Landmark-based heuristic online contingent planning

In contingent planning problems, agents have partial information about their state and use sensing actions to learn the value of some variables. When sensing and actuation are separated, plans for such problems can often be viewed as a tree of sensing actions, separated by conformant plans consisting of non-sensing actions that enable the execution of the next sensing action. We propose a heuristic, online method for contingent planning which focuses on identifying the next useful sensing action. We select the next sensing action based on a landmark heuristic, adapted from classical planning. We discuss landmarks for plan trees, providing several alternative definitions and discussing their merits. The key part of our planner is the novel landmarks-based heuristic, together with a projection method that uses classical planning to solve the intermediate conformant planning problems. The resulting heuristic contingent planner solves many more problems than state-of-the-art, translation-based online contingent planners, and in most cases, much faster, up to 3 times faster on simple problems, and 200 times faster on non-simple domains.     

Massive deliberation [Web agents]

Agents are proliferating on the Web, making it conceivable that their collective reasoning ability might someday be harnessed for robust decision-making. The hope is that massive deliberation power can soon help solve problems that require knowledge, reasoning, and intelligence. Until recently, working individually or in small groups, agents across the Web could barely communicate and could only reason under conditions of severely bounded rationality. Projects such as Agentcities showed that widespread heterogeneous agents could collaborate on specific predefined tasks and provide diverse agent-based services. When the tasks are dynamic, of long duration, and ill defined, however, success requires planning that is continual, distributed, and accounts for the social fabric into which the plans and their execution must fit. The authors discusses distributed planning and societal agents.     

CTP: A New Constraint-Based Formalism for Conditional, Temporal Planning

Temporal constraints pose a challenge for conditional planning, because it is necessary for a conditional planner to determine whether a candidate plan will satisfy the specified temporal constraints. This can be difficult, because temporal assignments that satisfy the constraints associated with one conditional branch may fail to satisfy the constraints along a different branch. In this paper we address this challenge by developing the Conditional Temporal Problem (CTP) formalism, an extension of standard temporal constraint-satisfaction processing models used in non-conditional temporal planning. Specifically, we augment temporal CSP frameworks by (1) adding observation nodes, and (2) attaching labels to all nodes to indicate the situation(s) in which each will be executed. Our extended framework allows for the construction of conditional plans that are guaranteed to satisfy complex temporal constraints. Importantly, this can be achieved even while allowing for decisions about the precise timing of actions to be postponed until execution time, thereby adding flexibility and making it possible to dynamically adapt the plan in response to the observations made during execution. We also show that, even for plans without explicit quantitative temporal constraints, our approach fixes a problem in the earlier approaches to conditional planning, which resulted in their being incomplete.     

An on-line robot planning strategy for target interception

On-line planning of gross robot motion for moving-object interception is considered in this article within the context of an active prediction planning, and execution (APPE) strategy. The objective is to find an optimal interception point such that the robot end-effector and the object arrive simultaneously at this target pregrasping point. In this approach, the optimality of the selected rendezvous point on the target trajectory is directly dependent on the robot-trajectory planning technique. Thus, for the solution of the general interception problem, three issues must be addressed: (i) optimal rendezvous-point selection, (ii) optimal robot-trajectory planning, and (iii) replanning in response to gross changes in the predicted target trajectory. The effect of uncertainties in the target-trajectory prediction must be considered at each planning stage. Herein, solutions to the first two problems are briefly reviewed as background to the proposed rendezvous-point replanning strategy. This strategy determines when replanning is necessary, modifies the rendezvous point, and iteratively replans robot “patch trajectories” to new interception points. Simulation results using two different on-line robot-motion-generation strategies are also presented. © 1998 John Wiley & Sons, Inc. 15: 97–114, 1998     

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.     

An imperative language for task-level planning: Definition in temporal logic

The problem of the execution of plans of actions by a robot inspired the conception of various representations. Some of them concern the problems of control theory and geometry involved in the execution of each task of a robot. We are interested in the task-level sequencing of such actions, from the point of view of planning in artificial intelligence and languages for the synchronisation of tasks. Planning formalisms are often based on predicate logic and sometimes temporal logic. Robot programming languages, at the task-level, have classical control structures derived from computer programming languages, as well as ones more specifically related to real-time execution. We propose a logical and temporal model of plans of actions augmented by an imperative control structure. We therefore define, on the basis of an interval-based temporal logic, a set of imperative control primitives that define the temporal arrangement of the actions. After that, primitives for the reaction to evolutions in the environment are defined in the same formalism, in order to respond to constraints concerning interaction and adaptation to the external world. The application of the model in a simulation system is described, as well as its use in execution monitoring systems.     

The VFO-Driven Motion Planning and Feedback Control in Polygonal Worlds for a Unicycle with Bounded Curvature of Motion

Integrated motion planning and control for the purposes of maneuvering mobile robots under state- and input constraints is a problem of vital practical importance in applications of mobile robots such as autonomous transportation. Those constraints arise naturally in practice due to specifics of robot mechanical construction and the presence of obstacles in motion environment. In contrast to approaches focusing on feedback control design under the assumption of given reference motion or motion planning with neglection of subsequent feedback motion execution, we adopt a controller-driven motion planning paradigm, which has recently gained attention of many researchers. It postulates design of motion planning algorithms dedicated to specific feedback control policies, which compute a sequence of feedback control subtasks instead of classically planned open-loop controls or parametric paths. In this spirit, we propose a motion planning algorithm driven by the VFO (Vector Field Orientation) control law for the waypoint-following task. Presented analysis of the VFO control law reveals its beneficial properties, which are subsequently utilized to solve a generally nonlinear and non-convex optimal motion planning problem by formulating it as a mixed-integer linear program (MILP). The solution proposed in this paper yields a waypoint sequence, which is designed for execution by application of the VFO control law to drive a robot to a prescribed final configuration under an input constraint imposed by bounded curvature of robot motion and state constraints resulting from a convex decomposition of task space. Satisfaction of these constraints is guaranteed analytically and exactly, i.e., without utilization of numerical approximations. Moreover, for a given discrete set of possible waypoint orientations, the proposed algorithm computes plans optimal w.r.t. given cost functional, which can be any convex linear combination of quantities such as robot path length, curvature of robot motion, distance to imposed state constraints, etc. Furthermore, the planning algorithm exploits the possibility of both forward or backward movement of the robot to allow maneuvering in demanding environments. Generated waypoint sequences are a compact representation of a motion plan, which can be immediately executed with the VFO controller without any additional post-processing. Validity of the proposed approach has been confirmed by simulation studies and experimental motion execution with a laboratory-scale mobile robot.     

On linear logic planning and concurrency

We present an approach to linear logic planning where an explicit correspondence between partial order plans and multiplicative exponential linear logic proofs is established. This is performed by extracting partial order plans from sound and complete encodings of planning problems in multiplicative exponential linear logic. These partial order plans exhibit a non-interleaving behavioural concurrency semantics, i.e., labelled event structures. Relying on this fact, we argue that this work is a crucial step for establishing a common language for concurrency and planning that will allow to carry techniques and methods between these two fields.     

智能规划中面向简单偏好的高效求解方法

智能规划(AI planning)简称规划,是人工智能领域的一个重要分支,在各领域均有广泛应用,如工厂车间作业调度、物资运输调度、机器人动作规划以及航空航天任务规划等.传统智能规划要求规划解(动作序列)必须最终实现整个目标集合,这种目标一般被称为硬目标(hard goal).然而,许多实际问题中,求解的重点并不只是尽快实现目标以及尽量减少动作序列产生的代价,还需考虑其他因素,如资源消耗或时间约束等.为此,简单偏好(也称软目标soft goal)的概念应运而生.与硬目标相反,简单偏好是可以违背的.本质上,简单偏好用于衡量规划解质量的优劣,而不会影响规划解是否存在.现有关于简单偏好的研究进展缓慢,在规划解质量方面不尽如人意即求得的规划解与最优解的差距较大.提出了一种求解简单偏好的高效规划方法,将简单偏好表达为经典规划(classical planning)模型的一部分,并利用SMT (satisfiability modulo theories)求解器识别多个简单偏好之间的各种关系,从而约简简单偏好集,减轻规划器的求解负担.该方法的主要优势在于:一方面,提前对简单偏好集进行裁剪,在一定程度...     

Strategies for Rescheduling Tightly-Coupled Parallel Applications in Multi-Cluster Grids

As computational Grids are increasingly used for executing long running multi-phase parallel applications, it is important to develop efficient rescheduling frameworks that adapt application execution in response to resource and application dynamics. In this paper, three strategies or algorithms have been developed for deciding when and where to reschedule parallel applications that execute on multi-cluster Grids. The algorithms derive rescheduling plans that consist of potential points in application execution for rescheduling and schedules of resources for application execution between two consecutive rescheduling points. Using large number of simulations, it is shown that the rescheduling plans developed by the algorithms can lead to large decrease in application execution times when compared to executions without rescheduling on dynamic Grid resources. The rescheduling plans generated by the algorithms are also shown to be competitive when compared to the near-optimal plans generated by brute-force methods. Of the algorithms, genetic algorithm yielded the most efficient rescheduling plans with 9–12% smaller average execution times than the other algorithms.     

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.     

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.     

On nonexistence of boundary arcs in control problems with bounded state variables

When solving optimal control problems with bounded state variables, one must determine whether the optimal trajectory intersects the boundary only at isolated points in time (boundary point) or remains on the boundary for a nonzero length of time (boundary arc). Previously, this determination has been made by trial and error. The task is complicated by the fact that the necessary conditions in common use for these problems assume that the solution has a boundary arc, and can thus yield a boundary arc when the solution has no boundary arc. In this paper the necessary conditions of [1] are used to derive conditions under which the optimal trajectory cannot have a boundary arc. These conditions include the condition for no boundary arcs developed in [1] as a special case. The application of these conditions is illustrated via several examples.     

Modeling Control Speculation for Timing Analysis

The schedulability analysis of real-time embedded systems requires worst case execution time (WCET) analysis for the individual tasks. Bounding WCET involves not only language-level program path analysis, but also modeling the performance impact of complex micro-architectural features present in modern processors. In this paper, we statically analyze the execution time of embedded software on processors with speculative execution. The speculation of conditional branch outcomes (branch prediction) significantly improves a program's execution time. Thus, accurate modeling of control speculation is important for calculating tight WCET estimates. We present a parameterized framework to model the different branch prediction schemes. We further consider the complex interaction between speculative execution and instruction cache performance, that is, the fact that speculatively executed blocks can generate additional cache hits/misses. We extend our modeling to capture this effect of branch prediction on cache performance. Starting with the control flow graph of a program, our technique uses integer linear programming to estimate the program's WCET. The accuracy of our method is demonstrated by tight estimates obtained on realistic benchmarks.     

Coordinated execution of tasks in a multiagent environment

This correspondence describes the application of discrete event control methods to provide conflict-free plan execution in a multiagent environment. This work uses planning methods to generate plans for multiple robots, and the plans are then compiled into Petri nets for analysis, execution, and monitoring. Supervisory control techniques are applied to the Petri net controller for the purpose of dealing with conflicts that arise due to the presence of shared resources. Furthermore, by preserving the state of the system replanning can occur at any time during execution to deal with unforeseen events.     

基于本体的应急预案研究

应急预案描述了应急响应事件处置的领域知识，根据处置预案、资源状态和事件状态，系统自动生成初步处置方案，经处置人员调整认可后形成处置方案，应急预案是高效应急响应的基础。基于知识的规划（KB Planning）和基于过程模板（Planning with templates）的规划是当前研究热点，该文在这一方面进行了有益的探索。以ABC本体作为顶层本体，引入了多个表示应急预案的词汇，形成应急预案本体，以此为基础，给出了应急预案表示，并根据应急预案本体和应急预案表示，定义了应急预案XML Schema来存储应急预案。     

Optimal design of sandwich and laminated composite plates based on the planning of experiments

Optimal design problems of sandwich plates with soft core and laminated composite face layers, and multilayered composite plates are investigated. The optimal design problems are solved by using the method of the planning of experiments. The optimization procedure is divided into the following stages: choice of control parameters and establishment of the domain of search, elaboration of plans of experiment for the chosen number of reference points, execution of the experiment, determination of simple mathematical models from the experimental data, design of the structure on the basis of the mathematical models discovered, and finally verification experiments at the point of the optimal solution. Vibration and damping analysis is performed by using a sandwich plate finite elements based on a broken line model. Damping properties of the core and face layers of the plate are taken into account in the optimal design. Modal loss factors are computed using the method of complex eigenvalues or the energy method. Frequencies and modal loss factors of the plate are constraints in the optimal design problem. There are also constraints on geometrical parameters and the bending stiffness of the plate. The mass of the plate is the objective function. Design parameters are the thickness of the plate layers. In the points of experiments computer simulation using FEM is carried out. Using this information, simple mathematical models for frequencies and modal loss factors for the plate are determined. These simple mathematical functions are used as constraints in the nonlinear programming problem, which is solved by random search and the penalty function method. Numerical examples of the optimal design of clamped sandwich and simply supported laminated composite plates are presented. A significant improvement of damping properties of a sandwich plate is observed in comparison with a simple plate of equal natural frequencies.     

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.