用改进的ART-2网络建立移动机器人环境模型的研究

本文应用自适应共振理论中ART-2神经网络进行移动机器人环境障碍模式识别。ART-2神经网络在处理单方向渐变的模式输入时具有模式漂移的特点，机器人在静态环境中运动依赖这种特点，但在动态环境中模式漂移的特点却会对机器人的安全造成威胁。为此，设计了一种改进的ART-2神经网络，使得移动机器人同时适应在静态和动态环境中安全运动。

Qualitative and Quantitative Knowledge in Classical Mechanics

This thesis investigates what knowledge is necessary to solve mechanics problems. A program NEWTON is described which understands and solves problems in mechanics mini-world of objects moving on surfaces. Facts and equations such as those given in mechanics text need to be represented. However, this is far from sufficient to solve problems. Human problem solvers rely on "common sense" and "qualitative" knowledge which the physics text tacitly assumes to be present. A mechanics problem solver must embody such knowledge. Quantitative knowledge given by equations and more qualitative common sense knowledge are the major research points exposited in this thesis. The major issue in solving problems is planning. Planning involves tentatively outlining a possible path to the solution without actually solving the problem. Such a plan needs to be constructed and debugged in the process of solving the problem. Envisionment, or qualitative simulation of the event, plays a central role in this planning process.

Tactile Sensing for Mechatronics: A State of the Art Survey

Lee M.H. and Nicholls H.R., Tactile Sensing for Mechatronics: A State of the Art Survey, Mechatronics, 9, Jan 1999, pp1-31.

ART: An ontology based tool for the translation of papers into Semantic Web format

To be presented at SIG/ISMB07 ontology workshop: http://bio-ontologies.org.uk/index.php To be published in BMC Bioinformatics. Sponsorship: JISC

Why Ideas Matter in International Relations : Hans Morgenthau, Classical Realism, and the Moral Construction of Power Politics

Williams, Mike, 'Why ideas matter in International Relations: Hans Morgenthau, Classical Realism, and the Moral Construction of Power Politics', International Organization (2004) 58(4) pp.633-665 RAE2008

From Art to Avant Garde? Television, Formalism and the Arts Documentary in 1960's Britain

Sexton, J. (2008). From Art to Avant Garde? Television, Formalism and the Arts Documentary in 1960's Britain. In L. Mulvey and J. Sexton (Eds.), Experimental British Television (pp.89-105). Manchester: Manchester University Press. RAE2008

Birkhoff normal forms in semi-classical inverse problems

Iantchenko, A.; Sj?strand, J.; Zworski, M., (2002) 'Birkhoff normal forms in semi-classical inverse problems', Mathematical Research Letters 9(3) pp.337-362 RAE2008

On Finding Sensitivity of Quantum and Classical Gates

We consider a fault model of Boolean gates, both classical and quantum, where some of the inputs may not be connected to the actual gate hardware. This model is somewhat similar to the stuck-at model which is a very popular model in testing Boolean circuits. We consider the problem of detecting such faults; the detection algorithm can query the faulty gate and its complexity is the number of such queries. This problem is related to determining the sensitivity of Boolean functions. We show how quantum parallelism can be used to detect such faults. Specifically, we show that a quantum algorithm can detect such faults more efficiently than a classical algorithm for a Parity gate and an AND gate. We give explicit constructions of quantum detector algorithms and show lower bounds for classical algorithms. We show that the model for detecting such faults is similar to algebraic decision trees and extend some known results from quantum query complexity to prove some of our results.

Bring ART into the ACT

ACT is compared with a particular type of connectionist model that cannot handle symbols and use non-biological operations that cannot learn in real time. This focus continues an unfortunate trend of straw man "debates" in cognitive science. Adaptive Resonance Theory, or ART, neural models of cognition can handle both symbols and sub-symbolic representations, and meets the Newell criteria at least as well as these models.

Fast Synchronization of Perpetual Grouping in Laminar Visual Cortical Circuits

Perceptual grouping is well-known to be a fundamental process during visual perception, notably grouping across scenic regions that do not receive contrastive visual inputs. Illusory contours are a classical example of such groupings. Recent psychophysical and neurophysiological evidence have shown that the grouping process can facilitate rapid synchronization of the cells that are bound together by a grouping, even when the grouping must be completed across regions that receive no contrastive inputs. Synchronous grouping can hereby bind together different object parts that may have become desynchronized due to a variety of factors, and can enhance the efficiency of cortical transmission. Neural models of perceptual grouping have clarified how such fast synchronization may occur by using bipole grouping cells, whose predicted properties have been supported by psychophysical, anatomical, and neurophysiological experiments. These models have not, however, incorporated some of the realistic constraints on which groupings in the brain are conditioned, notably the measured spatial extent of long-range interactions in layer 2/3 of a grouping network, and realistic synaptic and axonal signaling delays within and across cells in different cortical layers. This work addresses the question: Can long-range interactions that obey the bipole constraint achieve fast synchronization under realistic anatomical and neurophysiological constraints that initially desynchronize grouping signals? Can the cells that synchronize retain their analog sensitivity to changing input amplitudes? Can the grouping process complete and synchronize illusory contours across gaps in bottom-up inputs? Our simulations show that the answer to these questions is Yes.

Biased ART: A Neural Architecture that Shifts Attention Toward Previously Disregarded Features Following an Incorrect Prediction

Memories in Adaptive Resonance Theory (ART) networks are based on matched patterns that focus attention on those portions of bottom-up inputs that match active top-down expectations. While this learning strategy has proved successful for both brain models and applications, computational examples show that attention to early critical features may later distort memory representations during online fast learning. For supervised learning, biased ARTMAP (bARTMAP) solves the problem of over-emphasis on early critical features by directing attention away from previously attended features after the system makes a predictive error. Small-scale, hand-computed analog and binary examples illustrate key model dynamics. Twodimensional simulation examples demonstrate the evolution of bARTMAP memories as they are learned online. Benchmark simulations show that featural biasing also improves performance on large-scale examples. One example, which predicts movie genres and is based, in part, on the Netflix Prize database, was developed for this project. Both first principles and consistent performance improvements on all simulation studies suggest that featural biasing should be incorporated by default in all ARTMAP systems. Benchmark datasets and bARTMAP code are available from the CNS Technology Lab Website: http://techlab.bu.edu/bART/.

On the Relationship Between the Generalized Equality Classifier and ART 2 Neural Networks

In this paper, we introduce the Generalized Equality Classifier (GEC) for use as an unsupervised clustering algorithm in categorizing analog data. GEC is based on a formal definition of inexact equality originally developed for voting in fault tolerant software applications. GEC is defined using a metric space framework. The only parameter in GEC is a scalar threshold which defines the approximate equality of two patterns. Here, we compare the characteristics of GEC to the ART2-A algorithm (Carpenter, Grossberg, and Rosen, 1991). In particular, we show that GEC with the Hamming distance performs the same optimization as ART2. Moreover, GEC has lower computational requirements than AR12 on serial machines.

ART-EMAP: A Neural Network Architecture for Learning and Prediction by Evidence Accumulation

This paper introduces ART-EMAP, a neural architecture that uses spatial and temporal evidence accumulation to extend the capabilities of fuzzy ARTMAP. ART-EMAP combines supervised and unsupervised learning and a medium-term memory process to accomplish stable pattern category recognition in a noisy input environment. The ART-EMAP system features (i) distributed pattern registration at a view category field; (ii) a decision criterion for mapping between view and object categories which can delay categorization of ambiguous objects and trigger an evidence accumulation process when faced with a low confidence prediction; (iii) a process that accumulates evidence at a medium-term memory (MTM) field; and (iv) an unsupervised learning algorithm to fine-tune performance after a limited initial period of supervised network training. ART-EMAP dynamics are illustrated with a benchmark simulation example. Applications include 3-D object recognition from a series of ambiguous 2-D views.