and have been introduced in the recommendation process. Experimental results indicate that the proposed approach can outperform the state-of-the-art algorithms and achieve more accurate recommendations.

The purpose of emergency medical systems (EMS) is to save lives and reduce injuries with a quick response in emergencies. The performance of these systems is highly dependent on the locations of ambulances and the policy for dispatching them to the customers (i.e., patients). In this study, two new mathematical models are presented to combine the decisions about the location and dispatching policy by integrating the location and hypercube queuing models. In the presented models, the flow-balance equations of the hypercube queuing model are considered as the constraints of the location model. In the first model, the status of each server is idle or busy at any moment, as in the classic hypercube queuing model. In the second model, the travel time is considered independent of the on-scene time, and the status of each server is idle, busy, and traveling, or busy and serving a customer on the incident site. To verify the models, some small-sized examples are first solved exactly. Then, an optimization framework based on the genetic algorithm is presented due to the complexity of the models for solving larger-sized examples. Two approaches (i.e., the exact and simulation-optimization) are used to extend the framework. The results demonstrate that the proposed optimization framework can obtain proper solutions compared to those of the exact method. Finally, several performance measures are considered that can only be calculated using the second model.