One current explanation of the view independent representation of space by the place-cells of the hippocampus is that they arise out of the summation of view dependent Gaussians. This proposal assumes thatvisual representations show bounded invariance. Here we investigate whether a recently proposed visual encoding scheme called the temporal population code can provide such representations. Ouranalysis is based on the behavior of a simulated robot in a virtual environment containing specific visual cues. Our results showthat the temporal population code provides a representational substratethat can naturally account for the formation of place fields.

When we learn a new motor skill, we have to contend with both the variability inherentin our sensors and the task. The sensory uncertainty can be reduced by using information about the distribution of previously experienced tasks.Here we impose a distribution on a novel sensorimotor task and manipulate the variability of the sensory feedback. We show that subjects internally represent both the distribution of the task as well as their sensory uncertainty. Moreover, they combine these two sources of information in a way that is qualitatively predicted by optimal Bayesian processing. We further analyze if the subjects can represent multimodal distributions such as mixtures of Gaussians. The results show that the CNS employs probabilistic models during sensorimotor learning even when the priors are multimodal.

In real world planning problems, time for deliberation is often limited. Anytime planners are well suited for these problems: they find a feasible solutionquickly and then continually work on improving it until time runs out. In this paper we propose an anytime heuristic search, ARA*, which tunes its performance bound based on available search time. It starts by finding a suboptimal solution quickly using a loose bound, then tightens the bound progressively as time allows. Given enough time it finds a provably optimal solution. While improving its bound, ARA* reuses previous search efforts and, as a result, is significantly more efficient thanother anytime search methods. In addition to our theoretical analysis, we demonstrate the practical utility of ARA* with experiments on a simulated robot kinematic arm and a dynamic path planning problem foran outdoor rover.

We present and empirically test a novel approach for categorizing 3-D free form object shapesrepresented by range data . In contrast to traditional surface-signature based systems that use alignment to match specific objects, we adapted the newly introduced symbolic-signature representation to classify deformable shapes [10]. Our approach constructs anabstract description of shape classes using an ensemble of classifiers that learn object class parts and their corresponding geometrical relationships from a set of numeric and symbolic descriptors. We used our classification engine in a series of large scale discrimination experimentson two well-defined classes that share many common distinctive features. The experimental results suggest that our method outperforms traditional numeric signature-based methodologies.

A novel algorithm for actively trading stocks is presented. While traditional universalalgorithms (and technical trading heuristics) attempt to predict winners or trends, our approach relies on predictable statistical relations between all pairs of stocks in the market. Our empirical results on historical markets provide strong evidence that this type of technical tradingcan "beat the market" and moreover, can beat the best stock in the market. In doing so we utilize a new idea for smoothing critical parameters in the context of expert learning.

We describe a pattern acquisition algorithm that learns, in an unsupervised fashion,a streamlined representation of linguistic structures from a plain natural-language corpus. This paper addresses the issues of learning structuredknowledge from a large-scale natural language data set, and of generalization to unseen text. The implemented algorithm represents sentencesas paths on a graph whose vertices are words (or parts of words). Significant patterns, determined by recursive context-sensitive statistical inference, form new vertices. Linguistic constructions are represented bytrees composed of significant patterns and their associated equivalence classes. An input module allows the algorithm to be subjected toa standard test of English as a Second Language (ESL) proficiency. Theresults are encouraging: the model attains a level of performance consideredto be "intermediate" for 9th-grade students, despite having been trained on a corpus (CHILDES) containing transcribed speech of parents directed to small children.

Learning in a single-agent stationary-enVironment setting can be a hard problem, but relative to the rnulti-agent learning problem, it is easy.

We formulate linear dimensionality reduction as a semi-parametric estimation problem,enabling us to study its asymptotic behavior. We generalize the problem beyond additive Gaussian noise to (unknown) non-Gaussian additive noise, and to unbiased non-additive models.

Given a sentence, learning is first applied at word level to identify phrase candidates 0f the solution.

We will exemplify our reasoning using data from an experiment described in Sect.