Reinforcement learning of normative monitoring intensities

Choosing actions within norm-regulated environments involves balancing achieving one’s goals and coping with any penalties for non-compliant behaviour. This choice becomes more complicated in environments where there is uncertainty. In this paper, we address the question of choosing actions in environments where there is uncertainty regarding both the outcomes of agent actions and the intensity of monitoring for norm violations. Our technique assumes no prior knowledge of probabilities over action outcomes or the likelihood of norm violations being detected by employing reinforcement learning to discover both the dynamics of the environment and the effectiveness of the enforcer. Results indicate agents become aware of greater rewards for violations when enforcement is lax, which gradually become less attractive as the enforcement is increased

Destabilising conventions using temporary interventions

Conventions are an important concept in multi-agent systems as they allow increased coordination amongst agents and hence a more efficient system. Encouraging and directing convention emergence has been the focus of much research, particularly through the use of fixed strategy agents. In this paper we apply temporary interventions using fixed strategy agents to destabilise an established convention by (i) replacing it with another convention of our choosing, and (ii) allowing it to destabilise in such a way that no other convention explicitly replaces it. We show that these interventions are effective and investigate the minimum level of intervention needed

Emergence of scale-free leadership structure in social recommender systems

The study of the organization of social networks is important for understanding of opinion formation, rumor spreading, and the emergence of trends and fashion. This paper reports empirical analysis of networks extracted from four leading sites with social functionality (Delicious, Flickr, Twitter and YouTube) and shows that they all display a scale-free leadership structure. To reproduce this feature, we propose an adaptive network model driven by social recommending. Artificial agent-based simulations of this model highlight a "good get richer" mechanism where users with broad interests and good judgments are likely to become popular leaders for the others. Simulations also indicate that the studied social recommendation mechanism can gradually improve the user experience by adapting to tastes of its users. Finally we outline implications for real online resource-sharing systems

Journal of youth and theology : furthering the study, research and teaching of youth ministry internationally

Extracting norms from computer-mediated human interactions is gaining popularity since huge volume of data is available from which norms can be extracted. Open source communities offer exciting new application opportunities for extracting norms since such communities involve developers from different geographical regions, background and cultures. Investigating the types of norms that exist in open source projects and their efficacy (i.e. the usage of norms) in enabling smoother functioning however has not received much attention from the normative multi-agent systems (NorMAS) community. This paper makes two contributions in this regard. First, it presents norm compliance results from a case study involving three open source Java projects. Second, it presents an architecture for mining norms from open source projects. It also discusses the opportunities presented by the domain of software repositories for the study of norms. In particular, it points towards how norms can be mined by leveraging and extending prior work in the areas of Normative Multi-Agent Systems (NorMAS) and mining software repositories. 2014 Springer-Verlag

The Social Construction of “Shared Reality” in Socio-Technical Systems

International audienceAs the size, complexity and ubiquity of socio-technical systems increases, there is a concomitant expectation that humans will have to establish and maintain long-lasting ‘relationships’ with many types of digital artefact: for example with humanoid robots, driverless cars or software agents running on ‘smart’ devices. Rather than being limited to one-off interactions, these relationships will continue over longer time frames, correspondingly increasing the likelihood of errors occurring from numerous causes. When digital errors occur, often complete human mistrust and distrust is the outcome. The situation is exacerbated when the computer can make no act of reparation and no avenue of forgiveness is open to the human. In the pursuit of designing long-lasting socio-technical systems that are fit-for purpose, this position paper reviews past work in relevant social concepts and, based on the sociological theory of social constructivism, proposes a new approach to the joint human-computer construction of a “shared reality”

Manipulating conventions in a particle-based topology

Coordination is essential to the effective operation of multi-agent systems. Convention emergence offers a low-cost and decentralised method of ensuring compatible actions and behaviour, without requiring the imposition of global rules. This is of particular importance in environments with no centralised control or where agents belong to different, possibly conflicting, parties. The timely emergence of robust conventions can be facilitated and manipulated via the use of fixed strategy agents, who attempt to influence others into adopting a particular strategy. Although fixed strategy agents have previously been investigated, they have not been considered in dynamic networks. In this paper, we explore the emergence of conventions within a dynamic network, and examine the effectiveness of fixed strategy agents in this context. Using established placement heuristics we show how such agents can encourage convention emergence, and we examine the impact of the dynamic nature of the network. We introduce a new heuristic, Life-Degree, to enable this investigation. Finally, we consider the ability of fixed strategy agents to manipulate already established conventions, and investigate the effectiveness of placement heuristics in this domain