Tamotsu Mitamura

Serious games for learning programming languages

In recent years, so-called serious games, where some kind of knowledge is gained through digital game play, have received significant attention and there has been an active movement toward games that effectively enrich the learning environment. Serious games are games that are developed according to the concept, “It is possible through game play to obtain some kind of knowledge while having fun. Through these serious games, there has been an active movement toward fun learning using games in education at school and internal training at organizations. In contrast, at universities the level of learning programming differs significantly depending on the academic background of the student and there are issues with traditional programming education learning because the student does not feel enjoyment and is not interested in information engineering. As a countermeasure, teaching methods and teaching aids etc. have been cited as themes and learning through the utilization of serious games and game development has been proposed to tackle these themes. In this paper we propose and evaluate serious games for programming learning based on the concept “Learning programming through gaming”.

Spread-Repair Algorithm for Solving Extended Fuzzy Constraint Satisfaction Problems

In this paper, we propose a new type of FCSPs called hybrid domain FCSPs that have a mixture of discrete and continuous domains. To solve this type of problems, we present an algorithm called Spread-Repair based on the framework of iterative improvement. Experimental results on some test problems show that the algorithm has an ability of finding local approximate solutions with high probability in a computation time much shorter than the traditional, discrete-domain FCSP.

Fuzzy structural modeling based on FISM/fuzzy

Computer-supported cooperative work (CSCW) has emerged in the middle of the 80s as an identifiable research field focused on the role of computer technology in group work. CSCW examines how people work together in groups and how computer technology can support them. FISM (flexible interpretive structural modeling) developed by the author, is a method to develop structural models of complex systems. In this paper, FISM is extended to develop a new idea processing method: FISM/fuzzy. FISM/fuzzy is a fuzzy version of FISM. Some theoretical results which enable to model logically and efficiently are derived. The process of construct the structure of complex systems by FISM/fuzzy is called the FISM/fuzzy session. Outline of the FISM/fuzzy session is described and efficiency of the session is proposed. An illustrated example is shown.<<ETX>>

Characteristic Analysis of Agents in Adaptive Consensus Formation Models

The models of adaptive consensus formation are useful for obtaining harmonious consensus of a group when all members of the group pursue their individual rationality. In the process of consensus formation, the agents access to macro information and adapt their preferences to the overall group preference. In this paper, we propose and analyze some quantitative measures of the difference between the individual preferences and the agreed preference. Moreover, we introduce a model of the agents with the different speeds of adaption and analyze its effects to consensus formation by means of simulation.

A framework of an analytic hierarchy process method based on ordinal scale

Analytic hierarchy process (AHP), being used in decision making of various problems, is a method based on ratio scale. However, there are some problems in the use of the traditional AHP. We intend to solve those problems in this paper. We propose an AHP method based on ordinal scale which is appropriate to deal with qualitative variables. We also consider methods of evaluation suitable for the proposed method. Fuzzy algebraic evaluation and fuzzy integral evaluation using the Sugeno integral are described. Finally, we show an example to illustrate our methods.

Idea generation supporting system based on FISM/fuzzy

The field of computer-supported cooperative work (CSCW) focuses on the role of computer technology in group work. CSCW examines how people work together in groups and how computer technology can support them. FISM (flexible interpretive structural modeling), developed by the author, is a method to develop structural models of complex systems. In this paper, we propose the method of idea generation based on FISM/fuzzy: fuzzy flexible interpretive structural modeling. FISM/fuzzy is a fuzzy version of FISM. Some theoretical results which enable logical and efficient modeling are derived. The process of idea generation by FISM/fuzzy is called the FISM/fuzzy session.

Simulation of consensus formation models based on Structural Modeling

Consensus formation models are mathematical models for obtaining potential candidates for a group when all group members pursue their individual preferences. In such models, the members often change their preferences based on the groups global preference which summarizes the overall individual preferences. Based on the theory of partially-filled reachability matrices, the Flexible Interpretive Structural Modeling (FISM) provides a method for developing structural models of complex systems and is expected to be applied to decision-making support systems. In this paper, we propose a method for simulating consensus formation in FISM and study its effectiveness through the experiments with several consensus strategies.

Extending Fuzzy Constraint Satisfaction Problems

This paper propose a new type of Fuzzy CSP(Constraint Satisfaction Problem) that have a mixture of discrete and continuous domains, and a Spread-Repair algorithm. In traditional CSP and Fuzzy CSP, values for the variables are chosen from the discrete domains. However, this is often inconvenient when one wants to express real world problems. We show that this model, called HDFCSP(Hybrid Domain Fuzzy CSP), can be solved by Spread-Repair, an extension of the well known iterative improvement algorithms. Experimental results on some test problems show that the algorithm actually has an ability of finding partial approximate solutions with high probability in a computation time much shorter than the traditional, discretedomain FCSP.

Interactive approach to fuzzy structural modeling based on FISM/fuzzy

Computer-supported cooperative work (CSCW) emerged in the middle of the 80s as an identifiable research field focused on the role of computer technology in group work. CSCW examines how people work together in groups and how computer technology can support them. FISM (flexible interpretive structural modeling) developed by the author, is a method to develop structural models of complex systems. In this paper, FISM is extended to develop a new method: FISM/fuzzy. FISM/fuzzy is a fuzzy version of FISM. Some theoretical results which enable us to model logically and efficiently are derived. The process of constructing the structure of complex systems by FISM/fuzzy is called the FISM/fuzzy session. An outline of the FISM/fuzzy session is described and efficiency of the session is given.

An Associative Memorization Architecture of Extracted Musical Features from Audio Signals by Deep Learning Architecture

Abstract In this paper, we develop associative memorization architecture of the musical features from time sequential data of the music audio signals. This associative memorization architecture is constructed by using deep learning architecture. Challenging purpose of our research is the development of the new composition system that automatically creates a new music based on some existing music. How does a human composer make musical compositions or pieces? Generally speaking, music piece is generated by the cyclic analysis process and re-synthesis process of musical features in music creation procedures. This process can be simulated by learning models using Artificial Neural Network (ANN) architecture. The first and critical problem is how to describe the music data, because, in those models, description format for this data has a great influence on learning performance and function. Almost of related works adopt symbolic representation methods of music data. However, we believe human composers never treat a music piece as a symbol. Therefore raw music audio signals are input to our system. The constructed associative model memorizes musical features of music audio signals, and regenerates sequential data of that music. Based on experimental results of memorizing music audio data, we verify the performances and effectiveness of our system.