17205588
Neurocomputational Mechanisms for Generalization During the Sequential Learning of Multiple Tasks
Traditional artificial neural network models of learning suffer from catastrophic interference. They are commonly trained to perform only one specific task, and when trained on a new task, they forget the original task completely. It has been shown that the foundational neurocomputational principles embodied by the Leabra cognitive modeling framework are sufficient to overcome this limitation significantly. Leabra is consistent with known biological properties of the neural networks of the brain. In particular, this framework includes both fast lateral inhibition and a local synaptic plasticity model that incorporates both correlational and error-based dynamics. It has been shown that the use of sparse internal representation significantly reduces the problem of catastrophic interference during sequential learning of multiple motor skills. We have also provided prelimilary evidence that Leabra is able to generalize the sub-sequences of motor skills, when doing so is appropriate. In this paper, we provide a detailed analysis of the extent of generalization possible with Leabra during sequential learning of multiple tasks. For comparison, we measure the generalization shown by a backpropagation of error learning algorithm based artificial neural network.
8765967
A Dual-Pathway Neural Network Model of Control Relinquishment in Motor Skill Learning
Cognitive psychologists have long recognized that the acquisition of a motor skill involves a transition from attention-demanding controlled processing to more fluent automatic processing. Neuroscientific studies suggest that controlled and automatic processing rely on two largely distinct neural pathways. The controlled pathway, which includes the prefrontal cortex, is seen as acquiring declarative representations of skills. In comparison, the automatic pathway is thought to develop procedural representations. Automaticity in motor skill learning involves a reduction in dependence on frontal systems and an increased reliance on the automatic pathway. In this paper, we propose a biologically plausible computational model of motor skill automaticity. This model offers a dual-pathway neurocomputational account of the translation of declarative knowledge into procedural knowledge during motor learning. In support of the model, we review some previously reported human experimental results involving the learning of a sequential key pressing task, and we demonstrate, through simulation, howthe model provides a parsimonious explanation for these results.
32208794
Adaptive delivery of E-commerce web sites
Most e-commerce web sites are very large, often confusing and overwhelm the visitor with a huge amount of information. People cannot easily find what they are looking for. Moreover, the web site is presented in the same format to every visitor, irrespective of his needs. This paper proposes a model to solve the above problems. Our model divides the visitors into groups. Then it arranges web pages in the decreasing order of preference for each group and applies path prediction to find sink pages for that group. The results of these algorithms are displayed in a separate frame without modifying the site. Secondly, our paper addresses the need to guide visitors during the configuration of a product. We propose to apply data mining on the quotes for every group and find associations between different components of the product. We can then display these results as suggestions while the prospective buyer is configuring the product. These suggestions will dynamically change as each selection is made. We have discussed the data mining algorithms applicable to our model.
8920735
Partition-based decision heuristics for image computation using SAT and BDDs
Methods based on Boolean satisfiability (SAT) typically use a conjunctive normal form (CNF) representation of the Boolean formula, and exploit the structure of the given problem through use of various decision heuristics and implication methods. We propose a new decision heuristic based on separator-set induced partitioning of the underlying CNF graph. It targets those variables whose choice generates clause partitions with disjoint variable supports. This can potentially improve performance of SAT applications by decomposing the problem dynamically within the search. In the context of a recently proposed image computation method combining SAT and BDDs, this results in simpler BDD subproblems. We provide algorithms for CNF partitioning - one based on a clause-variable dependency matrix, and another based on standard hypergraph partitioning techniques, and also for the use of partitioning information in decision heuristics for SAT. The effectiveness of the proposed partition-based heuristic is shown with practical results for reachability analysis of benchmark sequential circuits.
6254649
Disjunctive Image Computation for Embedded Software Verification
Finite state models generated from software programs have unique characteristics that are not exploited by existing model checking algorithms. In this paper, we propose a novel disjunctive image computation algorithm and other simplifications based on these characteristics. Our algorithm divides an image computation into a disjunctive set of easier ones that can be performed in isolation. Hypergraph partitioning is used to minimize the number of live variables in each disjunctive component. We use the live variables to simplify transition relations and reachable state subsets. Our experiments on a set of real-world C programs show that the new algorithm achieves orders-of-magnitude performance improvement over the best known conjunctive image computation algorithm
206873246
Fast error diagnosis for combinational verification
We address the problem of localizing error sites in a combinational circuit that has been shown to be inequivalent to its specification. In the typical case, it is not possible to identify the error location exactly. We propose a novel diagnosis strategy of gradually increasing the level of detail in the analysis algorithm to ultimately derive a small list of potential error sites in a short time. Our techniques combine the use of simulation, BDDs, and SAT in a novel way to achieve the goal. A limitation of many previous approaches has been that they have been constrained to a specific error model. No such assumption is made in our work. We show through experimental results that these techniques are successful in that the final set of error sites derived is small, contains the actual error sites and is derived in a reasonable amount of time.
3246043
Parallel organization and performance of an information system
This paper investigates a directory structure, based on an M-B-T directory, that provides for parallel retrieval of information with low communication overhead. An information system of documents is assumed. It is compatible with current automatic retrieval and query processing techniques. It is also compatible with any organization of the documents. Weights that are assigned to keywords can be included in the directory with the keywords at no additional cost. In addition, it provides for indexing a secondary keyword within a primary keyword with no additional cost. Given a p-processor massively parallel computer, our organization allows a maximum of p retrievals to be performed concurrently and thus achieves an approximate speed up of p in the best case. The new, revised organization improves on previous work. An eecient parallel searching algorithm and its performance is also discussed.
11105634
Model-based mediation with domain maps
Proposes an extension to current view-based mediator systems called "model-based mediation", in which views are defined and executed at the level of conceptual models (CMs) rather than at the structural level. Structural integration and lifting of data to the conceptual level is "pushed down" from the mediator to wrappers which, in our system, export the classes, associations, constraints and query capabilities of a source. Another novel feature of our architecture is the use of domain maps - semantic nets of concepts and relationships that are used to mediate across sources from multiple worlds (i.e. whose data are related in indirect and often complex ways). As part of registering a source's CM with the mediator, the wrapper creates a "semantic index" of its data into the domain map. We show that these indexes not only semantically correlate the multiple-worlds data, and thereby support the definition of the integrated CM, but they are also useful during query processing, for example, to select relevant sources. A first prototype of the system has been implemented for a complex neuroscience mediation problem.