For frequently data arriving data environment, this paper tackles the following three problems. (1) Maximizing throughput. (2) Minimizing logging time. (3) Minimizing blocking time. To solve these problems, this paper proposes Two Level Asynchronous Checkpointing technique. Furthermore this paper designs and implements the technique within DBMS and experiments are conducted by using the DBMS to evaluate the technique. The result of experiments show that remote logging provides better performance than disk logging, 10 times for (1), 17 times for (2). Furthermore, average blocking time is shown as 4.38 micro seconds.

When describing a physical object, we indicate which object by pointing and using reference terms, such as ‘this’ and ‘that’, to inform the listener quickly of an indicated object's location. Therefore, this research proposes using a three-layer attention-drawing model for humanoid robots that incorporates such gestures and verbal cues. The proposed three-layer model consists of three sub-models: the Reference Term Model (RTM); the Limit Distance Model (LDM); and the Object Property Model (OPM). The RTM selects an appropriate reference term for distance, based on a quantitative analysis of human behaviour. The LDM decides whether to use a property of the object, such as colour, as an additional term for distinguishing the object from its neighbours. The OPM determines which property should be used for this additional reference. Based on this concept, an attention-drawing system was developed for a communication robot named ‘Robovie’, and its effectiveness was tested.

Previous research works in robotics and cognitive science have reported that humans utilize embodied cooperative behaviors in communication, such as nodding in response to another's it utterance and looking at a certain object in a certain direction as others look or point at. We have developed a humanoid robot that utilizes such an embodied cooperative behavior for natural communication in a route guidance situation. It obtains numerical data on a human's body movement via a motion capturing system and then autonomously selects appropriate cooperative embodiment units from 18 implemented units. Each unit realizes a certain cooperative embodiment behaviors such as eye-contact by using the motion capturing system as well. As a result of a subject experiment, we have verified the effectiveness of the embodied cooperative behaviors of the robot for reliable and sympathetic communication. Moreover, we analyzed how the auditory expression and the embodiment contributed to the effect.

This paper proposes a scheme to recover the state of peripheral devices in non-volatile main memory systems so that the system resumes its execution after an unpredictable power failure. Our scheme is software-based approach and accomplishes the recoverable system without complex hardware equipments. First, the requirements for maintaining consistency are discussed based on the concepts of distributed message-passing systems. Next, the application to a device driver is illustrated using an example. Experiments with UART and network devices showed that the system restarted properly after a power failure, and that system performance was barely decreased.

This paper proposes a network system and an algorithm for a rescue robot to obtain its position under collapsed area. The network system consists of communication tags put dynamically by the rescue robot in its rescue activities. According to the temporary tags, the system constructs temporary communication infrastructure and obtains geometrical information of the area. In particular, to get the position of the rescue robot, our algorithm employs "angle" obtained from Omni-directional Sensor mounted on the communication tag. The use of the "angle" information leads a significant decrease in the error in estimating tags' location. In this paper, the feasibility of our system and algorithm is confirmed with the simulation.

This study investigated what effect physical constraints have on the interpretation of demonstrative pronouns when a user navigates a robot. For this investigation, a robot navigation environment called Spondia-II was develope, and an experiment conducted. It is known that the interpretation of demonstrative pronouns requires information about not only the situation (or context) but also the speaker's viewpoint during a dialogue. The results of the experiment suggest that physical constraints do affect the user's viewpoint, especially when a user utters a demonstrative pronoun while navigating the robot. In actual fact, the user alters the use of demonstrative pronouns according to the change in the user's viewpoint. It is also suggested that the user and the robot share the same viewpoint during the physical interaction.

Sensor database systems need to provide both freshness of data and persistence to the incoming sensor streams. To provide persistence, a disk based logging method has been widely used, however it is not applicable for sensor streams because of its tardiness. In this paper, we propose the light neighbor write ahead logging protocol(L-WAL) for sensor streams. The L-WAL is a refinement of the neighbor-WAL (N-WAL). The L-WAL needs two network interfaces and applies a relaxed protocol rather than a two phase commit protocol. Since the relaxed protocol weakens the guarantee of logging successes, we have incorporated a repair system and checker system to enhance the guarantee. The result of experiments shows that the L-WAL is about 2.13 times faster than the N-WAL when the number of concurrent sensor streams is 250 and the L-WAL can enhance the persistence of data almost for free, while the N-WAL needs to pay high cost.

This paper describes communication load reduction on task negotiation with contract net protocol (CNP) for multiple autonomous mobile robots. For controlling multiple robots, CNP is useful, but the broadcast of task announcement messages on CNP tends to consume much communication load. In order to overcome this problem, the authors have developed a system called LEMMING which learns proper addresses for the task announcement messages with case-based reasoning. However, the learning method used in LEMMING sometimes caused inefficient task execution. In this paper, we propose an extension of LEMMING with message interception which enables the system to execute tasks more efficiently by eavesdropping on leaked message.

Intelligent robots need to share knowledge with human beings for flexible interaction. However, the gap between low‐level sensory data and abstract human knowledge makes it difficult to preencode robot behavior against human's various complex demands. This article presents a way of enabling robots to learn abstract concepts from sensory and perceptual data. In order to overcome the gap between the low‐level sensory data and higher level concept description, a method called feature abstraction is used. Feature abstraction dynamically defines abstract sensors from primitive sensory devices and makes it possible to learn appropriate sensory‐motor constraints. This method has been implemented on a real mobile robot as a learning system called Acorn‐II. Acorn‐II was evaluated with some empirical results and it was shown that the system can learn some abstract concepts more accurately than other existing systems.

In the near future, robots used by human-like personal computers will appear in office or at home. In this paper, we call these robots personal robots. A personal robot can be thought as a small autonomous mobile robot. First, we discuss a user interface design for personal robots in the face-to-face situation. Second, we consider the features of personal robots and their environments, and propose a new user interface concept for a personal robot an active interface. To show the effectiveness of the active interface, we design and implement a speech dialogue system called Chaser for human-robot interaction based on the active interface.

Describes LEMMING, a learning system for multiple mobile robot environments. LEMMING extends the idea of the broadcast-based contract net protocol with CBR (case-based reasoning). In terms of the CBR mechanism, LEMMING can learn to select appropriate robots for a given task. Thus, LEMMING makes it possible to reduce the waste of communication resources and the trouble of processing irrelevant messages. The paper evaluates LEMMING with some experiments and shows that LEMMING can deal with task negotiation more effectively than a broadcast-based contract net system.

One of the important factors required to realize personal robots is mutual cooperation between humans and robots. To support human cooperative work based on asynchronous communication, the authors had developed a groupware toolkit called Michele. They propose a multi-agent interface architecture called RT-Michele. A prototype of RT-Michele was implemented, and autonomous mobile robots were developed. RT-Michele was applied to office automation, where it provided not only asynchronous communication, but also real-time interactive communication for cooperative work among humans and mobile robots.>