Learning perceptual schemas to avoid the utility problem

This paper describes principles for representing and organising planning knowledge in a machine learning architecture. One of the difficulties with learning about tasks requiring planning is the utility problem: as more knowledge is acquired by the learner, the utilisation of that knowledge takes on a complexity which overwhelms the mechanisms of the original task. This problem does not, however, occur with human learners: on the contrary, it is usually the case that, the more knowledgeable the learner, the greater the efficiency and accuracy in locating a solution. The reason for this lies in the types of knowledge acquired by the human learner and its organisation. We describe the basic representations which underlie the superior abilities of human experts, and describe algorithms for using equivalent representations in a machine learning architecture

What forms the chunks in a subject's performance? Lessons from the CHREST computational model of learning

Computational models of learning provide an alternative technique for identifying the number and type of chunks used by a subject in a specific task. Results from applying CHREST to chess expertise support the theoretical framework of Cowan and a limit in visual short-term memory capacity of 3–4 looms. An application to learning from diagrams illustrates different identifiable forms of chunk

CHREST+: A simulation of how humans learn to solve problems using diagrams.

This paper describes the underlying principles of a computer model, CHREST+, which learns to solve problems using diagrammatic representations. Although earlier work has determined that experts store domain-specific information within schemata, no substantive model has been proposed for learning such representations. We describe the different strategies used by subjects in constructing a diagrammatic representation of an electric circuit known as an AVOW diagram, and explain how these strategies fit a theory for the learnt representations. Then we describe CHREST+, an extended version of an established model of human perceptual memory. The extension enables the model to relate information learnt about circuits with that about their associated AVOW diagrams, and use this information as a schema to improve its efficiency at problem solving

Learning-based constraints on schemata

Schemata are frequently used in cognitive science as a descriptive framework for explaining the units of knowledge. However, the specific properties which comprise a schema are not consistent across authors. In this paper we attempt to ground the concept of a schema based on constraints arising from issues of learning. To do this, we consider the different forms of schemata used in computational models of learning. We propose a framework for comparing forms of schemata which is based on the underlying representation used by each model, and the mechanisms used for learning and retrieving information from its memory. Based on these three characteristics, we compare examples from three classes of model, identified by their underlying representations, specifically: neural network, production-rule and symbolic network models

The CHREST model of active perception and its role in problem solving

We discuss the relation of TEC to a computational model of expert perception, CHREST, based on the chunking theory. TEC’s status as a verbal theory leaves several questions unanswerable, such as the precise nature of internal representations used, or the degree of learning required to obtain a particular level of competence: CHREST may help answer such questions

Adaptive prediction in digitally controlled buck converter with fast load transient response

An adaptive prediction scheme based on linear extrapolation for digitally controlled voltage-mode buck-type switching converter is presented. A major drawback of conventional digitally controlled switching converters is bandwidth limitation due to the additional phase lag in the digital feedback control loop. By predicting the future error voltage, the ADC sampling time delay is compensated in order to achieve a higher bandwidth even with a modest sampling rate. Both simulation and measurement results show that the output voltage settling time of the digitally controlled buck converter is reduced by as much as 28% with the proposed adaptive prediction. The fastest settling time in response to a 600mA load transient is around 15μs, approaching the transient response of the state-of-the-art analog-based controller.published_or_final_versio

Turning turtle: scaling relationships and self-righting ability in Chelydra serpentina

Testudines are susceptible to inversion and self-righting using their necks, limbs or both, to generate enough mechanical force to flip over. We investigated how shell morphology, neck length and self-righting biomechanics scale with body mass during ontogeny in Chelydra serpentina, which uses neck-powered self-righting. We found that younger turtles flipped over twice as fast as older individuals. A simple geometric model predicted the relationships of shell shape and self-righting time with body mass. Conversely, neck force, power output and kinetic energy increase with body mass at rates greater than predicted. These findings were correlated with relatively longer necks in younger turtles than would be predicted by geometric similarity. Therefore, younger turtles self-right with lower biomechanical costs than predicted by simple scaling theory. Considering younger turtles are more prone to inverting and their shells offer less protection, faster and less costly self-righting would be advantageous in overcoming the detriments of inversion

Scalability of Quasi-hysteretic FSM-based Digitally Controlled Single-inductor Dual-string Buck LED Driver To Multiple Strings

There has been growing interest in Single-Inductor Multiple-Output (SIMO) DC-DC converters due to its reduced cost and smaller form factor in comparison with using multiple single-output converters. An application for such a SIMO-based switching converter is to drive multiple LED strings in a multi-channel LED display. This paper proposes a quasi-hysteretic FSM-based digitally controlled Single-Inductor Dual-Output (SIDO) buck switching LED Driver operating in Discontinuous Conduction Mode (DCM) and extends it to drive multiple outputs. Based on the time-multiplexing control scheme in DCM, a theoretical upper limit of the total number of outputs in a SIMO buck switching LED driver for various backlight LED current values can be derived analytically. The advantages of the proposed SIMO LED driver include reducing the controller design complexity by eliminating loop compensation, driving more LED strings without limited by the maximum LED current rating, performing digital dimming with no additional switches required, and optimization of local bus voltage to compensate for variability of LED forward voltage (VF) in each individual LED string with smaller power loss. Loosely-binned LEDs with larger VF variation can therefore be used for reduced LED costs.postprin

Adaptive High-Bandwidth Digitally Controlled Buck Converter with Improved Line and Load Transient Response

Digitally controlled switching converter suffers from bandwidth limitation because of the additional phase delay in the digital feedback control loop. In order to overcome the bandwidth limitation without using a high sampling rate, this paper presents an adaptive third-order digital controller for regulating a voltage-mode buck converter with a modest 2x oversampling ratio. The phase lag due to the ADC conversion time delay is virtually compensated by providing an early estimation of the error voltage for the next sampling time instant, enabling a higher unity-gain bandwidth without compromising stability. An additional pair of low-frequency pole and zero in the third-order controller increases the low-frequency gain, resulting in faster settling time and smaller output voltage deviation during line transient. Both simulation and experimental results demonstrate that the proposed adaptive third-order controller reduces the settling time by 50% in response to a 1 V line transient and 30% in response to a 600 mA load transient, compared to the baseline static second-order controller. The fastest settling time is measured to be around 11.70 s, surpassing the transient performance of conventional digital controllers and approaching that of the state-of-the-art analog-based controllers.postprin

Learning perceptual chunks for problem decomposition

How students learn to use diagrammatic representations is an important topic in the design of effective representations for problem solving or conceptual learning, but few good models of their learning exist. In this paper, we explore the learning process with an experiment using AVOW diagrams as a representation for solving problems in electric circuits. We find that the participants decompose each circuit into a similar set of groups when solving the problems. The natural question is whether these groups are an artifact of the visual form of the circuit, or indeed the result of prior learning. We argue that the decompositions are a result of perceptual chunking, and that they are (at least partly) a result of learning. In support of this, we describe a computational model of perceptual learning, CHREST+, and show that it predicts the decomposition of problems evident in the participants' data