Proving the Correctness of the Standardized Algorithm for ABR Conformance

Conformance control for ATM cells is based on a real-time reactive algorithm which delivers a value depending on inputs from the network. This value must always fit with a well defined theoretical value. We present here the correctness proof of the algorithm standardized for the ATM transfer capability called ABR. The proof turned out to produce a key argument during the standardization process of ABR.     

面向使命任务的武器装备体系能力规划方法

武器装备体系(weapon system of systems,WSoS)能力规划是武器装备体系研究的重要内容.履行使命任务时,可出动的兵力以及携行可支撑能力的武器装备数量是有限的,对此,提出一种基于效能价值的武器装备体系能力规划方法.统筹考虑作战中各项任务能力需求,结合支撑不同能力的武器装备与相应装备数量之间的关系,快速给出不同规模部队的能力规划方案.以使命任务为牵引,分析能力需求,建立归一化作战能力效能模型;基于前景理论(prospect theory),给出考虑风险因素的作战能力效能价值函数;以装备单位数量为约束变量,构建基于效能价值的体系能力规划模型,对其求解可得出使体系效能价值最大的武器装备能力规划方案.最后,以武警部队担负的反恐处突作战任务为例,验证所提出方法的可行性和有效性.     

The Use of Proof Planning for Co-operative Theorem Proving

We describebarnacle: a co-operative interface to theclaminductive theorem proving system. For the foreseeable future, there will be theorems which cannot be proved completely automatically, so the ability to allow human intervention is desirable; for this intervention to be productive the problem of orienting the user in the proof attempt must be overcome. There are many semi-automatic theorem provers: we call our style of theorem provingco-operative, in that the skills of both human and automaton are used each to their best advantage, and used together may find a proof where other methods fail. The co-operative nature of thebarnacleinterface is made possible by the proof planning technique underpinningclam. Our claim is that proof planning makes new kinds of user interaction possible.Proof planning is a technique for guiding the search for a proof in automatic theorem proving. Common patterns of reasoning in proofs are identified and represented computationally as proof plans, which can then be used to guide the search for proofs of new conjectures. We have harnessed the explanatory power of proof planning to enable the user to understand where the automatic prover got to and why it is stuck. A user can analyse the failed proof in terms ofclam's specification language, and hence override the prover to force or prevent the application of a tactic, or discover a proof patch. This patch might be to apply further rules or tactics to bridge the gap between the effects of previous tactics and the preconditions needed by a currently inapplicable tactic.     

On the Comparison of Proof Planning Systems: , Ωmega and IsaPlanner

We present a framework for describing proof planners. This framework is based around a decomposition of proof planners into planning states, proof language, proof plans, proof methods, proof revision, proof control and planning algorithms.We use this framework to motivate the comparison of three recent proof planning systems, λCLaM, Ωmega and IsaPlanner, and demonstrate how the framework allows us to discuss and illustrate both their similarities and differences in a consistent fashion. This analysis reveals that proof control and the use of contextual information in planning states are key areas in need of further investigation.     

Mechanically verifying concurrent programs with the Boyer-Mooreprover

A proof system suitable for the mechanical verification of concurrent programs is described. This proof system is based on Unity, and may be used to specify and verify both safety and liveness properties. However, it is defined with respect to an operational semantics of the transition system model of concurrency. Proof rules are simply theorems of this operational semantics. This methodology makes a clear distinction between the theorems in the proof system and the logical inference rules and syntax which define the underlying logic. Since this proof system essentially encodes Unity in another sound logic, and this encoding has been mechanically verified, this encoding proves the soundness of this formalization of Unity. This proof system has been mechanically verified by the Boyer-Moore prover. This proof system has been used to mechanically verify the correctness of a distributed algorithm that computes the minimum node value in a tree     

USV&AUV水下目标协同搜索与打击航迹规划

异构载运工具合作执行复杂任务是提高任务完成效率的有效手段,已被广泛应用于军事与民用任务.关注对水下目标的搜索与打击任务,此项任务由水面航行器(unmanned surface vehicle, USV)与水下航行器(autonomous underwater vehicle, AUV)合作完成.首先描述USV&AUV系统的框架以及USV与AUV在系统中发挥的作用.在协同航迹规划模型中,整个任务被划分为目标搜索和目标打击两个阶段,两个阶段的目标分别是使得搜索空间最大和AUV与目标的末端位置误差最小.模型中的约束包括USV和AUV的机动能力、目标探测能力、通讯范围等.在协同策略的设计中,针对该任务两个阶段的特点,分别基于随机模拟实验和异步规划策略设计协同航迹规划算法,以集中式和分布式模式分别生成两阶段的航迹.仿真结果表明, USV&AUV系统能够在不同的条件下完成对水下目标的搜索与打击任务.在目标打击阶段,与集中式规划结构相比,分布式规划结构能够应对各种复杂的场景并成功完成任务.     

Constraint Solving for Proof Planning

Proof planning is an application of AI planning to theorem proving that employs plan operators that encapsulate mathematical proof techniques. Many proofs require the instantiation of variables; that is, mathematical objects with certain properties have to be constructed. This is particularly difficult for automated theorem provers if the instantiations have to satisfy requirements specific for a mathematical theory, for example, for finite sets or for real numbers, because in this case unification is insufficient for finding a proper instantiation. Often, constraint solving can be employed for this task. We describe a framework for the integration of constraint solving into proof planning that combines proof planners and stand-alone constraint solvers. Proof planning has some peculiar requirements that are not met by any off-the-shelf constraint-solving system. Therefore, we extended an existing propagation-based constraint solver in a generic way. This approach generalizes previous work on tackling the problem. It provides a more principled way and employs existing AI technology.     

非时间参考的机器人路径规划与控制方法

本文面向非结构化环境,针对传统的基于时间的规划方法的不足,提出了一种非时间参考的 机器人路径规划方法;并根据该规划的特点,设计了相应的控制算法．在这种规划方法下, 机器人遇到障碍时能自动停止运动,而当障碍物被清除后,又能沿以前的规划继续运动,避 免了系统所受到的损害和任务的重规划,提高了系统处理不确定事件的能力．     

Strategic context and patterns of IT infrastructure capability

The importance of a firm's information technology (IT) infrastructure capability is increasingly recognised as critical to firm competitiveness. Infrastructure is particularly important for firms in industries going through dynamic change, for firms reengineering their business processes and for those with multiple business units or extensive international or geographically dispersed operations. However, the notion of IT infrastructure is still evolving and there has been little empirically based research on the patterns of IT infrastructure capability across firms.We develop the concept of IT infrastructure capability through identification of IT infrastructure services and measurement of reach and range in large, multi-business unit firms. Using empirical case research, we examine the patterns of IT infrastructure capability in 26 firms with diverse strategic contexts, including different industry bases, level of marketplace volatility, extent of business unit synergies and the nature of firm strategy formation processes. Data collection was based on a combination of quantitative and qualitative methods with multiple participants.More extensive IT infrastructure capability is defined as a combination of more IT infrastructure services and more reach and range. More extensive IT infrastructure capability was found in firms where: (i) products changed quickly; (ii) attempts were made to identify and capture synergies across business units; (iii) there was greater integration of information and IT needs as part of planning processes; and (iv) there was greater emphasis on tracking the implementation of long term strategy. These findings have implications for both business and technology managers particularly in regard to how firms link strategy and IT infrastructure formation processes.     

面向区域侦察的实拍实传卫星规划推演系统

为支持多颗实拍实传卫星区域侦察任务,研制规划推演系统.设计了规划推演系统的总体结构和信息流程,并研究了系统研制所需关键技术,分别提出了面向区域侦察的数传任务规划算法、基于网格的卫星区域覆盖分析算法、基于OSGEarth的目标区域和侦察范围组合可视化方法.研制的规划推演系统已成功应用于某任务中,进行数传任务规划、能力分析和过程推演.     

Making a productive use of failure to generate witnesses for coinduction from divergent proof attempts

Coinduction is a proof rule. It is the dual of induction. It allows reasoning about non-well-founded structures such as lazy lists or streams and is of particular use for reasoning about equivalences. A central difficulty in the automation of coinductive proof is the choice of a relation (called a bisimulation). We present an automation of coinductive theorem proving. This automation is based on the idea of proof planning [7]. Proof planning constructs the higher level steps in a proof, using knowledge of the general structure of a family of proofs and exploiting this knowledge to control the proof search. Part of proof planning involves the use of failure information to modify the plan by the use of a proof critic [23] which exploits the information gained from the failed proof attempt. Our approach to the problem was to develop a strategy that makes an initial simple guess at a bisimulation and then uses generalisation techniques, motivated by a critic, to refine this guess, so that a larger class of coinductive problems can be automatically verified. The implementation of this strategy has focused on the use of coinduction to prove the equivalence of programs in a small lazy functional language which is similar to Haskell [22]. We have developed a proof plan for coinduction and a critic associated with this proof plan. These have been implemented in CoCLAM, an extended version of CLAM [9], with encouraging results. The planner has been successfully tested on a number of theorems. This revised version was published online in June 2006 with corrections to the Cover Date.     

Combining Proof Plans with Partial Order Planning for Imperative Program Synthesis

The structured programming literature provides methods and a wealth of heuristic knowledge for guiding the construction of provably correct imperative programs. We investigate these methods and heuristics as a basis for mechanizing program synthesis. Our approach combines proof planning with conventional partial order planning. Proof planning is an automated theorem proving technique which uses high-level proof plans to guide the search for proofs. Proof plans are structured in terms of proof methods, which encapsulate heuristics for guiding proof search. We demonstrate that proof planning provides a local perspective on the synthesis task. In particular, we show that proof methods can be extended to represent heuristics for guiding program construction. Partial order planning complements proof planning by providing a global perspective on the synthesis task. This means that it allows us to reason about the order in which program fragments are composed. Our hybrid approach has been implemented in a semi-automatic system called Bertha. Bertha supports partial correctness and has been tested on a wide range of non-trivial programming examples.     

基于证书的抗泄露的安全加密方案

近期实践表明密码系统容易受到各种攻击而泄漏密钥等相关秘密信息, 泄漏的秘密信息破坏了以前的已证明安全的方案, 因此设计抗泄漏的密码学方案是当前密码研究领域的一个热点研究方向。设计一个基于证书的加密方案, 总的设计思想是使用一个基于证书的哈希证明系统, 这个证明系统包含一个密钥封装算法, 用这个密钥封装算法结合一个提取器去加密一个对称加密所用的密钥, 那么得到的加密方案就是可以抵抗熵泄漏并且是安全的。对方案的安全性分析和抗泄漏性能分析, 表明本方案在抵抗一定量的密钥泄漏和熵泄漏时可以保持安全性。     

A new deterministic approach in a decision support system for ship’s trajectory planning

The paper introduces a Decision Support System for ships, developed to solve a problem of collision avoidance with static and dynamic obstacles. The system maps the decision making capability of a human (navigation) expert to solve the path planning problem for a ship in a complex navigation environment. It can be further developed to provide automatic control of a ship. It utilizes a new, fast and effective, deterministic method, called the Trajectory Base Algorithm, to calculate a safe, optimal path for a ship. The system structure, a detailed explanation of a new method, followed by results of simulation tests are all presented in the paper. The results proof a successful application of the method to solve a path planning problem for ships with the consideration of both static and dynamic obstacles in the environment, marine traffic regulations and dynamic properties of a ship, what makes this approach applicable in commercial systems. The approach can also be adapted for application in mobile robots path planning. The experimental results and ability of the system to achieve a new functionality of full autonomy show significance of this contribution to the development of Expert and Intelligent Systems domain. The author believes that autonomous systems constitute the future of Expert and Intelligent Systems.     

Proof planning with multiple strategies

Proof planning is a technique for theorem proving which replaces the ultra-efficient but blind search of classical theorem proving systems by an informed knowledge-based planning process that employs mathematical knowledge at a human-oriented level of abstraction. Standard proof planning uses methods as operators and control rules to find an abstract proof plan which can be expanded (using tactics) down to the level of the underlying logic calculus.In this paper, we propose more flexible refinements and a modification of the proof planner with an additional strategic level of control above the previous proof planning control. This strategic control guides the cooperation of the problem solving strategies by meta-reasoning.We present a general framework for proof planning with multiple strategies and describe its implementation in the Multi system. The benefits are illustrated by several large case studies, which significantly push the limits of what can be achieved by a machine today.     

Motion Planning for Redundant Manipulators Using a Floating Point Genetic Algorithm

This paper deals with the trajectory planning problem for redundant manipulators. A genetic algorithm (GA) using a floating point representation is proposed to search for the optimal end-effector trajectory for a redundant manipulator. An evaluation function is defined based on multiple criteria, including the total displacement of the end-effector, the total angular displacement of all the joints, as well as the uniformity of Cartesian and joint space velocities. These criteria result in minimized, smooth end-effector motions. Simulations are carried out for path planning in free space and in a workspace with obstacles. Results demonstrate the effectiveness and capability of the proposed method in generating optimized collision-free trajectories.     

An Agent Based Framework for Internetware Computing

Internetware intends to be a paradigm of Web-based software development. At present, researches on Internetware have gained daily expanding attentions and interests. This paper proposes an agent based framework for Internetware computing. Four principles are presented that are followed by this framework. They are the autonomy principle, the abstract principle, the explicitness principle and the competence principle. Three types of agents with di?erent responsibilities are designed and specified. They are the capability providing agents, the capability planning agents and the capability consuming agents. In this sense, capability decomposition and satisfaction turns to be a key issue for this framework and becomes a communication protocol among these distributed and heterogenous agents. A capability conceptualization is proposed and based on the conceptualization, an agent coalition formation mechanism has been developed. This mechanism features that (1) all the participants make their one decisions on whether or not joining the coalition based on the capability realization pattern generated by the capability planning agents as well as the benefits they can obtain; and (2) the coalition selection is conducted by a negotiation process for satisfying the expectations of all the participants as the complexity of this problem has been proven to be NP-complete.     

基于神经索引实例与知识推理的混合型智能CAPP策略

提出了一种新的基于耦合神经网络实例与知识的混合推理策略,采用面向对象的方法表达实例和知识,将神经索引模型引入实例推理中,在此基础上实现了ＣＡＰＰ的变异设计。同时,建立了基于零件及其工艺数据知识的分层知识表达与层次式推理机制,从而实现了ＣＡＰＰ的创成式设计。在给出这种知识化的ＣＡＰＰ系统的总体结构之后,详细讨论了基于神经网络的实例索引模型及实例推理和知识基推理的实现过程,由于吸收了派生法的类比设计思     

Dynamic optimal payload path planning of mobile manipulators among moving obstacles

This paper is dealt with dynamic analysis of the wheeled mobile manipulators in the presence of moving obstacles considering optimal payload criterion. General dynamic formulation of the system was derived, and the moving obstacle avoidance strategy was proposed in terms of dynamic potential functions. The problem of dynamic motion planning and payload maximization was formulated using open-loop optimal control theory. Then, the indirect solution based on Pontryagin’s minimum principle was employed to solve the problem. Using the proposed method, complete nonlinear states and control constraints were treated without any simplifications such as linearizing the dynamics equations, discretizing the robot’s workspace, or parameterizing the solution. The proposed method will be useful for the system design and in the situation where the trajectories of obstacles are predefined. Finally, capability and applicability of the proposed method were investigated by the number of simulations on a two-link mobile manipulator.     

An architecture for a self-improving instructional planner for intelligent tutoring systems

Machine instructional planners use changing and uncertain data to incrementally configure plans and control the execution and dynamic refinement of these plans. Current instructional planners cannot adequately plan, replan, and monitor the delivery of instruction. This is due in part to the fact that current instructional planners are incapable of planning in a global context, developing competing plans in parallel, monitoring their planning behavior, and dynamically adapting their control behavior. In response to these and other deficiencies of instructional planners a generic system architecture based on the blackboard model was implemented. This self-improving instructional planner (SUP) dynamically creates instructional plans, requests execution of these plans, replans, and improves its planning behavior based on a student's responses to tutoring. Global planning was facilitated by explicitly representing decisions about past, current, and future plans on a global data structure called the plan blackboard. Planning in multiple worlds is facilitated by labeling plan decisions by the context in which they were generated. Plan monitoring was implemented as a set of monitoring knowledge sources. The flexible control capability for instructional planner was adapted from the blackboard architecture BB1. The explicit control structure of SUP enabled complex and flexible planning behavior while maintaining a simple planning architecture.