Many studies on skill acquisition have claimed that improvements in performance follow the power law of practice. However, it is also well-known that during long-term practice there are fluctuations such as plateaus, regressions, and spurts. In order to objectively examine the fluctuations in learning process, we analyzed a long-term learning process in a simple assembly task. We applied a time-series analysis based on the state space method to the task completion time. The analysis revealed that the power law of practice provided only a first approximation, and that fluctuations around the power law line reflected long-term trends. Next, we focused on one of the fluctuations, and carried out cognitive analysis to find what produced the fluctuation. We found that, contrary to the dominant skill acquisition model, the slump was attributable to the mismatch between the level of skills and the environment that the skills are demonstrated in. This analysis suggests that, in order to fully elucidate the processes and mechanisms of skill acquisition, attention should be paid not only to the internal mechanisms, but also to the external environment that the skills are demonstrated in. We discussed the above results in terms of the essential role of fluctuations and environment in skill learning as well as the nature of the data obtained from a single subject.

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Skill, such as arts, sports and crafts, is regarded as a cycle that consists of the following three steps: recognition of objects, selection of appropriate action series and execution of the action. In arts and crafts, people produce works as a result of this cycle. Skill-learning environment should involve diagnosis-function providing appropriate advice for each step. This paper describes technique that is providing advice in real time when a learner learns recognition of drawing. To assist learners' recognition, we developed the sketch-area-dependent advising system that presents advice with voice for learners' drawing. The effectiveness of advice was confirmed through an experiment evaluating proposed technique.

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In this paper, we propose a method for directly measuring and analyzing driving behavior using wireless 3D-accelerometers. Whereas existing systems installed many sensors into a specially equipped test vehicle to indirectly measure driving behaviors, our method uses wireless 3D-accelerometers attached to a driver for directly measuring his/her behaviors in a vehicle. After applying independent component analysis (ICA) to reduce car-caused noise, our method detects anomalies in driving behaviors using one-class SVM. By directly measuring driving behavior, our method allows to point out anomalies in driving behaviors characteristics to novice drivers with precision of 71.1% and recall of 73.9%.

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Sign language is a visual language in which main articulators are hands, torso, head, and face. For simultaneous interpreters of Japanese sign language (JSL) and spoken Japanese, it is very important to recognize not only the hands movement but also prosody such like head, eye, posture and facial expression. This is because prosody has grammatical rules for representing the case and modification relations in JSL. The goal of this study is to introduce an examination called MPR (Measurement of Prosody Recognition) and to demonstrate that it can be an indicator for the other general skills of interpreters. For this purpose, we conducted two experiments: the first studies the relationship between the interpreter's experience and the performance score on MPR (Experiment-1), and the second investigates the specific skill that can be estimated by MPR (Experiment-2). The data in Experiment-1 came from four interpreters who had more than 1-year experience as interpreters, and more four interpreters who had less than 1-year experience. The mean accuracy of MPR in the more experienced group was higher than that in the less experienced group. The data in Experiment-2 came from three high MPR interpreters and three low MPR interpreters. Two hearing subjects and three deaf subjects evaluated their skill in terms of the speech or sign interpretation skill, the reliability of interpretation, the expeditiousness, and the subjective sense of accomplishment for the ordering pizza task. The two experiments indicated a possibility that MPR could be useful for estimating if the interpreter is sufficiently experienced to interpret from sign language to spoken Japanese, and if they can work on the interpretation expeditiously without making the deaf or the hearing clients anxious. Finally we end this paper with suggestions for conclusions and future work.

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We investigate an Abductive Logic Programming (ALP) framework to find appropriate hypotheses to explain both professional and amateur skill performance, and to distinguish and diagnose amateur faulty performance. In our approach, we provide two kinds of rules: motion integrity constraints and performance rules. Motion integrity constraints are essential to formulate skillful performance, as they prevent the generation of hypotheses that contradict the constraints.

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Based on the conjecture that acquisition of embodied expertise is a phenomenon that occurs through interactions among the learner's verbal thoughts, perception, physical movements and the surrounding environment, Suwa [2005b] has claimed the significance of dealing with subjective data such as verbalized thoughts in researches on embodied skills, and has advocated a theory on meta-cognitive verbalization. The present paper, based on the empirical findings in playing darts game, provides a cognitive model of embodied meta-cognitive verbalization. This model theorizes what kinds of cognitive processes involve embodied meta-cognitive verbalization, and how these processes change a learner's thoughts, perception, actions and self-awareness to those, and thereby promote acquisition of embodied expertise.

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Human movements are produced in variable external/internal environments. Because of this variability, the same motor command can result in quite different movement patterns. Therefore, to produce skilled movements humans must coordinate the variability, not try to exclude it. In addition, because human movements are produced in redundant and complex systems, a combination of variability should be observed in different anatomical/physiological levels. In this paper, we introduce our research about human movement variability that shows remarkable coordination among components, and between organism and environment. We also introduce nonlinear dynamical models that can describe a variety of movements as a self-organization of a dynamical system, because the dynamical systems approach is a major candidate to understand the principle underlying organization of varying systems with huge degrees-of-freedom.

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In the process of mixture model estimation using Expectation-Maximization (EM) methods, mixture densities are required to be measured at every step to obtain posterior probabilities. When the number of data n in a dataset or the number of mixtures m is large, the time complexity required for the evaluation of posterior probabilities is O(mn).

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In this paper, we propose an automatic Kansei fuzzy rule creating system using thesaurus. In general, there are a lot of words that express impressions. However, conventional approaches of Kansei engineering are not suitable to use many impression words because it is difficult to collect enough data. The proposed system is an enhanced algorithm of the conventional method that the authors proposed before. The proposed system extracts fuzzy rules for many words defined in the thesaurus dictionary while the conventional one can extract rules of specified words which user defined. The flow of the system consists of 3 steps: (1) construction of thesaurus networks; (2) data collection by web questionnaire sheets; (3) Extraction of fuzzy rules. In order to extract Kansei fuzzy rules, the system employs enhanced GRNN(general regression neural network) which can treat relative words of the thesaurus network. Using a Japanese thesaurus dictionary in the experiments, the sets of fuzzy rules for 1,195 impression words are extracted, and the fuzzy rules extracted by the proposed system obtained higher accuracy than those extracted by the conventional one.

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We present the intentional kernel as a new class of kernel functions for structured data. The class is highly contrasted to the convolution kernel, that is a typical class of kernel functions. That is, the convolution kernel is defined with sub-structures, while the intentional kernel is based on derivations constracting structures. We show instances of the intentional kernel for boolean functions, first-order terms, context sensitive languages, and RNA sequences. We also show some properties of the intentional kernel, and discuss the difference between the intentional kernel and the convolution kernel.

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This paper discusses evolutionary multi-objective optimization (EMO) method for lens system design problems that have properties of global and local multimodality, epistasis among parameters and ill-scaledness. Applying NSGA-II-like EMO to them, it faces some difficulties. To solve them, we present a two stage GA called Solid EMO that consists of a repeated ESO (Evolutionary Single-objective Optimization) and an augmented EMO. The repeated ESO searches seeds of Pareto optimal solutions through solving weighted sum minimization problems repeatedly by a real-coded GA using ISM that deals with global multi-modality well. The augmented EMO, that behaves like a kind of local search by k-nearest neighbor limitation in reproduction and crossover with an ability of explorative search, refines and expands the seeds found by the first stage GA. Solid EMO was applied to three and four element lens system design problems. As a result, the proposed method succeeded in finding highly precise solution sets that consist of well-known types, triplet-type and Lee-type lens systems, in the three-element and four-element lens system design problems, respectively.

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This paper proposes a method to measure the effects of TV advertisements on the Internet bulletin boards. It aims to clarify how the viewes' interests on TV advertisements are reflected on their images on the promoted products. Two kinds of time series data are generated based on the proposed method. First one represents the time series fluctuation of the interests on the TV advertisements. Another one represents the time series fluctuation of the images on the products. By analysing the correlations between these two time series data, we try to clarify the implicit relationship between the viewer's interests on the TV advertisement and their images on the promoted products. By applying the proposed method to an Internet bulletin board that deals with certain cosmetic brand, we show that the images on the products vary depending on the difference of the interests on each TV advertisement.

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We propose a method for learning semantic categories of words with minimal supervision from web search query logs. Our method is based on the Espresso algorithm (Pantel and Pennacchiotti, 2006) for extracting binary lexical relations, but makes important modifications to handle query log data for the task of acquiring semantic categories. We present experimental results comparing our method with two state-of-the-art minimally supervised lexical knowledge extraction systems using Japanese query log data, and show that our method achieves higher precision than the previously proposed methods.

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The presented study deals with the so-called soft constraint satisfaction problem (SCSP) and proposes an extension to the standard SCSP formulation to accommodate a wider class of over-constrained situations and allow for a generally higher level of flexibility in the constraint-driven problem-solving. The extended modeling approach called Achievement-Weighted Constraint Satisfaction (AWCS) assumes the definition of constraint parameters ``traditional'' for SCSPs, as well as additional parameters specified to dynamically manipulate constraint weights in the course of solution search. These latter parameters make it possible to ``relax'' over-constrained models and obtain a solution even when there are mutually contradicting rules utilized by an AWCS problem-solver. To explore the proposed modeling framework, a task of finding an optimal route in car navigation, based on user preferences - a popular are of research in SCSP studies - is considered. A case study is presented, in which an optimal route is first modeled with constraints reflecting user preferences. Problem solutions having different optimality levels are then obtained. A software system is developed to automate both the optimal route modeling (via interaction with the user) and the solution search processes. The system is applied in an experiment conducted to validate the theoretical ideas. Experimental results are discussed, and conclusions are drawn.

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In this study, we propose the stop and go particle swarm optimization (PSO) algorithm, a new method to dynamically adapt the PSO population size. Stop and go PSO (SG-PSO) takes advantage of the fact that in practical problems there is a limit to the required accuracy of the optimization result. In SG-PSO, particles are stopped when they have approximately reached the required accuracy. Stopped particles do not consume valuable function evaluations. Still, the information contained in the stopped particles' state is not lost, but rather as the swarm evolves, the particles may become active again, behaving as a memory for the swarm. In addition, as an extension to the SG-PSO algorithm we propose the mixed SG-PSO (MSG-PSO) algorithm. In the MSG-PSO algorithm each particle is given a required accuracy, and through the accuracy settings global search and local search can be balanced. Both SG-PSO and MSG-PSO algorithms are straightforward modifications to the standard PSO algorithm. The SG-PSO algorithm shows strong improvements over the standard PSO algorithm on multimodal benchmark functions from the PSO literature while approximately equivalent results are observed on unimodal benchmark functions. The MSG-PSO algorithm outperforms the standard PSO algorithm on both unimodal and multimodal benchmark functions.

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