A knowledge-based task-level programming and execution environment for robots

A knowledge-based framework to support task-level programming and operational control of robots is described. Our bask intention is to enhance the intelligence of a robot control system so that it may carefully coordinate the interactions among discrete, asynchronous and concurrent events under the constraints of action precedence and resource allocation. We do this by integrating both off-line and on-line planning capabilities in a single framework. The off-line phase is equipped with proper languages for describing workbenches, specifying tasks, and soliciting knowledge from the user to support the execution of robot tasks. A static planner is included in the phase to conduct static planning, which develops local plans for various specific tasks. The on-line phase is designed as a dynamic control loop for the robot system. It employs a dynamic planner to tackle any contingent situations during the robot operations. It is responsible for developing proper working paths and motion plans to achieve the task goals within designated temporal and resource constraints. It is implemented in a distributed and cooperative blackboard system, which facilitates the integration of various types of knowledge. Finally, any failures from the on-line phase are fed back to the off-line phase. This forms the interaction between the off-line and on-line phases and introduces an extra closed loop opportunistically to tune the dynamic planner to adapt to the variation of the working environment in a long-term manner.     

Autonomous Driving with Concurrent Goals and Multiple Vehicles: Mission Planning and Architecture

We introduce a new distributed planning paradigm, which permits optimal execution and dynamic replanning of complex multi-goal missions. In particular, the approach permits dynamic allocation of goals to vehicles based on the current environment model while maintaining information-optimal route planning for each individual vehicle to individual goals. Complex missions can be specified by using a grammar in which ordering of goals, priorities, and multiple alternatives can be described. We show that the system is able to plan local paths in obstacle fields based on sensor data, to plan and update global paths to goals based on frequent obstacle map updates, and to modify mission execution, e.g., the assignment and ordering of the goals, based on the updated paths to the goals.The multi-vehicle planning system is based on the GRAMMPS planner; the on-board dynamic route planner is based on the D* planner. Experiments were conducted with stereo and high-speed ladar as the to sensors used for obstacle detection. This paper focuses on the multi-vehicle planner and the systems architecture. A companion paper (Brumitt et al., 2001) analyzes experiments with the multi-vehicle system and describes in details the other components of the system.     

Multi-agent collaboration in time-constrained domains

Timeliness is usually an indispensable attribute of planning and problem solving for resource allocation in command, control and communication systems. The success of such a system is judged on its ability to respond to scheduled and unscheduled tasks within a permissible time period. The response is based on a plan that covers the following activities: resource allocation, plan execution and monitoring and dynamic plan mending, if necessary. Decision making for resource selection can become very time consuming when there are many resources and the number of constraints is large. In a changing environment of multiple agents, restrictive organizational structures and strict communication protocols may cause intolerable further delays.Traditional approaches to planning in deterministic environments require a predictable amount of time to produce and execute plans. However, given more time, such systems usually cannot improve on the plans. In this paper we describe a multi-agent resource scheduler which uses a prioritized rule base to model decision making under the constraints of time. We also discuss dynamic scoping as a negotiation technique for inter-agent cooperation and constrained lattice-like communications as an optimized message routing strategy. Finally, we present some empirical results from a sequence of experiments.     

General Temporal Knowledge for Planning and Data Mining

We consider the architecture of systems that combine temporal planning and plan execution and introduce a layer of temporal reasoning that potentially improves both the communication between humans and such systems, and the performance of the temporal planner itself. In particular, this additional layer simultaneously supports more flexibility in specifying and maintaining temporal constraints on plans within an uncertain and changing execution environment, and the ability to understand and trace the progress of plan execution. It is shown how a representation based on single set of abstractions of temporal information can be used to characterize the reasoning underlying plan generation and execution interpretation. The complexity of such reasoning is discussed.     

Scaling up knowledge graph creation to large and heterogeneous data sources

RDF knowledge graphs (KG) are powerful data structures to represent factual statements created from heterogeneous data sources. KG creation is laborious and demands data management techniques to be executed efficiently. This paper tackles the problem of the automatic generation of KG creation processes declaratively specified; it proposes techniques for planning and transforming heterogeneous data into RDF triples following mapping assertions specified in the RDF Mapping Language (RML). Given a set of mapping assertions, the planner provides an optimized execution plan by partitioning and scheduling the execution of the assertions. First, the planner assesses an optimized number of partitions considering the number of data sources, type of mapping assertions, and the associations between different assertions. After providing a list of partitions and assertions that belong to each partition, the planner determines their execution order. A greedy algorithm is implemented to generate the partitions’ bushy tree execution plan. Bushy tree plans are translated into operating system commands that guide the execution of the partitions of the mapping assertions in the order indicated by the bushy tree. The proposed optimization approach is evaluated over state-of-the-art RML-compliant engines, and existing benchmarks of data sources and RML triples maps. Our experimental results suggest that the performance of the studied engines can be considerably improved, particularly in a complex setting with numerous triples maps and large data sources. As a result, engines that time out in complex cases are enabled to produce at least a portion of the KG applying the planner.     

Hierarchical planning involving deadlines, travel time, and resources

This paper describes a planning architecture that supports a form of hierarchical planning well suited to applications involving deadlines, travel time, and resource considerations. The architecture is based upon a temporal database, a heuristic evaluator, and a decision procedure for refining partial plans. A partial plan consists of a set of tasks and constraints on their order, duration, and potential resource requirements. The temporal database records the partial plan that the planner is currently working on and computes certain consequences of that information to be used in proposing methods to further refine the plan. The heuristic evaluator examines the space of linearized extensions of a given partial plan in order to reject plans that fail to satisfy basic requirements (e.g., hard deadlines and resource limitations) and to estimate the utility of plans that meet these requirements. The information provided by the temporal database and the heuristic evaluator is combined using a decision procedure that determines how best to refine the current partial plan. Neither the temporal database nor the heuristic evaluator is complete and, without reasonably accurate information concerning the possible resource requirements of the tasks in a partial plan, there is a significant risk of missing solutions. A specification language that serves to encode expectations concerning the duration and resource requirements of tasks greatly reduces this risk, enabling useful evaluations of partial plans. Details of the specification language and examples illustrating how such expectations are exploited in decision making are provided.     

Autonomous Driving with Concurrent Goals and Multiple Vehicles: Experiments and Mobility Components

In this paper, we describe a complete system for mission planning and execution for multiple robots in natural terrain. We report on experiments with a system for autonomously driving two vehicles based on complex mission specifications. We show that the system is able to plan local paths in obstacle fields based on sensor data, to plan and update global paths to goals based on frequent obstacle map updates, and to modify mission execution, e.g., the assignment and ordering of the goals, based on the updated paths to the goals.Two recently developed sensors are used for obstacle detection: a high-speed laser range finder, and a video-rate stereo system. An updated version of a dynamic path planner, D*, is used for on-line computation of routes. A new mission planning and execution-monitoring tool, GRAMMPS, is used for managing the allocation and ordering of goals between vehicles.We report on experiments conducted in an outdoor test site with two HMMWVs. Implementation details and performance analysis, including failure modes, are described based on a series of twelve experiments, each over 1/2 km distance with up to nine goals.The work reported here includes a number of results not previously published, including the use of a real-time stereo machine and a high-performance laser range finder, and the use of the GRAMMPS planning system.     

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.     

Planning in highly dynamic environments: an anytime approach for planning under time constraints

In this paper, we present a novel and domain-independent planner aimed at working in highly dynamic environments with time constraints. The planner follows the anytime principles: a first solution can be quickly computed and the quality of the final plan is improved as long as time is available. This way, the planner can provide either fast reactions or very good quality plans depending on the demands of the environment. As an on-line planner, it also offers important advantages: our planner allows the plan to start its execution before it is totally generated, unexpected events are efficiently tackled during execution, and sensing actions allow the acquisition of required information in partially observable domains. The planning algorithm is based on problem decomposition and relaxation techniques. The traditional relaxed planning graph has been adapted to this on-line framework by considering information about sensing actions and action costs. Results also show that our planner is competitive with other top-performing classical planners.     

Interleaving Planning and Robot Execution for Asynchronous User Requests

ROGUE is an architecture built on a real robot which provides algorithms for the integration of high-level planning, low-level robotic execution, and learning. ROGUE addresses successfully several of the challenges of a dynamic office gopher environment. This article presents the techniques for the integration of planning and execution.ROGUE uses and extends a classical planning algorithm to create plans for multiple interacting goals introduced by asynchronous user requests. ROGUE translates the planner';s actions to robot execution actions and monitors real world execution. ROGUE is currently implemented using the PRODIGY4.0 planner and the Xavier robot. This article describes how plans are created for multiple asynchronous goals, and how task priority and compatibility information are used to achieve appropriate efficient execution. We describe how ROGUE communicates with the planner and the robot to interleave planning with execution so that the planner can replan for failed actions, identify the actual outcome of an action with multiple possible outcomes, and take opportunities from changes in the environment.ROGUE represents a successful integration of a classical artificial intelligence planner with a real mobile robot.     

TOLERANT PLANNING AND NEGOTIATION IN MULTIAGENT ENVIRONMENTS

Tolerant planning improves the likelihood of plans being successfully executed despite uncertainties and changes during execution. By tolerating execution errors, dynamic replanning need not be invoked as often or as immediately as in a less tolerant plan. The approach in designing tolerant plans is to allow for redundancies in the requirements (usually resources) for execution. While this approach is feasible, it raises another problem—more conflicts must be resolved during planning. This conflict resolution problem can be solved using a novel model of iterative negotiation for multiagent coordination. It requires agents to be skillful in negotiating with other agents to resolve conflicts in such a way as to minimize compromising their own tolerance while being benevolent in helping others find a feasible plan. This paper also describes an application of these concepts in a planner that generates conflict-free movement schedules for several mobile robots in a factory domain.     

SCI中资源调度算法的分析与仿真

IEEE的1596协议标准SCI是一个面向高速、低延迟应用的互联协议。主要应用在大规模的集群系统以及实时性要求较高的任务系统。为了保证在数据通信中的高速、低延迟,SCI在每个节点输出、输入接口处分别提供了两个重要的资源调度算法——带宽分配协议及队列分配协议。文中将对这两种调度算法进行深入的分析及仿真,并对结果进行分析,提出了下一步的研究方向。     

RTAI下动态集成的资源预留调度器的设计与实现

近年来基于双内核架构增强Linux操作系统实时性的RTAI(Real-Time Application Interface)在工业控制等硬实时领域得到广泛应用。RTAI通过抢占Linux的执行来保障硬实时性,Linux被抢占的时间依赖于硬实时应用的处理器要求而每次均会有较大不同,导致Linux的执行时间不可预测,从而无法保障软实时应用的服务质量。动态集成的资源预留调度器(Dynamic Integrated Resource Reserved Scheduler,DIRRS)通过增强RTAI调度器使其支持资源预留机制,在Linux实现可动态集成的、基于服务器的调度策略,不但可以保证Linux及其以上的软实时应用,即使在有硬实时任务并发时也能得到处理器资源,而且很容易通过更换不同的服务器内核模块来实现用户自定义的调度策略。     

并行查询下查询执行计划的选择

查询是数据库系统的主要负载,其效率决定了数据库性能的好坏。一个查询存在多种执行计划,当前,查询优化器只能按照数据库系统的配置参数,静态地为查询选择一个较优的执行计划。并行查询间存在复杂多变的资源争用,很难通过配置参数准确反映,而且同一执行计划在不同情景下的效率并不一致。并行查询下执行计划的选择需考虑查询间的相互影响——查询交互。基于此,提出了一种在并行查询下度量查询受查询交互影响大小的标准QIs。针对并行查询下查询执行计划的选择,还提出了一种动态地为查询选择执行计划的方法TRating,该方法通过比较查询组合中按不同执行计划执行的查询受查询交互影响的大小,选择受查询交互影响较小的执行计划作为该查询的较优执行计划。实验结果表明,TRating方法为查询选择较优执行计划的准确率达61%,相比查询优化器提高了25%;而且在为查询选择次优执行计划时,其准确率也高达69%。     

超算环境科学工作流应用平台的引擎设计和资源调度

高性能计算机体系结构的复杂性对使用者提出了更高要求;而且在工程实际和科学实验中,通常需要使用多种应用软件相互协作才能解决复杂问题。围绕超算资源的易用性和多类软件的集成以及协作需求,开发了超算环境下的科学工作流应用平台,设计了异步并发的流程执行引擎,采取调度算法和调度器、引擎相分离的设计策略,给出了资源调度方案。提出了局部资源池化技术和资源预约算法,并比较分析了五种常用调度算法的性能,给出了算法选择的建议。实际应用表明设计的引擎能够支撑复杂工作流的灵活执行方式,给出的资源调度方案能够满足超算环境下工作流应用的高效执行。     

Speculative plan execution for information gathering

The execution performance of an information gathering plan can suffer significantly due to remote I/O latencies. A streaming dataflow model of execution addresses the problem to some extent, exploiting all natural opportunities for parallel execution, as allowed by the data dependencies in a plan. Unfortunately, plans that integrate information from multiple sources often use the results of one operation as the basis for forming queries to a subsequent operation. Such cases require sequential execution, an inefficiency that can erase prior gains made through techniques like streaming dataflow. To address this problem, we present a technique called speculative plan execution, an out-of-order method that capitalizes on knowledge gained from prior executions as a means for overcoming remaining data dependencies between plan operators. Our approach inserts additional plan operators that generate and confirm speculative results, while preserving the safety and fairness of overall execution. To increase the utility of speculative execution, we propose a method of value prediction that combines caching with the more effective and space-efficient techniques of classification and transduction. We present experimental results that demonstrate how the performance of information gathering plans can benefit from speculative execution and how its overall utility can be increased through our hybrid method of value prediction.     

A loop acceleration technique to speed up verification of automatically generated plans

The CIRCA planning system automatically creates reactive plans and uses formal verification techniques to prove that those plans will preserve system safety. CIRCA’s timed automata verification system is highly efficient, yet can display pathologically bad behavior when reasoning about reaction loops, a particular form of interacting cycles of states. In this paper, we describe a loop acceleration technique that recognizes these state-space structures during the verification process and bypasses the process of expanding an arbitrarily large cycle of states, effectively compressing loops of arbitrary size into a compact, finite set of states. The resulting performance improvement can be very dramatic: in domains where tight loops of short-duration transitions interact with long-duration transitions, our new loop acceleration methods can reduce verification time (and hence planning time) from hours to below a second.     

Robust design of flexible manufacturing systems using, colored Petri net and genetic algorithm

A method is presented for the robust design of flexible manufacturing systems (FMS) that undergo the forecasted product plan variations. The resource allocation and the operation schedule of a FMS are modeled as a colored Petri net and an associated transition firing sequence. The robust design of the colored Petri net model is formulated as a multi-objective optimization problem that simultaneously minimizes the production costs under multiple production plans (batch sizes for all jobs), and the reconfiguration cost due to production plan changes. A genetic algorithm, coupled with the shortest imminent operation time (SIO) dispatching rule, is used to simultaneously find the near-optimal resource allocation and the event-driven schedule of a colored Petri net. The resulting Petri net is then compared with the Petri nets optimized for a particular production plan in order to address the effectiveness of the robustness optimization. The simulation results suggest that the proposed robustness optimization scheme should be considered when the products are moderately different in their job specifications so that optimizing for a particular production plan creates inevitably bottlenecks in product flow and/or deadlock under other production plans.     

Managing uncertainty in time-critical plan evaluation

In complex real-world domains, uncertainty in predicting the results of plan execution drives the evaluation component of the planning cycle to explore a combinatorial explosion of alternative futures. This evaluation component is critical in evaluating the feasibility, strengths and weaknesses of a proposed plan. In time critical situations the planner is thus faced with a trade-off between timeliness and evaluation completion. Furthermore, a human planner is faced with the additional problem of evaluation credibility when using fast automatic evaluation in a complex and uncertain domain. An approach to handling these problems of time-criticality, uncertainty, and credibility is explored using the wargaming component of the military operational planning cycle. The Semi-Automated Forces Wargamer has been developed using two techniques. The first technique integrates procedural representations of plans and intentions with heuristic representations of simulated probabilistic execution. This facilitates the simulated execution of plans with multiple worlds corresponding to the possible results of actions taken in the real and uncertain world. The second provides a what-if capability via a tree representation of the possible combat outcomes. This provides the user with a tool for intelligent and focussed exploration of the space of possible outcomes to the plan. These techniques combine to generate a manageable and useful subset of the space of simulated plan results from which the user can apply human expertize to guide plan exploration.