Tamami Sugasaka

Designing ubiquitous shopping support systems based on human-centered approach

We introduce our human-centered approach for the purpose of developing a ubiquitous computing system aiming at providing better experiences for shoppers at a supermarket. We focus on shopping processes by using ethnographic research techniques, understand the process with details, and construct TPM which classifies a shoppers behaviors and states of mind change into three phases. We also describe our concept design of service types for a prototype system and deal with allocation and configuration of the service types corresponding to TPM.

Mobile robot control by neural networks using self-supervised learning

A reinforcement learning algorithm based on supervised learning is described. It uses associative search to discover and learn actions that make the system perform a desired task. One problem with associative search is that the systems actions are often inconsistent. In the searching process, the systems actions are always decided stochastically, so the system cannot perform learned actions more than once, even if they have been determined to be suitable actions for the desired task. To solve this problem, a neural network that can predict an evaluation of an action and control the influence of the stochastic element is used. Results from computer simulations using the algorithms to control a mobile robot are described. >

Exploring Everyday Activities for Pervasive Decision-Aid

In this paper, we explain the process of establishing shoppers activity-models based on a series of user-researches as the basis of pervasive systems for a supermarket. Pervasive systems have been recognized as the technologies which enable users decision-aid in his/her everyday-activities. For instance, a smart travel navigation system, which employs embedded and wearable devices and mobile agent technologies, was proposed as a promising information system for the society: it renders complex tasks into simple subtasks including providing adequate information regarding transit to the other train at an arbitrary station for visually-impaired person. Few researches on analysis of users everyday-activities for the systems design, however, have been conducted. We believe important to examine users everyday-activities as well as to develop elemental technologies of pervasive systems simultaneously, which will become a powerful way of solving a variety of real-world problems. An important knowledge regarding the model is that a shopper gradually elaborates vague plans primarily conceived at off the store into final decision-making at checkouts, instead of buying items according to well-defined plan as well as just on impulse. We regarded that the system dynamically adapting to these shoppers contexts is very different from other shoppers decision-aid systems

An agent-based system for electronic commerce using recipes

The paper proposes an agent-based system, called SAGE:Francis, to search a commodity or sets of commodities for electronic commerce. This system has the following functions: (1) seamlessly integrating information about commodities distributed over networks by conversational agents; (2) searching for a commodity required by compound conditions; and (3) creating a menu for a search for sets of commodities from legacy knowledge such as cooking recipes. The paper describes the system configuration, interface between agents, and experimental results. As a result, SAGE:Francis successfully supports users when searching for sets of commodities by recipe-based menus and integrated information about commodities stored in databases over networks by conversational agents.

Application of neural networks for self-supervised learning

The learning method of layered neural networks can be supervised or unsupervised. Back propagation learning algorithm is a common method of supervised learning that can learn automatically from teaching patterns. However, accurate teaching patterns are not always available for robotic applications and it is necessary to devise a method of producing them. In this paper, two applications of neural network for self-supervised learning are described. One is a system for which a mobile robot learns its behavior by automatically generating and self- evaluating teaching data through a random walk. The other is a control method of an inverted pendulum using a knowledge-based neural network. The system collects the state data of the inverted pendulum such as angles and angular velocities by trial and error. After that, the system generates teaching data by comparing the collected data with stored knowledge. This knowledge expresses the ideal status of the inverted pendulum when it inverts. The system learns from the generated teaching data and the pendulum inverts stably after some trial and error. In both systems, the neural network learns the teaching data that is generated by the system itself.

Control of an inverted pendulum by a neural network with self-supervised learning

Summary form only given, as follows. The authors propose an adaptive self-supervised learning system based on a neural network with supervised learning. The adaptive system learns the desired task autonomously. Although this system, like many adaptive learning systems, uses trial and error, experience rules are implemented into the system as an equation so that the system can effectively generate training data based on the experience rules during trial and error and train the neural network controlling the system itself via supervised learning. The authors discuss control of an inverted pendulum to show how the adaptive system is used. The system was able to invert the pendulum stably at the target position.<<ETX>>