While detection of rumours in online social networks has been intensively studied in the literature, mitigation of the spread of rumours has only recently gained attention and remains a challenging task. Some studies developed user opinion models to find top influential users as debunkers to spread the truth to counter rumour spread. Other studies designed an intervention framework to optimize the mitigation activities for given debunkers. The issue of optimizing the selection of debunkers in a dynamic environment where users’ beliefs and behaviour change remains under investigated. This paper addresses this issue by proposing a rumour mitigation approach based on the deep reinforcement learning framework. In particular, we model the changes in users’ beliefs with an epidemic model. We further employ deep reinforcement learning to train an agent to learn a multi-stage policy for selecting the optimal debunkers to inject truthful information under a budget constraint. Our model selects debunkers to inject truthful information at multiple stages with an overall objective to maximize the number of users who will believe in the true information (a.k.a number of recovered nodes), such that the spread of rumours is minimized. Our experiments on synthetic and real-world social networks show that our proposed method for rumour mitigation can effectively minimize the spread of rumours.

This work proposes a Bayesian optimisation with Gaussian Process approach to learn decision maker (DM) preferences in the attribute search space of a multi-objective optimisation problem (MOP). The DM is consulted periodically during optimisation of the problem and asked to provide their preference over a series of pairwise comparisons of candidate solutions. After each consultation, the most preferred solution is used as the reference point in an appropriate multiobjective optimisation evolutionary algorithm (MOEA). The rationale for using Bayesian optimisation is to identify the most preferred location in the decision search space with the least number of DM queries, thereby minimising DM cognitive burden and fatigue. This enables non-expert DMs to be involved in the optimisation process and make more informed decisions. We further reduce the number of preference queries required, by progressively redefining the Bayesian search space to reflect the MOEA's decision bounds as it converges toward the Pareto Front. We demonstrate how this approach can locate a reference point close to an unknown preferred location on the Pareto Front, of both benchmark and real-world problems with relatively few pairwise comparisons.

Understanding the spread of rumors on online social networks (OSNs) is crucial for designing strategies to detect and mitigate rumor propagation. Previous studies analysing rumor propagation have focused on summarising the static measurements of propagation-based information cascades. But static features are unable to capture the dynamic nature of information propagation across time. In this paper, we employed two generative models, Multivariate Hawkes process (MHP) and marked Hawkes process (marked HP) to model user influence and the dynamics of rumor propagation. Using the MHP model, we were able to derive a novel measurement of user influence in information propagation, namely, the influence rate. We then employed the marked HP model and considered various mark measurements including the proposed influence rate to provide new insights into differentiating between rumor and non-rumor propagation, and among different types of rumor propagation. Our analysis on Twitter rumor datasets clearly showed that users play different roles (i.e., possess different influence rates) across different categories of source tweets. Moreover, different categories of source tweets have different patterns of diffusion. In particular, rumor cascades typically attracted more influential users at the early stage of cascades, and they are more likely to generate more retweets than non-rumor cascades. Among different types of rumors, false rumors diffused faster than true rumors.

In this paper, we study a large-scale heterogeneous task allocation problem in complex systems. Existing work on task allocation mainly tackles this well-known NP-hard problem from an optimisation perspective, where an exact or approximate solutions can be found after intensive computation. They have not been able to cater for the extra needs of scalability and robustness in large scale complex systems. In this work, we employ a game-theoretic framework to model the studied task allocation problem, and align the objective in task allocation (i.e., system optimality) with the concept of Nash equilibrium in game theory. Our formulation enables the expression of heterogeneity in both agents and tasks, and allows multiple agents to form teams or coalitions to cooperatively perform a task. Based on this formulation, we propose a novel GreedyNE algorithm to efficiently search for a Nash equilibrium solution. The proposed GreedyNE algorithm is a scalable, anytime, and monotonic algorithm, which in turn, makes it robust for the deployment in complex systems. GreedyNE is simple, easy to implement and flexible enough, so that it can also be used as a local search algorithm for improving the quality of any existing allocation solution. By conducting comprehensive experiments, we show that GreedyNE achieves a solution quality as good as the state-of-the-art approximation algorithms, yet with significantly lower computation time.

This paper explores the heterogeneous task allocation problem in Multi-robot systems. A game-theoretic formulation of the problem is proposed to align the goal of individual robots with the system objective. The concept of Nash equilibrium is applied to define a desired solution for the task allocation problem in which each robot can allocate itself to an appropriate task group. We also introduce a market-based distributed mechanism, called DisNE, to allow the robots to exchange messages with tasks and move between task groups, eventually reaching an equilibrium solution. We carry out comprehensive empirical studies to demonstrate that DisNE achieves near-optimal system utility in significantly shorter computation times when compared with the state-of-the-art mechanisms.

Very often, we have to look into multiple agents' preferences, and compare or aggregate them. In this paper, we consider the well-known model, namely, lexicographic preference trees (LP-trees), for representing agents' preferences in combinatorial domains. We tackle the problem of calculating the dissimilarity/distance between agents' LP-trees. We propose an algorithm LpDis to compute the number of disagreed pairwise preferences between agents by traversing their LP-trees. The proposed algorithm is computationally efficient and allows agents to have different attribute importance structures and preference dependencies.

Automated negotiation is one of the most common approaches used to make decisions and manage disputes between computational entities leading them to optimal agreements. Many existing works tackle single-issue negotiations and the negotiation environment is assumed to be static so that the agents can make decisions based solely on the proposals of the counterparts and their own fixed parameters. Most real-world scenarios, however, involve complex domains and dynamic environments. In such cases, it is no longer sufficient to consider negotiation as an isolated activity in a static environment. Therefore, a more general framework for automated negotiation is needed in which the negotiation agents can be very flexible and adaptive. In this paper, we describe a generic framework for automated negotiation, which captures descriptively the social dynamics of the negotiation process. The proposed framework enables the agents to behave responsively to the changes in the environment. Their strategies can adapt as the conditions outside of the negotiation change to ensure that their decisions remain rational. And the agents are proactive and responsive by searching for options, which are outside of the negotiation and which may improve their outcomes. The key ideas and the overall system architecture together with a specific negotiation instance in a basic bilateral setting are described, along with two illustrative examples. The first example is in the context of e-commerce, and the second example is an application scenario of service level agreement negotiation in service computing. We also describe a prototypical implementation of the proposed negotiation framework.