Planning and execution through variable resolution planning

Generating sequences of actions–plans–for robots using Automated Planning in stochastic and dynamic environments has been shown to be a difficult task with high computational complexity. These plans are composed of actions whose execution might fail due to different reasons. In many cases, if the execution of an action fails, it prevents the execution of some (or all) of the remainder actions in the plan. Therefore, in most real-world scenarios computing a complete and sound (valid) plan at each (re-)planning step is not worth the computational resources and time required to generate the plan. This is specially true given the high probability of plan execution failure. Besides, in many real-world environments, plans must be generated fast, both at the start of the execution and after every execution failure. In this paper, we present Variable Resolution Planning which uses Automated Planning to quickly compute a reasonable (not necessarily sound) plan. Our approach computes an abstract representation–removing some information from the planning task–which is used once a search depth of $k$ steps has been reached. Thus, our approach generates a plan where the first $k$ actions are applicable if the domain is stationary and deterministic, while the rest of the plan might not be necessarily applicable. The advantages of this approach are that it: is faster than regular full-fledged planning (both in the probabilistic or deterministic settings); does not spend much time on the far future actions that probably will not be executed, since in most cases it will need to replan before executing the end of the plan; and takes into account some information of the far future, as an improvement over pure reactive systems. We present experimental results on different robotics domains that simulate tasks on stochastic environments.     

对规划理论研究现状的分析

1 引言现在不少人工智能(AI)研究者认为定义人工智能的一种方法就是将它看作是以构建具有智能行为的agent为目标的研究领域。从这种观点看,“agent”实际上就是人工智能的核心。自从80年代后期以来,关于agent理论及应用的研究取得了很大发展,目前agent是主流计算机科学,包括数据通讯、并行系统、机器人、用户接口设计等等的研究人员所讨论的一个主题。构建agent的经典方法是将它们看作特殊类型的基于知识的系统,这就是通常的符号方法,而相应的a-gent则被称为慎思的agent。一个慎思的agent包含对环境的确切描述的符号模型,并且其决策(例如应采取什么行动)都是基于模式识别或符号处理,通过逻辑推     

Distributed planning and control systems for the virtual enterprise: organizational requirements and development life-cycle

This paper describes the requirements analysis and system specification of an Order Promise module to be used as part of a broader Decision Support System for production and operations planning of a Virtual Enterprise. This work is part of a broader project with a particular focus on the microelectronics industry which is a good example of Virtual Enterprise, and where a quick response to the customers needs and to unpredictable changes in production conditions is considered a major factor for success. First, the analysis and specification are presented within a development framework that involves the study of organizational issues of semiconductor enterprises. The use of ontological engineering for supporting the communication and shared understanding of the system concepts is explained and a virtual enterprise ontology is outlined. Following the clarification of the concept of virtual enterprise, the generic techno-organizational requirements for the information system are derived. Finally, a specification of the global planning module and a more detailed one regarding the order promise module is presented.     

客户关系管理

客房关系管理（CRM）是世界各地主要企业正在讨论的一个热点问题。随着现代企业信息化管理的不断发展,实行有效的CRM策略对企业的生存和发展显得越来越重要。本文介绍了客户关系管理的系统组成、功能、作用、技术支持等方面的内容,讨论了它的数据处理过程及未来的发展。     

Cooperation of categorical and behavioral learning in a practical solution to the abstraction problem

Real robots should be able to adapt autonomously to various environments in order to go on executing their tasks without breaking down. They achieve this by learning how to abstract only useful information from a huge amount of information in the environment while executing their tasks. This paper proposes a new architecture which performs categorical learning and behavioral learning in parallel with task execution. We call the architectureSituation Transition Network System (STNS). In categorical learning, it makes a flexible state representation and modifies it according to the results of behaviors. Behavioral learning is reinforcement learning on the state representation. Simulation results have shown that this architecture is able to learn efficiently and adapt to unexpected changes of the environment autonomously. Atsushi Ueno, Ph.D.: He is a research associate in the Artificial Intelligence Laboratory at the Graduate School of Information Science at the Nara Institute of Science and Technology (NAIST). He received the B.E., the M.E., and the Ph.D. degrees in aeronautics and astronautics from the University of Tokyo in 1991, 1993, and 1997 respectively. His research interest is robot learning and autonomous systems. He is a member of Japan Association for Artificial Intelligence (JSAI). Hideaki Takeda, Ph.D.: He is an associate professor in the Artificial Intelligence Laboratory at the Graduate School of Information Science at the Nara Institute of Science and Technology (NAIST). He received his Ph.D. in precision machinery engineering from the University of Tokyo in 1991. He has conducted research on a theory of intelligent computer-aided design systems, in particular experimental study and logical formalization of engineering design. He is also interested in multiagent architectures and ontologies for knowledge base systems.     

Testing Planning Domains (without Model Checkers)

We address the problem of verifying planning domains as used in model-based planning, for example in space missions. We propose a methodology for testing flight rules of planning domains which is self-contained, in the sense that flight rules are verified using a planner and no external tools are required. We review and analyse coverage conditions for requirements-based testing, and we reason in detail on "Unique First Cause" (UFC) coverage for test suites. We characterise flight rules using patterns, encoded using LTL, and we provide UFC coverage for them. We then present a translation of LTL formulae into planning goals, and illustrate our approach on a case study.     

基于Web和XML的CAPP系统

介绍了一种基于Web和XML的CAPP系统,主要包括系统的体系结构、系统的信息流程、基于XML工艺文档表示、工艺规则对XML文档的操作、XML文档的客户端处理。在网络环境中,客户端与服务器端利用XML进行信息交互,提高了Web环境下CAPP系统的性能,满足了企业工艺设计的需求,也使B/S模式下的应用系统面临的诸多问题得到很好的解决。     

Effects of turning gait parameters on energy consumption and stability of a six-legged walking robot

Minimization of energy consumption plays a key role in the locomotion of a multi-legged robot used for various purposes. Turning gaits are the most general and important factors for omni-directional walking of a six-legged robot. This paper presents an analysis on energy consumption of a six-legged robot during its turning motion over a flat terrain. An energy consumption model is developed for statically stable wave gaits in order to minimize dissipating energy for optimal feet forces distributions. The effects of gait parameters, namely angular velocity, angular stroke and duty factors are studied on energy consumption, as the six-legged robot walks along a circular path of constant radius with wave gait. The variations of average power consumption and energy consumption per unit weight per unit traveled length with the angular velocity and angular stroke are compared for the turning gaits of a robot with four different duty factors. Computer simulations show that wave gait with a low duty factor is more energy-efficient compared to that with a high duty factor at the highest possible angular velocity. A stability analysis based on normalized energy stability margin is performed for turning motion of the robot with four duty factors for different angular strokes.