There is a longstanding controversy on the site of the cerebellar motor learning. Different theories and experimental results suggest that either the cerebellar flocculus or the brainstem learns the task and stores the memory. With a dynamical system approach, we clarify the mechanism of transferring the memory generated in the flocculus to the brainstem and that of so-called savings phenomena. The brainstem learning must comply with a sort of Hebbian rule depending on Purkinje-cell activities. In contrast to earlier numerical models, our model is simple but it accommodates explanationsand predictions of experimental situations as qualitative features of trajectories in the phase space of synaptic weights, without fine parameter tuning.

Diffusion Tensor Magnetic Resonance Imaging (DT-MRI) is a non invasive methodfor brain neuronal fibers delineation. Here we show a modification forDT-MRI that allows delineation of neuronal fibers which are infiltrated by edema. We use the Muliple Tensor Variational (MTV) framework which replaces the diffusion model of DT-MRI with a multiple componentmodel and fits it to the signal attenuation with a variational regularizationmechanism. In order to reduce free water contamination weestimate the free water compartment volume fraction in each voxel, remove it, and then calculate the anisotropy of the remaining compartment.

Recurrent networks that perform a winner-take-all computation have been studied extensively. Although some of these studies include spiking networks,they consider only analog input rates. We present results of this winner-take-all computation on a network of integrate-and-fire neurons which receives spike trains as inputs. We show how we can configure theconnectivity in the network so that the winner is selected after a predetermined number of input spikes. We discuss spiking inputs with both regular frequencies and Poisson-distributed rates. The robustness of the computation was tested by implementing the winner-take-all network on an analog VLSI array of 64 integrate-and-fire neurons which have an innate variance in their operating parameters.

In real-life temporal scenarios, uncertainty and preferences are often essential and coexisting aspects. We present a formalism where quantitative temporal constraints with both preferences and uncertainty can be defined. We show how three classical notions of controllability (that is, strong, weak, and dynamic), which have been developed for uncertain temporal problems, can be generalized to handle preferences as well. After defining this general framework, we focus on problems where preferences follow the fuzzy approach, and with properties that assure tractability. For such problems, we propose algorithms to check the presence of the controllability properties. In particular, we show that in such a setting dealing simultaneously with preferences and uncertainty does not increase the complexity of controllability testing. We also develop a dynamic execution algorithm, of polynomial complexity, that produces temporal plans under uncertainty that are optimal with respect to fuzzy preferences.

It was recently proved that a sound and complete qualitative simulator does not exist, that is, as long as the input-output vocabulary of the state-of-the-art QSIM algorithm is used, there will always be input models which cause any simulator with a coverage guarantee to make spurious predictions in its output. In this paper, we examine whether a meaningfully expressive restriction of this vocabulary is possible so that one can build a simulator with both the soundness and completeness properties. We prove several negative results: All sound qualitative simulators, employing subsets of the QSIM representation which retain the operating region transition feature, and support at least the addition and constancy constraints, are shown to be inherently incomplete. Even when the simulations are restricted to run in a single operating region, a constraint vocabulary containing just the addition, constancy, derivative, and multiplication relations makes the construction of sound and complete qualitative simulators impossible.

Allocating scarce resources among agents to maximize global utility is, in general, computationally challenging. We focus on problems where resources enable agents to execute actions in stochastic environments, modeled as Markov decision processes (MDPs), such that the value of a resource bundle is defined as the expected value of the optimal MDP policy realizable given these resources. We present an algorithm that simultaneously solves the resource-allocation and the policy-optimization problems. This allows us to avoid explicitly representing utilities over exponentially many resource bundles, leading to drastic (often exponential) reductions in computational complexity. We then use this algorithm in the context of self-interested agents to design a combinatorial auction for allocating resources. We empirically demonstrate the effectiveness of our approach by showing that it can, in minutes, optimally solve problems for which a straightforward combinatorial resource-allocation technique would require the agents to enumerate up to 2^100 resource bundles and the auctioneer to solve an NP-complete problem with an input of that size.

We consider interactive tools that help users search for their most preferred item in a large collection of options. In particular, we examine example-critiquing, a technique for enabling users to incrementally construct preference models by critiquing example options that are presented to them. We present novel techniques for improving the example-critiquing technology by adding suggestions to its displayed options. Such suggestions are calculated based on an analysis of users' current preference model and their potential hidden preferences. We evaluate the performance of our model-based suggestion techniques with both synthetic and real users. Results show that such suggestions are highly attractive to users and can stimulate them to express more preferences to improve the chance of identifying their most preferred item by up to 78%.

Imagine an Internet-scale knowledge system where people and intelligent agents can collaborate on solving complex problems in business, engineering, science, medicine, and other endeavors. Its resources include semantically tagged websites, wikis, and blogs, as well as social networks, vertical search engines, and a vast array of web services from business processes to AI planners and domain models. Research prototypes of decentralized knowledge systems have been demonstrated for years, but now, thanks to the web and Moore's law, they appear ready for prime time. This article introduces the architectural concepts for incrementally growing an Internet-scale knowledge system and illustrates them with scenarios drawn from e-commerce, e-science, and e-life.

The celebration then moved to web and integrated intelligence, as on Artificial Intelligence and Boston where a huge turnout of AAAI well as the nectar and senior member the Nineteenth Innovative Applications fellows--from founding luminaries to papers, is a significant factor in this of Artificial Intelligence Conference 2006 fellow inductees--reported a trend." Senior member papers are a commemorated fifty years of great weekend meeting prior to the way to collect reflections about areas artificial intelligence research in AAAI conference full of discussions of work by leaders in the field.

In this paper, we show that there is a close relation between consistency in a constraint network and set intersection. A proof schema is provided as a generic way to obtain consistency properties from properties on set intersection. This approach not only simplifies the understanding of and unifies many existing consistency results, but also directs the study of consistency to that of set intersection properties in many situations, as demonstrated by the results on the convexity and tightness of constraints in this paper. Specifically, we identify a new class of tree convex constraints where local consistency ensures global consistency. This generalizes row convex constraints. Various consistency results are also obtained on constraint networks where only some, in contrast to all in the existing work,constraints are tight.