Nathan Fortier

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This paper is part of an ongoing effort to facilitate wider acceptance and further development of the IEEE Std 1232-2010 Standard for Artificial Intelligence Exchange and Service Tie to All Test Environments (AI-ESTATE). To that end, we describe a tool named SAPPHIRETM, which includes an implementation of AI-ESTATE in Java and a corresponding GUI tool that(More)
A novel swarm-based algorithm is proposed for the training of artificial neural networks. Training of such networks is a difficult problem that requires an effective search algorithm to find optimal weight values. While gradient-based methods, such as backpropagation, are frequently used to train multilayer feedforward neural networks, such methods may not(More)
In this paper we propose several approximation algorithms for the problems of full and partial abductive inference in Bayesian belief networks. Full abductive inference is the problem of finding the k most probable state assignments to all non-evidence variables in the network while partial abductive inference is the problem of finding the k most probable(More)
Abductive inference in Bayesian networks, is the problem of finding the most likely joint assignment to all non-evidence variables in the network. Such an assignment is called the most probable explanation (MPE). A novel swarm-based algorithm is proposed that finds the k-MPE of a Bayesian network. Our approach is an overlapping swarm intelligence algorithm(More)
Bayesian networks are powerful probabilistic models that have been applied to a variety of tasks. When applied to classification problems, Bayesian networks have shown competitive performance when compared to other state-of-the-art classifiers. However, structure learning of Bayesian networks has been shown to be NP-Hard. In this paper, we propose a novel(More)
Bayesian networks are probabilistic graphical models that have proven to be able to handle uncertainty in many real-world applications. One key issue in learning Bayesian networks is parameter estimation, i.e., learning the local conditional distributions of each variable in the model. While parameter estimation can be performed efficiently when complete(More)
Factored Evolutionary Algorithms (FEA) are a new class of evolutionary search-based optimization algorithms that have successfully been applied to various problems, such as training neural networks and performing abductive inference in graphical models. An FEA is unique in that it factors the objective function by creating overlapping subpopulations that(More)
A program using rules of music theory was written to create original compositions. Following the rules of music theory guarantees harmonious compositions, but certain aspects of musical composition cannot be defined by music theory. It is in these aspects of musical composition where the human mind uses creativity. The current research utilizes a genetic(More)
Energy consumption and coverage are common design issues in Wireless Sensor Networks (WSNs). For that reason, it is vital to consider network coverage and energy consumption in the design of WSN layouts. Because selecting the optimal geographical positions of the nodes is usually a very complicated task, we propose a novel heuristic search technique to(More)