Koichiro Morihiro

Bio: Koichiro Morihiro is an academic researcher from Hyogo University of Teacher Education. The author has contributed to research in topics: Reinforcement learning & Flocking (behavior). The author has an hindex of 7, co-authored 24 publications receiving 147 citations.

A Feedback-Augmented Method for Detecting Errors in the Writing of Learners of English

Abstract: This paper proposes a method for detecting errors in article usage and singular plural usage based on the mass count distinction. First, it learns decision lists from training data generated automatically to distinguish mass and count nouns. Then, in order to improve its performance, it is augmented by feedback that is obtained from the writing of learners. Finally, it detects errors by applying rules to the mass count distinction. Experiments show that it achieves a recall of 0.71 and a precision of 0.72 and outperforms other methods used for comparison when augmented by feedback.

Emergence of flocking behavior based on reinforcement learning

Abstract: Grouping motion, such as bird flocking, land animal herding, and fish schooling, is well-known in nature Many observations have shown that there are no leading agents to control the behavior of the group Several models have been proposed for describing the flocking behavior, which we regard as a distinctive example of the aggregate motions In these models, some fixed rule is given to each of the individuals a priori for their interactions in reductive and rigid manner Instead of this, we have proposed a new framework for self-organized flocking of agents by reinforcement learning It will become important to introduce a learning scheme for making collective behavior in artificial autonomous distributed systems In this paper, anti-predator behaviors of agents are examined by our scheme through computer simulations We demonstrate the feature of behavior under two learning modes against agents of the same kind and predators

Characteristics of Flocking Behavior Model by Reinforcement Learning Scheme

Abstract: Grouping motion of creatures is observed in various scenes in nature. As its typical cases, bird flocking, land animal herding, and fish schooling are well-known. Many observations have shown that there are no leading agents to control the behavior of the group. Several models have been proposed for describing the flocking behavior. In these models, some fixed rule is given to each of the individuals a priori for their interactions in reductive and rigid manner. Instead of this, we have proposed a new framework for self-organized flocking of agents by reinforcement learning. It will become important to introduce a learning scheme for making collective behavior in artificial autonomous distributed systems. In this paper, anti-predator behaviors of agents are examined by our scheme through computer simulations. We demonstrate the feature of behavior under two learning modes against agents of the same kind and predators.

Edu-mining for Book Recommendation for Pupils

Abstract: This paper proposes a novel method for recommending books to pupils based on a framework called Edu-mining. One of the properties of the proposed method is that it uses only loan histories (pupil ID, book ID, date of loan) whereas the conventional methods require additional information such as taste information from a great number of users which is costly to obtain. To achieve this, the proposed method solves the book recommendation problem as a problem of loan date prediction, relying solely on loan histories. Experiments show that the proposed method achieves an accuracy of 60% and outperforms the method (weighted slope open collaborative filtering) used for comparison. In addition to the performance, the proposed method has the following two advantages: (i) it is inexpensive compared to the conventional methods and (ii) reading level is adjustable.

Learning Grouping and Anti-predator Behaviors for Multi-agent Systems

Abstract: Several models have been proposed for describing grouping behavior such as bird flocking, terrestrial animal herding, and fish schooling. In these models, a fixed rule has been imposed on each individual a priori for its interactions in a reductive and rigid manner. We have proposed a new framework for self-organized grouping of agents by reinforcement learning. It is important to introduce a learning scheme for developing collective behavior in artificial autonomous distributed systems. This scheme can be expanded to cases in which predators are present. In this study we integrate grouping and anti-predator behaviors into our proposed scheme. The behavior of agents is demonstrated and evaluated in detail through computer simulations, and their grouping and anti-predator behaviors developed as a result of learning are shown to be diverse and robust by changing some parameters of the scheme.

Educational Data Mining: A Review of the State of the Art

Abstract: Educational data mining (EDM) is an emerging interdisciplinary research area that deals with the development of methods to explore data originating in an educational context. EDM uses computational approaches to analyze educational data in order to study educational questions. This paper surveys the most relevant studies carried out in this field to date. First, it introduces EDM and describes the different groups of user, types of educational environments, and the data they provide. It then goes on to list the most typical/common tasks in the educational environment that have been resolved through data-mining techniques, and finally, some of the most promising future lines of research are discussed.

Automated Grammatical Error Correction for Language Learners

Abstract: It has been estimated that over a billion people are using or learning English as a second or foreign language, and the numbers are growing not only for English but for other languages as well. These language learners provide a burgeoning market for tools that help identify and correct learners' writing errors. Unfortunately, the errors targeted by typical commercial proofreading tools do not include those aspects of a second language that are hardest to learn. This volume describes the types of constructions English language learners find most difficult -- constructions containing prepositions, articles, and collocations. It provides an overview of the automated approaches that have been developed to identify and correct these and other classes of learner errors in a number of languages. Error annotation and system evaluation are particularly important topics in grammatical error detection because there are no commonly accepted standards. Chapters in the book describe the options available to researchers, recommend best practices for reporting results, and present annotation and evaluation schemes. The final chapters explore recent innovative work that opens new directions for research. It is the authors' hope that this volume will contribute to the growing interest in grammatical error detection by encouraging researchers to take a closer look at the field and its many challenging problems. Table of Contents: Introduction / History of Automated Grammatical Error Detection / Special Problems of Language Learners / Language Learner Data / Evaluating Error Detection Systems / Article and Preposition Errors / Collocation Errors / Different Approaches for Different Errors / Annotating Learner Errors / New Directions / Conclusion

The Ups and Downs of Preposition Error Detection in ESL Writing

Abstract: In this paper we describe a methodology for detecting preposition errors in the writing of non-native English speakers. Our system performs at 84% precision and close to 19% recall on a large set of student essays. In addition, we address the problem of annotation and evaluation in this domain by showing how current approaches of using only one rater can skew system evaluation. We present a sampling approach to circumvent some of the issues that complicate evaluation of error detection systems.

A Q-Learning Approach to Flocking With UAVs in a Stochastic Environment

Abstract: In the past two decades, unmanned aerial vehicles (UAVs) have demonstrated their efficacy in supporting both military and civilian applications, where tasks can be dull, dirty, dangerous, or simply too costly with conventional methods. Many of the applications contain tasks that can be executed in parallel, hence the natural progression is to deploy multiple UAVs working together as a force multiplier. However, to do so requires autonomous coordination among the UAVs, similar to swarming behaviors seen in animals and insects. This paper looks at flocking with small fixed-wing UAVs in the context of a model-free reinforcement learning problem. In particular, Peng’s $Q(\lambda )$ with a variable learning rate is employed by the followers to learn a control policy that facilitates flocking in a leader-follower topology. The problem is structured as a Markov decision process, where the agents are modeled as small fixed-wing UAVs that experience stochasticity due to disturbances such as winds and control noises, as well as weight and balance issues. Learned policies are compared to ones solved using stochastic optimal control (i.e., dynamic programming) by evaluating the average cost incurred during flight according to a cost function. Simulation results demonstrate the feasibility of the proposed learning approach at enabling agents to learn how to flock in a leader-follower topology, while operating in a nonstationary stochastic environment.