論文

本研究では，赤外線センサを用いたセンサネットワークのトポロジを，センサの時系列データのみから推定するモデルを提案する．近年，センサネットワークやユビキタスコンピューティングを活用した，人の行動に基づいた機能やサービスを提供するアプリケーションが提案されている．これらのサービスを実現するためには，現在の環境や人の習慣に即した動線に基づく，センサネットワークのトポロジ情報が必要である．また，このトポロジ情報は行動習慣の変化や環境の物理的変化により頻繁に変化するため，高精度に自動推定する手法が求められる．提案手法では，より確実性の高い隣接関係の推定結果を用いながら，段階的に推定する隣接関係の数を増やす手法を用いる．これにより，推定の精度を保ちながら，多くの隣接関係を推定することを可能とする．実験では，実際の環境で収集したセンサデータを用いて提案手法を評価し，従来手法と比較して高精度・広範囲に隣接関係が推定できることを示す．

ここ数年，Twitter をはじめとしたソーシャルメディアを日常的に利用している人は多くなっている．先の震災でも，情報収集手段として活用した人も多数いた．これらソーシャルメディアが，災害時の情報伝播にどのように寄与したのかを知ることは，将来起こりうる災害において，情報技術をより有効に活用するための重要な知見となる．既存研究にて，災害をはじめとした社会的なイベントを知るのにソーシャルメディアが有効とされている．さらに，情報伝搬に関しても様々な視点から議論されている．本研究では，震災前後３週間の約４億の Tweet データを対象に，ユーザの利用状況・行動・コミュニケーション構造を，全体ならびに地域単位で分析することで，ユーザの状況やコミュニケーション構造を分析・推定する．それにより，将来，個々のユーザに適した情報を伝搬するために必要な仕組みを提供するための基礎的知見を明らかにする．

Traditionally, the support in the event of a disaster were based on hardware, the expertise are strongly required to rescuers. For this reason, in the case that the disaster had been occured, the role of the people expressly divided to the victims, the rescuers, and the onlookers. On the other hand, in the case of the Great East Japan Earthquake, many engineers try to develope information support systems for the victims by information technologies. It can be estimated that one of the reasons to realize these systems is generally utilization of information infrastructures such as web in our daily life. After such situation is considered, we started a project ``Collaborative Heterogeneous Integration of Disaster and Rescue Information (CHIDRI)'' which adopted to Challenge for Realizing Early Profits (CREP) in Japanese Society for Artificial Intelligence. In this paper, we explain the frame, the purpose, and the activities of the challenge.

In standard fighting videogames, users usually prefer playing against other users rather than against machines because opponents controlled by machines are in a rut and users can memorize their behaviors after repetitive plays. On the other hand, human players adapt to each other's behaviors, which makes fighting videogames interesting. Thus, in this paper, we propose an artificial agent for a fighting videogame that can adapt to its users, allowing users to enjoy the game even when playing alone. In particular, this work focuses on combination attacks, or combos, that give great damage to the opponent. The agent treats combos independently, i.e., it is composed of a subagent for predicting combos the user executes, that for choosing combos the agent executes, and that for controlling the whole agent. Human users evaluated the agent compared to static opponents, and the agent received minimal negative ratings.

Nowadays, users of Twitter, one of famous social networking service, have rapidly increased in number, and many people have been exchanging information by Twitter. When the Great East Japan Earthquake struck in 2011, people were able to obtain information from social networking services. Though Twitter played an important role, one problem was especially pointed out: false rumor diffusion. In this study, we propose an information diffusion model based on the SIR model and discuss how to prevent false rumor diffusion.

It is important to understand consumer needs correctly and clarify target of goods and service in marketing. In recent years, as information processing technology develops, video image analysis also has become as important tool for customer behavior analysis. It is said that discovering consumers’ purchase patterns of choosing purchased goods may be possible by using video data. Video is sequential temporal data, so time-series data mining technique is necessary. And generally consumer behavior is ambiguous. To respond to these situation, we are now developing a collective intelligence-based sequential pattern mining approach with high robustness and adaptability, and this time, we have succeeded in visualizing the relation of goods that they are continuously touched up by consumer.

During the 2011 East Japan Great Earthquake Disaster, some people used social media such as Twitter to get information important to their lives. However, the spread of groundless rumor information was big social problem. Therefore, social media users pay attention to prevent wrong information from diffusing. The way to stop the spread of a false rumor is needed, so we have to understand a diffusion of information mechanism. We have proposed information diffusion model which is based on SIR model until now. This model is represented by the stochastic state transition model for whether to propagate the information, and its transition probability is defined as the same value for all agents. People ’s thinking or actions are not the same. To solve this problem, we adopted three elements in our model: A new internal state switching model, user diversity and multiplexing of information paths. In this paper, we propose a novel information diffusion model, the Agent-based Information Diffusion Model (AIDM). We reproduce two kinds of false rumor information diffusion using proposed model. One is “single burst type false rumor spread ”, and another is “multi burst type false rumor spread. ”Proposal model is estimated by comparing real data with a simulation result.

Twitter is a popular microblogging service in Japan. People use the Twitter for communicating with friends, and posting tweet daily life events. In addition, Twitter has also been used in an emergency situation, such as the earthquake. In the East Japan great earthquake disaster, people were using Twitter to get refuge and rescue informations. However, spreading false rumor has become a major social problem. We aim to propose suppression scheme of false rumor. Therefore, we propose a novel multiagent-based information diffusion model to reveal the diffusion mechanism of false rumor in this paper. Our model is to focus on the information diffusion behavior of each Twitter user. We consider three elements of each user, “User’s diversity”, “Life pattern”, and “State transition”. In addition, our model also takes into account multiple of the information path, which is a feature of Twitter. We evaluate the validity of our model.

Traffic congestion is a serious problem for people living in urban areas, causing social problems such as time loss, economical loss, and environmental pollution. Therefore, we propose a multi-agent-based traffic light control framework for intelligent transport systems. Achieving consistent traffic flow necessitates the real-time adaptive coordination of traffic lights; however, many conventional approaches are of the centralized control type and do not have this feature. Our multi-agent-based control framework combines both indirect and direct coordination. Reaction to dynamic traffic flow is attained by indirect coordination, whereas green-wave formation, which is a systematic traffic flow control strategy involving several traffic lights, is attained by direct coordination. We present the detailed mechanism of our framework and verify its effectiveness using simulation to carry out a comparative evaluation.

Ants are known to use a colony-specific blend of cuticular hydrocarbons (CHCs) as a pheromone to discriminate between nestmates and non-nestmates and the CHCs were sensed in the basiconic type of antennal sensilla (S. basiconica). To investigate the functional design of this type of antennal sensilla, we observed the ultra-structures at 2D and 3D in the Japanese carpenter ant, Camponotus japonicus, using a serial block-face scanning electron microscope (SBF-SEM), and conventional and high-voltage transmission electron microscopes. Based on the serial images of 352 cross sections of SBF-SEM, we reconstructed a 3D model of the sensillum revealing that each S.

大型放射光施設“SPring-8”は,国内外の産官学に開かれた共同利用施設であり,幅広い分野の研究開発に利用されている。本施設のリソースには限りがあることから,成果に基づいた施設運用が求められる。そのため,成果が増加・減少する研究領域の把握は,施設運用の方向性を考えるために重要である。研究成果は論文として公表されるケースが大半であるが,論文化には実験後2,3年を要する場合が多く,即時的な把握は困難という問題がある。そこで本論文では,研究施設の運営支援に活用することを目的に,実験期終了後3年経過時点の成果公開状況を事前に予測するモデルを構築した。その結果,相関係数0.937で予測できることを確認した。