Hybrid systems as the next generation of intelligent applications within the field of mass appraisal and valuation are investigated. Motivated by the obvious limitations of paradigms that are being used in isolation or as stand-alone techniques such as multiple regression analysis, artificial neural networks and expert systems. Clearly, there are distinct advantages in integrating two or more information processing systems that would address some of the discrete problems of individual techniques. Examines first, the strategic development of mass appraisal approaches which have traditionally been based on``stand-alone'' techniques; second, the potential application of an intelligent hybrid system. Highlights possible solutions by investigating various hybrid systems that may be developed incorporating a nearest neighbour algorithm (k-NN). The enhancements are aimed at two major deficiencies in traditional distance metrics; user dependence for attribute weights and biases in the distance metric towards matching categorical variables in the retrieval of neighbours. Solutions include statistical techniques: mean, coefficient of variation and significant mean. Data mining paradigms based on a loosely coupled neural network or alternatively a tight coupling with genetic algorithms are used to discover attribute weights. The hybrid architectures developed are applied to a property data set and their performance measured based on their predictive value as well as perspicuity. Concludes by considering the application and the relevance of these techniques within the field of computer assisted mass appraisal.
In complex open Multi-Agent Systems (MAS), where there is no centralised control and individuals have equal authority, ensuring cooperative and coordinated behaviour is challenging. Norms and conventions are useful means of supporting cooperation in an emergent decentralised manner, however it takes time for effective norms and conventions to emerge. Identifying influential individuals enables the targeted seeding of desirable norms and conventions, which can reduce the establishment time and increase efficacy. Existing research is limited with respect to considering (i) how to identify influential agents, (ii) the extent to which network location imbues influence on an agent, and (iii) the extent to which different network structures affect influence.In this paper, we propose a methodology for learning a model for predicting the network value of an agent, in terms of the extent to which it can influence the rest of the population. Applying our methodology, we show that exploiting knowledge of the network structure can significantly increase the ability of individuals to influence which convention emerges. We evaluate our methodology in the context of two agent-interaction models, namely, the language coordination domain used by Salazar et al. [40] and a coordination game of the form used by Sen and Airiau [42] with heterogeneous agent learning mechanisms, and on a variety of synthetic and real-world networks. We further show that (i) the models resulting from our methodology are effective in predicting influential network locations, (ii) there are very few locations that can be classified as influential in typical networks, (iii) four single metrics are robustly indicative of influence across a range of network structures, and (iv) our methodology learns which single metric or combined measure is the best predictor of influence in a given network.
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