多数の自律エージェントからなるmulti-agent system (MAS)で柔軟に協調動作を組織するには，適切な位置に適切な分量の個体を配置して適切な動作を分担させる必要がある．筆者らは，拡散信号を用いて間接通信するMASにおいて，エージェントの行動則を逐次修正しながら協調動作を設計する方法を提案した．本論文では，既存の分別収集タスクモデル(DeneubourgのAnt-like Robot, ALR)を題材に取り上げ，上記方法を適用してALRを改良した．具体的にはALRのエージェントの行動則に「拡散信号への誘引動作」と「信号不応期のランダム移動」を付け加えて，信号拡散システムを新たに構築した．この信号拡散システムは反応拡散機構に従って動作し，ALRと比べて高い分別収集効率を示す．
Many users are attracted by online social media such as Delicious and Digg, and they put tags on online resources. Relations among users, tags, and resources are represented as a tripartite network composed of three types of vertices. Detecting communities (densely connected subnetworks) from such tripartite networks is important for finding similar users, tags, and resources. For unipartite networks, several attempts have been made for detecting communities, and one of the popular approaches is to optimize modularity, a measurement for evaluating the goodness of network divisions. Modularity for bipartite networks is proposed by Barber, Guimera, Murata and Suzuki. However, as far as the author knows, there is few attempt for defining modularity for tripartite networks. This paper defines a new tripartite modularity which indicates the correspondence between communities of three vertex types. By optimizing the value of our tripartite modularity, better community structures can be detected from synthetic tripartite networks.