SUMMARY Intelligent service robots provide various services to users by understanding the context and goals of a user task. In order to provide more reliable services, intelligent service robots need to consider various factors, such as their surrounding environments, users' changing needs, and constrained resources. To handle these factors, most of the intelligent service robots are controlled by a task‐based control system, which generates a task plan that represents a sequence of actions, and executes those actions by invoking the corresponding functions. However, the traditional task‐based control systems lack the consideration of resource factors even though intelligent service robots have limited resources (limited computational power, memory space, and network bandwidth). Moreover, system‐specific concerns such as the relationships among functional modules are not considered during the task generation phase. Without considering both the resource conditions and interdependencies among software modules as a whole, it will be difficult to efficiently manage the functionalities that are essential to provide core services to users. In this paper, we propose a mechanism for intelligent service robots to efficiently use their resources on‐demand by separating system‐specific information from task generation. We have defined a sub‐architecture that corresponds to each action of a task plan, and provides a way of using the limited resources by minimizing redundant software components and maintaining essential components for the current action. To support the optimization of resource consumption, we have developed a two‐phase optimization process, which is composed of the topological and temporal optimization steps. We have conducted an experiment with these mechanisms for an infotainment robot, and simulated the optimization process. Results show that our approach contributed to increase the utilization rate by 20% of the robot resources. Copyright © 2011 John Wiley & Sons, Ltd.
Open Source Software (OSS) has become an important environment where developers can create, exchange, and improve reusable software assets by collaborating with other developers. Although developers may find useful software assets to reuse from OSS for their projects, they usually experience difficulties in solving problems that occur while integrating the assets to their own software. We investigated data from major open source environments such as Sourceforge.net and GitHub, and learned that there is a common pattern of solving reuse-related problems in OSS. To analyze the pattern in detail, we have developed an ontological model to formally represent the symptoms and causes of the reuse-related problems, and the correlations between them. Based on this model, we collected data from Sourceforge.net, and built a knowledge base for the most common problem type. We extracted the core types of symptoms and causes for the problem type and calculated the number of correlations between the types of symptoms and causes. We found that there exist correlations between the symptoms and causes that are extracted from the discussion threads for the problem type, and about 60% of them are statistically significant. We also conducted a study to understand the effective timing of recommending solutions to the developers by analyzing the recall rates of finding the causes of the problems in a timeline. We figured that most of the important causes of a problem are discussed at the beginning of the forum discussion. This leads us to the conclusion that recommending the causes of a problem early by using our knowledge framework may help developers spend less amount of time to solve the problem (around 50% less time than solving the problem without using our framework).
Self-growing software is a software system that grows its functionalities and configurations by itself based on dynamically monitored situations. Self-growing software is especially necessary for intelligent service robots, which monitor their surrounding environments and provide appropriate behaviors for human users. Intelligent service robots often face problems that cannot be resolved with the conventional software technology. To support self-growing software for intelligent service robots, the SemBots project at ICU is developing a repository framework that allows robot software to dynamically acquire software components that are necessary to resolve a dynamic situation. In this paper, we describe the requirements and architecture of the repository system for self-growing software. We also present a prototype implementation of the repository system.Proceedings of the 12th Asia-Pacific Software Engineering Conference (APSEC'05) 0-7695-2465-6/05 $20.00 © 2005 IEEE
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