Norms are shared expectations of behaviours that exist in human societies. Norms help societies by increasing the predictability of individual behaviours and by improving co-operation and collaboration among members. Norms have been of interest to multi-agent system researchers as software agents intend to follow certain norms. But, owing to their autonomy, agents sometimes violate norms which needs monitoring. There are two main branches of research in normative agent systems. One of the branches focuses on normative agent architectures, norm representations, norm adherence and the associated punitive or incentive measures. The other branch focuses on two main issues. The first issue is on the study of spreading and internalization of norms. The second issue that has not received much attention is the emergence of norms in agent societies. Our objective in this paper is to propose mechanisms for norm emergence in artificial agent societies and provide initial experimental results.
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In this paper we propose a mechanism for norm emergence based on role models. The mechanism uses the concept of normative advice whereby the role models provide advice to the follower agents. Our mechanism is built using two layers of networks, the social link layer and the leadership layer. The social link network represents how agents are connected to each other. The leadership network represents the network that is formed based on the role played by each agent on the social link network. The two kinds of roles are leaders and followers. We present our findings on how norms emerge on the leadership network when the topology of the social link network changes. The three kinds of social link networks that we have experimented with are fully connected networks, random networks and scale-free networks.
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This paper proposes to design a mechanism that will allow Maori users to specify their privacy preferences related to their culture when a software system asks for culturally sensitive information. We first identify various concepts associated with sensitive aspects of Maori culture, such as tapu. We propose to build an ontology that describes these concepts and the relations between them in a formal way. This ontology will help service providers integrate Maori cultural protocols in order to make Maori users more confident about the use of the sensitive information related to their culture.
Keywords: privacy, Maori culturally sensitive information
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In tackling data mining and pattern recognition tasks, finding a compact but effective set of features has often been found to be a crucial step in the overall problem-solving process. In this paper we present an empirical study on feature analysis for classical instrument recognition, using machine learning techniques to select and evaluate features extracted from a number of different feature schemes. It is revealed that there is significant redundancy between and within feature schemes commonly used in practice. Our results suggest that further feature analysis research is necessary in order to optimize feature selection and achieve better results for the instrument recognition problem.
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