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Hideyuki Nakashima

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7 papers
1 author row

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7

IJCAI Conference 2007 Conference Paper

  • Yutaka Matsuo
  • Naoaki Okazaki
  • Kiyoshi Izumi
  • Yoshiyuki Nakamura
  • Takuichi Nishimura
  • Koiti Hasida
  • Hideyuki Nakashima

Recent development of location technologies enables us to obtain the location history of users. paper proposes a new method to infer users' longterm properties from their respective location histories. Counting the instances of sensor detection every user, we can obtain a sensor-user matrix. After generating features from the matrix, a machine learning approach is taken to automatically users into different categories for each user property. Inspired by information retrieval research, problem to infer user properties is reduced categorization problem. We compare weightings of several features and also propose sensor weighting. Our algorithms are evaluated using experimental location data in an office environment.

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AAMAS Conference 2007 Conference Paper

Emergence of Global Network Property based on Multi-agent Voting Model

  • Kosuke Shinoda
  • Yutaka Matsuo
  • Hideyuki Nakashima

Recent studies have shown that various models can explain the emergence of complex networks, such as scale-free and small-world networks. This paper presents a different model to generate complex networks using a multi-agent approach. Each node is considered as an agent. Based on voting by all agents, edges are added repeatedly. We use four different kinds of centrality measures as a utility functions for agents. Depending on the centrality measure, the resultant networks differ considerably: typically, closeness centrality generates a scale-free network, degree centrality produces a random graph, betweenness centrality favors a regular graph, and eigenvector centrality brings a complete subgraph. The importance of the network structure among agents is widely noted in the multi-agent research literature. This paper contributes new insights into the connection between agents' local behavior and the global property of the network structure. We describe a detailed analysis on why these structures emerge, and present a discussion of the possible expansion and application of the model.

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AIJ Journal 1997 Journal Article

Causality as a key to the frame problem

  • Hideyuki Nakashima
  • Hitoshi Matsubara
  • Ichiro Osawa

In a formal description of actions and changes, there is a famous problem known as the “frame problem”. In its generalized form, the frame problem is that it is practically impossible to describe, or infer, all the necessary preconditions (qualification problem) and all the possible consequences of a given action (ramification problem). It is not only difficult to determine what changes and what does not change. It is also difficult to determine what is relevant. Our observations show that even humans cannot solve the frame problem. Humans simply behave as if there were no such problem. The solution is to use some kind of heuristics. We claim that the notion of causality is the heuristics necessary to solve the frame problem. The most important working hypothesis is that causality is not a logical relation. As one of the heuristics it is subject to change as more information becomes available. There are two kinds of nonmonotonicity in the reasoning of changes: one due to a lack of information, the other due to change itself. We need separate mechanisms for them because they are used in combination to solve the frame problem.

Details DOI
v2026.09.13