This paper shows the findings through the quest for the essence of “fuzziness” and “naturalness” by starting from focusing on a way of problem solving called constraint-oriented approach. The quest arrived at first an unexpected point of view where various entities are so strongly tied together that they cannot be divided into subsets which are exclusive with each other. On this view point, “Natural Farming” by Masanobu Fukuoka and “Permaculture” by Bill Mollison are very suggestive through which the quest arrived at the notion of “Stackedness” that seems to reflect the essence of the naturalness. Moreover, the quest encountered with a very interesting and important institution called “Urakawa Bethel's House” where people with mental disorders work together by creating a very distinguished community culture which seems to reflect the core essence of “naturalness in human living”. Moreover, certain researches on group dynamics and caring such as canopy theory by Toshio Sugiman, narrative therapy, etc. are referred to for elucidating the essence of naturalness. Further researches on the stackedness are done by analyzing the stacked structure of communication. This is done by introducing Leibnizian notions of Space and Time with focusing on a theater play example. Then a communication act is shown to be a co-creation of Leibnizian space and time yielding a stacked and nested spatio-temporal structure. This structure can be regarded as “soil” for communication generation and in turn is enriched through community formation. This resulted in a novel research field, “Information Edaphology.” Moreover, a novel notion of information, “Inclusive Information”, and a novel style of thinking, “Stacked Thinking”, are introduced that are closely related to Information Edaphology. Finally, novel notion of systems, “fluffy systems”, is introduced which elucidates the generative aspects of stacked systems, while stackedness itself is a resultant characteristic of these systems. That is, we have returned to the original point of departure; the original meaning of “fuzziness” is “being fluffy”.

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The purpose of this paper is that the author, who lacks the experience in SOFT, objectively makes a novel unbiased proposal by looking back in the past twenty years and the history of SOFT. The paper analyzed SOFT from five points of view; the membership, the number of papers, the titles of special issue, information visualization based on research field network and comments on the journal of SOFT. I described some subjective proposals, which will lead SOFT to the bright future I believe lies ahead. I would be happy if my objective investigations and subjective proposals trigger widespread discussion for SOFT and the members of SOFT.

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Owing to the progress of needs to analyze huge amount of data collected from real world and help us do decision makings for our various activities based on the analysis, the fusion of data mining techniques and soft computing techniques is growing. The author thinks the trend is more and more growing, and it expands the research field. This paper investigates trend of the field in FUZZ-IEEE which is one of representative conferences for the soft computing techniques. Also, it grasps the trend from theoretical researches to application researches. Lastly, this paper focuses on the fuzzy association rule discovery vigorously studied by many researchers. It introduces the research directions such as the discovery from complicated data and intuitive data, and the improvement of the discovery methods reflecting the features of fuzzy sets.

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Four types of discussions as to usefulness of fuzzy clustering are shown. First, fuzzy graphs play the central role in agglomerative hierarchical clustering algorithms. Second, Entropy-based methods in fuzzy c-means clustering show generalizations of Gaussian mixture models and connect the hard c-means with a statistical model. Third, fuzzy classification functions show theoretical properties of fuzzy classification rules induced from fuzzy c-means clustering. Lastly, fuzzy cluster validity measures provide a good approach to determine the number of clusters.

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Decision making problems in decentralized organizations are often modeled as Stackelberg games, and they are formulated as two-level mathematical programming problems. There are two decision makers in the problems. If they do not have any motivation to cooperate mutually and behave rationally, the outcomes of the problems can be explained by Stackelberg equilibrium which is not always Pareto optimal. From the computational aspect, it is known that solving the problem is NP-hard even if the objective functions and the constraint are linear. In contrast, if decision makers can select strategies cooperatively, the most important aspect is to derive Pareto optimal solutions favorable to the decision makers, and as a method of this line of approach interactive fuzzy programming has been developed, taking into account fuzziness of human judgments. In this paper, after reviewing the development of solution methods for two- and multi-level programming problems, we focus on cooperative decision making in decentralized organizations and present interactive fuzzy programming for two-level linear programming problems, which provide satisfactory solutions in accordance with preference of the decision makers. Moreover, we describe extensions of interactive fuzzy programming for two-level linear programming problems under multiobjective environments and under uncertainty.

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It is generally thought that living things have desires for conformity as well as desires for differentiation, which make their preferences show trends. Recently, it was confirmed that there were trends in the preferences of how female birds chose their mates. We think trends in the preferences are related to desires for conformity and differentiation and the strength of desires among living species are genetically different from one and another. We describe the strength of desires among living species as being artificial agents of genes. In this paper, we simulate phenomena of fashion in female preferences for a mate by using an agent model that consists of imported conformity and differentiation as genes. In this experiment, we found that there were two kinds of periodic phenomena of fashion and reported the influence of conformity and differentiation on the transition of female preferences.

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In this paper, we tried to show covering domain “invisible specialty” of research fields by offering of the relations between papers in the field to novice researchers by proposed visualization method. We found it is not significantly different between experts and novices concerning the comprehension of actual covering domain of two academic journals in economics by a sorting test of published papers in each journal. Moreover, we confirmed that novices comprehend more clearly and consistently the covering domain of the fields by the developed information visualization system based on a hierarchical research field network.

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This paper considers a multiobjective random fuzzy linear programming problem using possibility and necessity measures. The proposed model is not a well-defined problem due to including randomness and fuzziness, and so introducing chance-constraints and the degree of possibility and necessity with fuzzy goals, the proposed model is transformed into the deterministic equivalent problem. Furthermore, in order to solve the multiobjective programming problem, the model maximizing the minimum aspiration level among all objects is proposed. Then, in order to solve it more efficiently, the relation between absolute deviation and variance is introduced by using the normality of random variables and the efficient solution method based on the linear programming is developed.

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This paper proposes a keyword map equipped with a relevance balance controller for supporting data analysis according to compensatory/non-compensatory decision making strategy. A keyword map has been proposed as general-purpose interactive graph visualization tool and applied to various decision making tasks. However, it cannot support data analysis according to compensatory/non-compensatory decision making strategy, because only single relationship type is used. The proposed relevance balance controller enables a user to dynamically adjust weight of each attribute for relationship calculation. Experimental results show the proposed system can provide various viewpoints and support data analysis according to compensatory/non-compensatory decision making strategy.

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In this paper, we describe an adaptive trading agent which can sell and buy electric power effectively in a locally produced and consumed electric energy network, ECONET (Electric Power Cluster Oriented Network). The trading agents manage the amount of electric power generated by solar panels or other renewable energies and stored in a storage battery in a minimal cluster. The agent learns a trading strategy by maximizing future cumulative reward based on reinforcement learning method. Especially, we build autonomous trading agents based on the natural actor-critic method, which is a type of natural policy gradient methods. Several experiments show that the adaptive trading agents can reduce useless energy consumption and a deficiency in most cases.

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An image index, extracting DCT domain color and texture features in indexing process, is proposed for JPEG and MPEG fast image retrieval. Feature extraction from 1,000 JPEG images requires 2.86 seconds, which is 116.17 times faster than previously reported by Serata (2006) because of efficient compressed domain feature extraction. Retrieval performances of the proposed and Serata's indexes are evaluated on Corel database, and the results suggest that the maximum precision and recall of the proposed index are improved by 22.7% and 11.1% with reduction of indexing times. Due to reduced computational times, the index is applicable to databases which include 100 times larger data sizes than current one.

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A central pattern generator (CPG) and passive dynamic walking (PDW) have attracted much attention in the research field of bipedal locomotion. We describe a motion control method based on dynamic joint passivization for biped robot locomotion. CPG-based motion control is effective for walking on uneven terrain. However, it has serious problems with energy loss. In contrast, PDW saves energy because a robot can walk without any active control or energy input on a downhill slope. However, PDW robot can not walk on uneven terrain, but only on a downhill slope. We think that active walking needs to be mixed with PDW for robot walking. Our motion control method is based on a mixture of the CPG and PDW, that is, the dynamic passivization of joint control. Experiments using the motion control method based on dynamic passivization of joint control successfully generated energy efficient walking and enabled superior gaits.

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This paper proposes the situation-dependent membership function estimation method based on analogical reasoning. The proposed method defines the relation between membership functions representing same category in different situations as a function, which is called analogy. If membership functions expressing same categories in different situations are already identified, analogy can be identified by them, and other membership functions in the situation can be estimated by analogical reasoning. This paper also confirms effectiveness of the proposed method experimentally. (1) The relations between membership functions in different situations are expressed by some degree polynomials in preliminary experiments and those polynomials are analyzed to obtain the appropriate degree of polynomials as analogy. (2) Situation-dependent membership functions estimated by analogical reasoning are compared with those identified by other method.

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This paper aims at the construction of a friendly system, which plays a seven-card stud poker game with a human player against an opponent player. This paper calls a cooperative friendly system a partner agent. Seven-card stud poker is a kind of a game with imperfect information and it is difficult to consider optimal strategy in such a game. Therefore, the partner agent has a discussion with a human player on game strategy cooperating with the player. If a human player needs some advice on a game, the partner agent presents its reply. Furthermore, the partner agent presents not only linguistic expressions of strategy but also facial expressions according to the game situation so that a human player can feel a sense of affinity with the agent. Neural network models are used for facial expressions. This paper also performs subject experiments to evaluate the effectiveness of the proposed partner agent with facial expressions, where each subject plays poker games with partner agents. This paper analyzes questionnaire data that subject answer after experiments.

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This paper presents an imitation learning method, which enables an autonomous robot to extract demonstrator's characteristic motions by observing unsegmented human motions. To imitate another's motions through unsegmented interaction, the robot has to find what he learns from the continuous time series. The learning architecture is developed mainly based on a switching autoregressive model (SARM), a keyword extraction method based on minimum description length principle, and singular vector decomposition to reduce dimensionality of high dimensional human bodily motion. In most previous research on methods of robotic imitation learning, target motions that were given to robots were segmented into several meaningful parts by the experimenters in advance. However, to imitate certain behaviors from the continuous motion of a person, the robot needs to find segments that should be learned. To achieve this goal, the learning architecture converts the continuous time series into a discrete time series of letters by using SARM after reducing its dimensionality by using SVD. After the conversion, the proposed method finds characteristic motions by utilizing n-gram statistics referring to description length. In our experiment, a demonstrator displayed several unsegmented motions to a robot. The results revealed that the framework enabled the robot to obtain several prepared characteristic human motions.

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