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.     

Group planning with time constraints

Embedding planning systems in real-world domains has led to the necessity of Distributed Continual Planning (DCP) systems where planning activities are distributed across multiple agents and plan generation may occur concurrently with plan execution. A key challenge in DCP systems is how to coordinate activities for a group of planning agents. This problem is compounded when these agents are situated in a real-world dynamic domain where the agents often encounter differing, incomplete, and possibly inconsistent views of their environment. To date, DCP systems have only focused on cases where agents’ behavior is designed to optimize a global plan. In contrast, this paper presents a temporal reasoning mechanism for self-interested planning agents. To do so, we model agents’ behavior based on the Belief-Desire-Intention (BDI) theoretical model of cooperation, while modeling dynamic joint plans with group time constraints through creating hierarchical abstraction plans integrated with temporal constraints network. The contribution of this paper is threefold: (i) the BDI model specifies a behavior for self interested agents working in a group, permitting an individual agent to schedule its activities in an autonomous fashion, while taking into consideration temporal constraints of its group members; (ii) abstract plans allow the group to plan a joint action without explicitly describing all possible states in advance, making it possible to reduce the number of states which need to be considered in a BDI-based approach; and (iii) a temporal constraints network enables each agent to reason by itself about the best time for scheduling activities, making it possible to reduce coordination messages among a group. The mechanism ensures temporal consistency of a cooperative plan, enables the interleaving of planning and execution at both individual and group levels. We report on how the mechanism was implemented within a commercial training and simulation application, and present empirical evidence of its effectiveness in real-life scenarios and in reducing communication to coordinate group members’ activities.     

A study of a dynamic progressive reasoning system

Controlling resource-bounded systems not only involves taking decisions about planning and scheduling reasoning components to achieve time-constrained goals, but also about how to monitor the progress of these components. We propose an extended closed-loop planning, scheduling and execution. This extended closed-loop allows one to address the following limitations: (1) dynamic and time-constrained environments, (2) uncertainty regarding the duration of reasoning components and (3) tolerance to execution failure. The interactions in the extended closed-loop allow one: first to revise the current schedule when deviations from a predetermined schedule occur during execution, second to update continuously the set of goals on which the control focuses its decision and third how this set of goals is updated when an execution failure occurs. We discuss these issues in a progressive reasoning system implemented and applied to a railway control application where real-time (dynamic and time-constrained) situations occur.     

Resource-bounded sensing and planning in autonomous systems

This paper is concerned with the implications of limited computational resources and uncertainty on the design of autonomous systems. To address this problem, we redefine the principal role of sensor interpretation and planning processes. Following Agre and Chapman's plan-as-communication approach, sensing and planning are treated as computational processes that provide information to an execution architecture and thus improve the overall performance of the system. We argue that autonomous systems must be able to trade off the quality of this information with the computational resources required to produce it. Anytime algorithms, whose quality of results improves gradually as computation time increases, provide useful performance components for time-critical sensing and planning in robotic systems. In our earlier work, we introduced a compilation scheme for optimal composition of anytime algorithms. This paper demonstrates the applicability of the compilation technique to the construction of autonomous systems. The result is a flexible approach to construct systems that can operate robustly in real-time by exploiting the tradeoff between time and quality in planning, sensing and plan execution.     

Integrated planning and execution control for an autonomous mobile robot

We present a distributed hierarchical planning and execution monitoring system and its implementation on an actual mobile robot. The planning system is a distributed hierarchical domain independent system called FPS for Flexible Planning System. It is a rule based plan generation system with planning specific and domain specific rules. A planning solution to the ‘Boxes and Wedge’ Problem is presented.The Robot Control System (RCS) operates and monitors the robot in the real world. In order to allow real-time responses to asynchronous events (both internal and external), RCS consists of a rule-based decision kernel and a distributed set of sensor/effector monitors. RCS contains an execution model and may authorize local corrective actions, e.g., unexpected obstacle avoidance during execution of a trajectory. RCS also generates status and failure reports through which the PMs inform the different decision subsystems as to the robot's state and current capacities. The failure reports help the RCS and planners in correcting/replanning a plan that has aborted. An illustrative example of system behaviour is to be presented.     

Learning action models for the improved execution of navigation plans

Most state-of-the-art navigation systems for autonomous service robots decompose navigation into global navigation planning and local reactive navigation. While the methods for navigation planning and local navigation themselves are well understood, the plan execution problem, the problem of how to generate and parameterize local navigation tasks from a given navigation plan is largely unsolved.

This paper describes how a robot can autonomously learn to execute navigation plans. We formalize the problem as a Markov Decision Process (MDP) and derive a decision theoretic action selection function from it. The action selection function employs models of the robot’s navigation actions, which are autonomously acquired from experience using neural networks or regression tree learning algorithms. We show, both in simulation and on an RWI B21 mobile robot, that the learned models together with the derived action selection function achieve competent navigation behavior.     

基于维护计划的卷烟生产线设备运维管理系统

介绍了一个基于维护计划的卷烟生产线设备运维管理系统,分别从系统功能模型、计划业务流程等角度描述了系统的设计思想。并阐述了系统涉及的设备润滑计划制定、派工、运行维护数据分析、系统开发与应用等关键技术。开发的系统具有一定的可集成性、可扩展性和可重构性,与移动通讯技术、数据采集技术相结合,集成在制造执行系统中,可以实现卷烟生产企业制丝、卷包生产线的实时设备运行与维护保养管理。     

Evaluation of decision support systems in medicine

Evaluation deals with the measurement or judgement of system characteristics and with comparison of these with the frame of reference. Evaluation of medical decision support systems is important because these systems are planned to support human decision making in tasks where information from different sources is combined to support clinicians' decisions concerning diagnosis, therapy planning and monitoring of the disease and treatment processes. As the field of decision support systems is still relatively unexplored, standards or generally accepted methodologies are not yet available for evaluation. Evaluation of medical decision support systems should be approached from the perspectives of knowledge acquisition, system development life-cycle and user-system integrated environment.     

Recovery planning of industrial robots based on semantic information of failures and time-dependent utility

Industrial robots are required to recover from temporary errors and continue operations under a changing environment. In this paper, we propose a recovery planning system that considers the semantic information behind errors during robotic actions. In order to establish general repair strategies for feasible recovery plans under uncertainties, the proposed system uses a conceptual graph based on case grammar and a Bayesian network that is dynamically constructed according to the semantic information. In addition, we tackle the problem that the wealth of the recovery plan depends on the uncertainty of execution costs against the deadline at the production site. The proposed system controls the decision model by using a time-dependent utility. We demonstrate the effectiveness of the proposed system through simulations of assembly tasks by multiple robots.     

实时专家系统中反应—规划—动作的集成

以AMPE模型为基础,探讨了在动态环境下AI系统如何集成响应执行和规划生成的问题。AMPE模型是从实际应用中演化出的AI模型,采用分离结构,在规划中引入一类被称为观察点的特殊行为,支持异步重规划。描述了AMPE的各类规划行为的形式和消息的基本结构。给出一个规划表示的实例。     

A theoretic and practical framework for scheduling in a stochastic environment

There are many systems and techniques that address stochastic planning and scheduling problems, based on distinct and sometimes opposite approaches, especially in terms of how generation and execution of the plan, or the schedule, are combined, and if and when knowledge about the uncertainties is taken into account. In many real-life problems, it appears that many of these approaches are needed and should be combined, which to our knowledge has never been done. In this paper, we propose a typology that distinguishes between proactive, progressive, and revision approaches. Then, focusing on scheduling and schedule execution, a theoretic model integrating those three approaches is defined. This model serves as a general template to implement a system that will fit specific application needs: we introduce and discuss our experimental prototypes which validate our model in part, and suggest how this framework could be extended to more general planning systems.     

A path planning and path‐following control framework for a general 2‐trailer with a car‐like tractor

Maneuvering a general 2‐trailer with a car‐like tractor in backward motion is a task that requires a significant skill to master and is unarguably one of the most complicated tasks a truck driver has to perform. This paper presents a path planning and path‐following control solution that can be used to automatically plan and execute difficult parking and obstacle avoidance maneuvers by combining backward and forward motion. A lattice‐based path planning framework is developed in order to generate kinematically feasible and collision‐free paths and a path‐following controller is designed to stabilize the lateral and angular path‐following error states during path execution. To estimate the vehicle state needed for control, a nonlinear observer is developed, which only utilizes information from sensors that are mounted on the car‐like tractor, making the system independent of additional trailer sensors. The proposed path‐planning and path‐following control framework is implemented on a full‐scale test vehicle and results from simulations and real‐world experiments are presented.     

作战班组层次行为建模

战术仿真是作战仿真中重要的一部分,是针对合同战术、班组战术层次的仿真,涉及班组的整体决策和成员的个体行为建模,班组的决策行为通常关注于班组成员间的协同合作,而成员的个体行为易受各种战场要素的影响,关注于个体行为的规划,只在单一层次进行建模会造成行为描述的混乱。为了区分班组行为和个体行为,增强个体行为的表达能力,基于行为树和HTN（Hierarchical Task Network）构建了班组层次行为模型,将战术命令的执行分为班组整体决策和实体具体执行两个层次。班组决策层采用行为树对班组的决策行为进行建模,提出了寻径距离因素和武器威胁因素的战术位置选择评估方法,负责班组成员间的协同决策,为班组成员分配任务;实体执行层采用HTN对作战实体的行为进行建模,根据班组决策层分配的任务利用规划算法规划个体任务计划,提升对实体行为描述的表达能力。并通过搭建仿真对抗环境验证了该层次行为模型的有效性。     

Realizing active data warehouses with off‐the‐shelf database technology

Active data warehouses belong to a new category of decision support systems, which automate decision making for routine decision tasks and semi‐routine decision tasks. Just as active database systems extend conventional database systems with event–condition–action rules for integrity constraint enforcement or procedure execution, active data warehouses extend conventional data warehouses with analysis rules that mimic the work of an analyst during decision making. This paper demonstrates how analysis rules can be implemented on top of a passive relational data warehouse system by using commercially available database technology. Copyright © 2002 John Wiley & Sons, Ltd.     

基于改进型批决策与调度建模的生产计划调度系统的研究

热轧型钢生产工艺复杂,其生产中极易出现由于计划调度安排不当而产生的交货期延误、库存超负荷等问题。针对以上问题研究设计了MES生产计划调度系统,改进了批决策调度策略用于数学建模,利用自适应遗传算法求解生产调度计划。以此为基础,为某热轧企业设计实现了生产计划调度系统,并通过真实的热轧型钢订单、原料、设备等数据,对模型改进前后的计划编制方法进行模拟与比较,验证了利用该改进型批决策与调度模型编制的热轧型钢生产调度计划可节省生产时间、降低设备调度时间,以此来指导热轧型钢的生产可切实减少交货延误和减少库存占用率,并提高企业利润率。     

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.     

An analytical approach to improving due-date and lead-time dynamics in production systems

A deterioration of due-date reliability is often attributed by planners to external causes rather than to their own planning behavior. Particularly, planners tend to underestimate the effects of time delays, and may not sufficiently take control actions into account that have been initiated but are not yet demonstrating any effects. Unfavorable dynamic behavior can result if planners react inappropriately to short-term decreases in due-date reliability and, for example, use their intuition to adjust planned lead times. A better understanding is needed of the impact of time delays and lead-time-related adjustments on resulting system behavior and of how often plans and associated work releases should be adjusted in practice.In this paper, two planning and control approaches are modeled and analyzed: First, a production system is modeled in which planned lead times and work input are adjusted periodically if the average lead time deviates from the planned lead time. Second, a production system is modeled in which regulation of lead time towards a planned lead time is accomplished by adjusting the work input. For both approaches, discrete (z-transform) equations are obtained that allow trends in dynamic behavior to be characterized as a function of delays in obtaining production information, and delays in making lead time adjustment decisions and implementing them. Industrial data from a steel-producing company are used to illustrate the potential effects of time delays and of averaging of lead time data, as well as to illustrate how analytical results can be used to guide selection of the adjustment period and of lead time regulation parameters. The analytical approach presented here can be used as a tool for quantifying and guiding improvements in the performance, the robustness, and the agility of production systems. This is of particular interest with respect to cyber-physical technologies such as autonomous data collection and embedded models that present significant future opportunities for reducing delays in decision making and decision implementation.     

A temporal logic-based planning and execution monitoring framework for unmanned aircraft systems

Research with autonomous unmanned aircraft systems is reaching a new degree of sophistication where targeted missions require complex types of deliberative capability integrated in a practical manner in such systems. Due to these pragmatic constraints, integration is just as important as theoretical and applied work in developing the actual deliberative functionalities. In this article, we present a temporal logic-based task planning and execution monitoring framework and its integration into a fully deployed rotor-based unmanned aircraft system developed in our laboratory. We use a very challenging emergency services application involving body identification and supply delivery as a vehicle for showing the potential use of such a framework in real-world applications. TALplanner, a temporal logic-based task planner, is used to generate mission plans. Building further on the use of TAL (Temporal Action Logic), we show how knowledge gathered from the appropriate sensors during plan execution can be used to create state structures, incrementally building a partial logical model representing the actual development of the system and its environment over time. We then show how formulas in the same logic can be used to specify the desired behavior of the system and its environment and how violations of such formulas can be detected in a timely manner in an execution monitor subsystem. The pervasive use of logic throughout the higher level deliberative layers of the system architecture provides a solid shared declarative semantics that facilitates the transfer of knowledge between different modules.     

The impact of interface-induced handling requirements on action generation in technical system control

In analysing action generation in technical system control we differentiate between a purely cognitive stage of pre-actional decision-making and an action-related cognitive-physical stage of action planning and plan execution. This study investigates the impact of interface-induced handling requirements on the action-related processes in the domain of artificial respiration in intensive care. Thirty-two novice and experienced intensive care nurses had to solve three realistic control tasks on two types of respiration unit equipped either with an 'analogue' or with a 'digital' user interface. The quality of the novices' plan execution and—unexpectedly— action planning, too, decreased when working on the 'digital' interface. Interface-induced handling requirements obviously have an important influence on the usability of technical systems not only on a purely cognitive level, but also on the action-related level.     

Conformant plans and beyond: Principles and complexity

Conformant planning is used to refer to planning for unobservable problems whose solutions, like classical planning, are linear sequences of operators called linear plans. The term ‘conformant’ is automatically associated with both the unobservable planning model and with linear plans, mainly because the only possible solutions for unobservable problems are linear plans. In this paper we show that linear plans are not only meaningful for unobservable problems but also for partially-observable problems. In such case, the execution of a linear plan generates observations from the environment which must be collected by the agent during the execution of the plan and used at the end in order to determine whether the goal had been achieved or not; this is the typical case in problems of diagnosis in which all the actions are knowledge-gathering actions.Thus, there are substantial differences about linear plans for the case of unobservable or fully-observable problems, and for the case of partially-observable problems: while linear plans for the former model must conform with properties in state space, linear plans for partially-observable problems must conform with properties in belief space. This differences surface when the problems are allowed to express epistemic goals and conditions using modal logic, and place the plan-existence decision problem in different complexity classes.Linear plans is one extreme point in a discrete spectrum of solution forms for planning problems. The other extreme point is contingent plans in which there is a branch point for every possible observation at each time step, and thus the number of branch points is not bounded a priori. In the middle of the spectrum, there are plans with a bounded number of branch points. Thus, linear plans are plans with zero branch points and contingent plans are plans with unbounded number of branch points.In this work, we lay down foundations and principles for the general treatment of linear plans and plans of bounded branching, and provide exact complexity results for novel decision problems. We also show that linear plans for partially-observable problems are not only of theoretical interest since some challenging real-life problems can be dealt with them.