Masanori Akiyoshi

Agent-based decision support framework for asynchronous negotiation of resource transfer

Addresses an agent-based framework that supports decision making for resource transfer planning among distributed organizations. When resource transfer organizations, categorized into either supply or demand sites, form a temporary negotiation group on the network, negotiation-based commitment is necessary to reach mutual agreement. However, the negotiation process usually involves complicated procedures such as proposal, selection, reply, and cancellation. Furthermore, in asynchronous negotiation, it is quite difficult when to finalize the negotiation procedure under a self-interested environment. We investigate such a negotiation process and design a negotiation agent to support a human manager. We also apply our framework to a construction waste recycling problem and discuss the division of roles between a human manager and a negotiation agent.

Toward generating causal explanation: qualitative simulation with association mechanism to quantitative information

One of the central issues of qualitative reasoning is generating causal explanation in response to a users query and the model construction. Constructing an adequate model is crucial in generating explanation in case of large-scale physical systems. This paper describes new mechanisms in the model construction environment, using the causal ordering algorithm and qualitative conversion of numerical data. Our approach involves two key ideas: a qualitative model is constructed from a unique quantitative model, and simulated qualitative behaviors are compared with numerical data. The result of applying the mechanisms to power plants is discussed.<<ETX>>

A computer-based supporting environment for causal understanding of large scale systems by hierarchical qualitative simulation

A framework for a computer-based training system to support acquiring deep understanding of large-scale systems through the process of forming mental models is discussed. The system provides the following functions: (1) support for constructing a simulation model based on a physical hierarchy of devices according to a users viewpoints, (2) support for using qualitative representation of physical parameters, and (3) support for acquiring causal information through qualitative simulation. A repetitive and transparent process for acquiring understanding is the characteristic of this supporting environment. The design concept is presented, and its implementation on a computer,including the user interface, is described. The use of the system to treat the phenomenon of step load decrease is reported.<<ETX>>

Cellular phone applied systems with interaction by figures on diagrams

The purpose of this research is to provide a system framework that enables users to interact with systems by icons, using cellular phones as user interface devices. The response of the interaction of the system must be quick enough to satisfy users under such limitations as data transmission speed and performance. This work describes an application framework that separates interaction logic into a server and a client. To reduce the amount of client-side logic and transmission data, the interaction logic of both server-side and client-side cooperate to achieve users operation using a simple script. A prototype system has been developed and evaluated. The client program immediately updates the screen when a user handles icons. The number of requests to the server is reduced 30%, and it takes less than 9 seconds to display typical contents, good enough for practical use.

Towards generating causal explanation: qualitative simulation with association mechanism to quantitative information

One of the central issues of qualitative reasoning is generating causal explanation in response to a users query and the model construction. Constructing an adequate model is crucial in generating explanation in case of large-scale physical systems. This paper describes new mechanisms in the model construction environment, using the causal ordering algorithm and qualitative conversion of numerical data. Our approach involves two key ideas: a qualitative model is constructed from a unique quantitative model, and simulated qualitative behaviors are compared with numerical data. The result of applying the mechanisms to power plants is discussed. >