Norms in human societies are expectations of behaviours of the individuals. In human societies, there are several types of norms such as moral norms, social norms and legal norms (laws). In multi-agent systems, software agents are modelled as possess

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 recognition of classical instrument, 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.

Ontologies are becoming increasingly important because they provide the critical semantic foundation for many rapidly expanding technologies such as software agents, e-commerce and knowledge management (McGuinness, 2002). The Unified Modelling Language (UML)1 has been widely adopted by the software engineering community and its scope is broadening to include more diverse modelling tasks. This paper discusses the recent convergence of UML and ontologies and suggests some possible future directions.

Most works on norms have investigated how norms are regulated using institutional mechanisms. Very few works have focused on how an agent may infer the norms of a society without the norm being explicitly given to the agent. This paper describes a mechanism for identifying one type of norm, an obligation norm. The Obligation Norm Inference (ONI) algorithm described in this paper makes use of an association rule mining approach to identify obligation norms. Using agent based simulation of a virtual restaurant we demonstrate how an agent can identify the tipping norm. The experiments that we have conducted demonstrate that an agent in the system is able to add, remove and modify norms dynamically. An agent can also flexibly modify the parameters of the system based on whether it is successful in identifying a norm.