In spite of the fact that the relation between theory and practice is a foundation of scientific and technological development, the trend of increasing the gap between theory and practice accelerates in these years. The gap embraces a risk of distrust of science and technology. Ontological engineering as the content-oriented research is expected to contribute to the resolution of the gap. This paper presents the feasibility of organization of theoretical knowledge on ontological engineering and new-generation intelligent systems based on it through an application of ontological engineering in the area of learning/instruction support. This area also has the problem of the gap between theory and practice, and its resolution is strongly required. So far we proposed OMNIBUS ontology, which is a comprehensive ontology that covers different learning/instructional theories and paradigms, and SMARTIES, which is a theory-aware and standard-compliant authoring system for making learning/instructional scenarios based on OMNIBUS ontology. We believe the theory-awareness and standard-compliance bridge the gap between theory and practice because it links theories to practical use of standard technologies and enables practitioners to easily enjoy theoretical support while using standard technologies in practice. The following goals are set in order to achieve it; computers (1) understand a variety of learning/instructional theories based on the organization of them, (2) utilize the understanding for helping authors' learning/instructional scenario making and (3) make such theoretically sound scenarios interoperable within the framework of standard technologies. This paper suggests an ontological engineering solution to the achievement of these three goals. Although the evaluation is far from complete in terms of practical use, we believe that the results of this study address high-level technical challenges from the viewpoint of the current state of the art in the research area of artificial intelligence not only in education but also in general, and therefore we hope that constitute a substantial contribution for organization of theoretical knowledge in many other areas.

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Can we discover a node which is not observable directly but mediates the stochastic diffusion process in a network? We address such a node discovery and mathematically formulate the basic concept which is promising to solving the problem in general. The proposed method is tested with a node discovery in a Barabási-Albert model which the conventional method raised and partially succeeded in. Its performance is measured with the receiver operating characteristic curves and van Rijsbergen's F-measure (the harmonic mean of precision and recall). The proposed method succeeds in discovering an unobservable peripheral node, and an unobservable hub node in a less clustered network where the conventional method failed.

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G-Protein coupled receptors (GPCRs) comprise a large superfamily of proteins and are a target for nearly 50% of drugs in clinical use today. GPCRs have a unique structural motif, seven transmembrane helices, and it is known that agonists and antagonists dock with a GPCR in its ``active'' and ``inactive'' condition, respectively. Knowing conformations of both states is eagerly anticipated for elucidation of drug action mechanism. Since GPCRs are difficult to crystallize, the 3D structures of these receptors have not yet been determined by X-ray crystallography, except the inactive-state conformation of two proteins. The conformation of them enabled the inactive form of other GPCRs to be modeled by computer-aided homology modeling. However, to date, the active form of GPCRs has not been solved. This paper describes a novel method to predict the 3D structure of an active-state GPCR aiming at molecular docking-based virtual screening using real-coded genetic algorithm (real-coded GA), receptor-ligand docking simulations, and molecular dynamics (MD) simulations. The basic idea of the method is that the MD is first used to calculate an average 3D coordinates of all atoms of a GPCR protein against heat fluctuation on the pico- or nano- second time scale, and then real-coded GA involving receptor-ligand docking simulations functions to determine the rotation angle of each helix as a movement on wider time scale. The method was validated using human leukotriene B4 receptor BLT1 as a sample GPCR. Our study demonstrated that the established evolutionary search for the active state of the leukotriene receptor provided the appropriate 3D structure of the receptor to dock with its agonists.

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We study how initial network structure affects the evolution of cooperation in a spatial prisoner's dilemma game. The network structure is characterized by various statistical properties. In those properties, we focus on the variance of degree distribution, and inquire how it affects the evolution of cooperation. Some interactions between the variance of degree distribution and other statistical properties such as degree correlation and cluster coefficient are investigated. Moreover we compare results of static networks with those of dynamical networks generated in a process of replacing links by natural selection. It is found that a scale-free network does not always promote the evolution of cooperation, and there exists an appropriate value of the variance, at which the cooperation progresses strongly. In addition, we find that the effects of degree correlation and cluster coefficient for the evolution of cooperation vary with different variances of degree distribution.

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In this paper, we propose a verification methodology of large-scale legal knowledge. With a revision of legal code, we are forced to revise also other affected code to keep the consistency of law. Thus, our task is to revise the affected area properly and to investigate its adequacy. In this study, we extend the notion of inconsistency besides of the ordinary logical inconsistency, to include the conceptual conflicts. We obtain these conflictions from taxonomy data, and thus, we can avoid tedious manual declarations of opponent words. In the verification process, we adopt extended disjunctive logic programming (EDLP) to tolerate multiple consequences for a given set of antecedents. In addition, we employ abductive logic programming (ALP) regarding the situations to which the rules are applied as premises. Also, we restrict a legal knowledge-base to acyclic program to avoid the circulation of definitions, to justify the relevance of verdicts. Therefore, detecting cyclic parts of legal knowledge would be one of our objectives. The system is composed of two subsystems; we implement the preprocessor in Ruby to facilitate string manipulation, and the verifier in Prolog to exert the logical inference. Also, we employ XML format in the system to retain readability. In this study, we verify actual code of ordinances of Toyama prefecture, and show the experimental results.

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Cooperative problem solving with shared resources is important in practical multi-agent systems. Resource constraints are necessary to handle practical problems such as distributed task scheduling with limited resource availability. As a fundamental formalism for multi-agent cooperation, the Distributed Constraint Optimization Problem (DCOP) has been investigated. With DCOPs, the agent states and the relationships between agents are formalized into a constraint optimization problem. However, in the original DCOP framework, constraints for resources that are consumed by teams of agents are not well supported. A framework called Resource Constrained Distributed Constraint Optimization Problem (RCDCOP) has recently been proposed. In RCDCOPs, a limit on resource usage is represented as an n-ary constraint. Previous research addressing RCDCOPs employ a pseudo-tree based solver. The pseudo-tree is an important graph structure for constraint networks. A pseudo-tree implies a partial ordering of variables. However, n-ary constrained variables, which are placed on a single path of the pseudo-tree, decrease efficiency of the solver. We propose another method using (i) a pseudo-tree that is generated ignoring resource constraints and (ii) virtual variables representing the usage of resources. However the virtual variables increase search space. To improve pruning efficiency of search, (iii) we apply a set of upper/lower bounds that are inferred from resource constraints. The efficiency of the proposed method is evaluated by experiment.

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Although recommender systems have been evaluated in accuracy to capture user satisfaction, it is argued that the bottom-line measure of the success of a recommender system should go beyond accuracy since it alone is insufficient to capture it. Techniques to enhance various aspects of recommender systems such as similarity or novelty adding to accuracy were also proposed. In this paper, we propose a recommendation method to enhance serendipity based on the assumption that a certain degree of serendipity enhances user satisfaction. The basic idea of the proposed method is that the user would be unexpected if the system recommend the user's favorite contents which are depart from the habit in access. In our method, we firstly introduce a preference model and a habit model for the user, and then estimate the serendipity based on the differences between the results by both of them. We finally make a recommendation list by merging the contents selected by the preference model and those based on the estimated serendipity. We verify the effectiveness of the proposed method through TV show recommendation.

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Figures and tables in Vol.24, No.5, pp.397-404 were not displayed correctly so they were revised in Vol.24,No.5, p. 438-445.

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We study how initial network structure affects the evolution of cooperation in a spatial prisoner's dilemma game. The network structure is characterized by various statistical properties. In those properties, we focus on the variance of degree distribution, and inquire how it affects the evolution of cooperation. Some interactions between the variance of degree distribution and other statistical properties such as degree correlation and cluster coefficient are investigated. Moreover we compare results of static networks with those of dynamical networks generated in a process of replacing links by natural selection. It is found that a scale-free network does not always promote the evolution of cooperation, and there exists an appropriate value of the variance, at which the cooperation progresses strongly. In addition, we find that the effects of degree correlation and cluster coefficient for the evolution of cooperation vary with different variances of degree distribution.

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