An unsupervised learning system, implemented as an autonomous agent is presented. A simulation of a challenging path planning problem is used to illustrate the agent design and demonstrate its problem solving ability. The agent, dubbed the ORG, employs fuzzy logic and clustering techniques to efficiently represent and retrieve knowledge and uses innovative sensor modeling and attention focus to process a large number of stimuli. Simple initial fuzzy rules (instincts) are used to influence behavior and communicate intent to the agent. Self-reflection is utilized so the agent can learn from its environmental constraints and modify its own state. Speculation is utilized in the simulated environment, to produce new rules and fine-tune performance and internal parameters. The ORG is released in a simulated shallow water environment where its mission is to dynamically and continuously plan a path to effectively cover a specified region in minimal time while simultaneously learning from its environment. Several paths of the agent design are shown, and desirable emergent behavior properties of the agent design are discussed.

Stochastic resonance is said to occur when just the right amount of noise enhances the performance of a process. For a simple threshold detector, the first moment of stochastic resonance is obtained by passing the signal through a transfer function equal to a transposed and shifted version of the underlying noise's probability distribution function. The process is readily evident in images wherein noise corresponding to a linear transfer function produces a better visual representation than when other noise is used.

Considers the problem of neural network supervised learning when the number of output nodes can vary for differing training data. The paper proposes irregular weight updates and learning rate adjustment to compensate for this variation. In order to compensate for possible over training, an a posteriori probability that shows how often the weights associated with each output neuron are updated is obtained from the training data set and is used to evenly distribute the opportunity for weight update to each output neuron. The weight space becomes smoother and the generalization performance is significantly improved.

The composite effort of the system team, rather, is significantly more important than a single player's individual performance. We consider the case wherein each player's performance is tuned to result in maximal team performance for the specific case of maximal area coverage (MAC). The approach is first illustrated through solution of MAC by a fixed number of deformable shapes. An application to sonar is then presented. Here, sonar control parameters determine a range-depth area of coverage. The coverage is also affected by known but uncontrollable environmental parameters. The problem is to determine K sets of sonar ping parameters that result in MAC. The forward problem of determining coverage given control and environmental parameters is computationally intensive. To facilitate real time cooperative optimization among a number of such systems, the sonar input-output is captured in a feedforward layered perceptron neural network.

We present a study of complex visualization usage by expert meteorological forecasters. We performed a protocol analysis and examined the types of visualizations they examined. We present evidence for how experts are able to make use of complex visualizations. Our findings suggest that users of complex visualizations create qualitative mental models from which they can then generate quantitative information. In order to build their qualitative mental models, forecasters integrated information across multiple visualizations and extracted primarily qualitative information from visualizations in a goal-directed manner. We discuss both theoretical and practical implications of this study.