StackOverflow2013

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copied!<p>It sounds like at this point you have just a classification problem. You want to map some knowledge about what you are currently walking on to one of a set of classes. Knowing the class of the terrain allows you to then invoke the proper subroutine. Is this correct?</p> <p>If so, then there are a wide array of classification engines that you can use including neural networks, Bayesian networks, decision trees, nearest neighbor, etc. In order to pick the best fit, we will need more information about your problem.</p> <p>First, what kind of input or sensory data do you have available to help you identify the behavior class you should invoke? Second, can you describe the circumstances in which you will be training this classifier and what the circumstances are during runtime when you deploy it, such as any limits on computational resources or requirements of robustness to noise?</p> <p>EDIT: Since you have a fixed number of classes, and you have some parameterized model for generating all possible terrains, I would consider using <a href="http://en.wikipedia.org/wiki/K-means_clustering" rel="nofollow noreferrer">k-means clustering</a>. The principle is as follows. You cluster a whole bunch of terrains into k different classes, where each cluster is associated with one of your specialized subroutines that performs best for that cluster of terrains. Then when a new terrain comes in, it will probably fall near one of these clusters. You then invoke the corresponding specialized subroutine to navigate that terrain.</p> <p>Do this offline: Generate enough random terrains to sufficiently sample the parameter space, map these terrains to your sensory space (but remember which points in sensory space correspond to which terrains), and then run k-means clustering on this sensory space corpus where k is the number of classes you want to learn. Your distance function between a class representative C and a point P in sensory space would be simply the fitness function of letting algorithm C navigate the terrain that generated P. You would then get a partitioning of your sensory space into k clusters, each cluster mapping to the best subroutine that you've got. Each cluster will have a representative point in sensory space.</p> <p>Now during runtime: You will get some unlabeled point in sensory space. Use a different distance function to find the closest representative point to this new incoming point. That tells you what class the terrain is.</p> <p>Note that the success of this method depends on the quality of the mapping from the parameter space of terrain generation to sensory space, from sensory space to your fitness functions, and the eventual distance function you use to compare points in sensory space.</p> <p>Note also that if you had enough memory, instead of only using the k representative sensory points to tell you which class an unlabeled sensory point belongs to, you might go through your training set and label all points with the learned class. Then during runtime you pick the nearest neighbor, and conclude that your unlabeled point in sensory space is in the same class as that neighbor.</p>

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