Multi-model cooperative task assignment and path planning of multiple UCAV formation

Multimedia Tools and Applications - Multi-model techniques have shown an outstanding effectiveness in the cooperative task assignment and path planning of the unmanned combat aerial vehicle(UCAV)...     

Aligning Real Process Executions and Prescriptive Process Models through Automated Planning

Modern organizations execute processes to deliver product and services, whose enactment needs to adhere to laws, regulations and standards. Conformance checking is the problem of pinpointing where deviations are observed. This paper shows how instances of the conformance checking problem can be represented as planning problems in PDDL (Planning Domain Definition Language) for which planners can find a correct solution in a finite amount of time. If conformance checking problems are converted into planning problems, one can seamlessly update to the recent versions of the best performing automated planners, with evident advantages in term of versatility and customization. The paper also reports on results of experiments conducted on two real-life case studies and on eight larger synthetic ones, mainly using the Fast-downward planner framework to solve the planning problems due to its performances. Some experiments were also repeated though other planners to concretely showcase the versatility of our approach. The results show that, when process models and event logs are of considerable size, our approach outperforms existing ones even by several orders of magnitude. Even more remarkably, when process models are extremely large and event log traces very long, the existing approaches are unable to terminate because they run out of memory, while our approach is able to properly complete the alignment task.     

Using Many-Sorted Logic in the Object-Oriented Data Model for Fast Robot Task Planning

Search space explosion is a critical problem in robot task planning. This problem limits current robot task planners to solve only simple block world problems and task planning in a real robot working environment to be impractical. This problem is mainly due to the lack of utilization of domain information in task planning. In this paper, we describe a fast task planner for indoor robot applications that effectively uses domain information to speed up the planning process. In this planner, domain information is explicitly represented in an object-oriented data model (OODM) that uses many-sorted logic (MSL) representation. The OODM is convenient for the management of complex data and many-sorted logic is effective for pruning in the rule search process. An inference engine is designed to take advantage of the salient features of these two techniques for fast task planning. A simulation example and complexity analysis are given to demonstrate the advantage of the proposed task planner.     

多无人机任务推演系统研究

研究多无人机任务推演系统的设计与实现过程,在多无人机任务推演系统具体需求的基础上,设计系统的分层体系结构与具体功能模块。针对系统实现过程中的多机协同任务分配及航迹规划问题,建立相应的数学模型并使用改进遗传算法对模型进行求解。使用分布式处理技术解决模型解算与实时数据显示的矛盾。基于MapX控件实现系统中战场地图的显示与操作功能。该系统对制定合理作战方案、发挥无人机的最佳作战效能具有重要意义。     

Learning local linear Jacobians for flexible and adaptive robot arm control

Successful planning and control of robots strongly depends on the quality of kinematic models, which define mappings between configuration space (e.g. joint angles) and task space (e.g. Cartesian coordinates of the end effector). Often these models are predefined, in which case, for example, unforeseen bodily changes may result in unpredictable behavior. We are interested in a learning approach that can adapt to such changes—be they due to motor or sensory failures, or also due to the flexible extension of the robot body by, for example, the usage of tools. We focus on learning locally linear forward velocity kinematics models by means of the neuro-evolution approach XCSF. The algorithm learns self-supervised, executing movements autonomously by means of goal-babbling. It preserves actuator redundancies, which can be exploited during movement execution to fulfill current task constraints. For detailed evaluation purposes, we study the performance of XCSF when learning to control an anthropomorphic seven degrees of freedom arm in simulation. We show that XCSF can learn large forward velocity kinematic mappings autonomously and rather independently of the task space representation provided. The resulting mapping is highly suitable to resolve redundancies on the fly during inverse, goal-directed control.     

对三种典型分布式任务分配算法的分析

本文先分析了基于图论的分配算法，整数规划方法和试探法等几种典型的分布式任务分配算法的基本思想、特点，不足和算法复杂度，以及可进一步改进之处，然后给出了一种试探法的改进算法，并简单讨论了其特点和性能，最后指出了分布式任务分配的发展方向。     

Competences-based performance model of multi-skilled workers with learning and forgetting

The relationship between performance and experience is non-linear, thus planning models that seek to manage workforce development through task assignment are difficult to solve. This gets even more complicated when taking into account multi-skilled workers that are capable of performing a variety of tasks. In this paper we develop a competences-based analytical model of the performance of multi-skilled workers undertaking repetitive tasks, taking into account learning and forgetting. A learning curve can be used to estimate improvement when repeating the same operation. Inverse phenomenon is forgetting, which can occur due to interruption in the production process. The Performance Evaluation Algorithm (PEA) was developed for two cases: fixed shift duration and fixed production output. The aim was to build a tool that better describes the capabilities of workers to perform repetitive tasks by binding together hierarchical competences modeled as a weighted digraph together with a learning and forgetting curve model (LFCM) to express individual learning rates.     

Optimal methods for resource allocation and scheduling: a cross-disciplinary survey

Classical scheduling formulations typically assume static resource requirements and focus on deciding when to start the problem activities, so as to optimize some performance metric. In many practical cases, however, the decision maker has the ability to choose the resource assignment as well as the starting times: this is a far-from-trivial task, with deep implications on the quality of the final schedule. Joint resource assignment and scheduling problems are incredibly challenging from a computational perspective. They have been subject of active research in Constraint Programming (CP) and in Operations Research (OR) for a few decades, with quite difference techniques. Both the approaches report individual successes, but they overall perform equally well or (from a different perspective) equally poorly. In particular, despite the well known effectiveness of global constraints for scheduling, comparable results for joint filtering of assignment and scheduling variables have not yet been achieved. Recently, hybrid approaches have been applied to this class of problems: most of them work by splitting the overall problem into an assignment and a scheduling subparts; those are solved in an iterative and interactive fashion with a mix of CP and OR techniques, often reporting impressive speed-ups compared to pure CP and OR methods. Motivated by the success of hybrid algorithms on resource assignment and scheduling, we provide a cross-disciplinary survey on such problems, including CP, OR and hybrid approaches. The main effort is to identify key modeling and solution techniques: they may then be applied in the construction of new hybrid algorithms, or they may provide ideas for novel filtering methods (possibly based on decomposition, or on alternative representations of the domain store). In detail, we take a constraint-based perspective and, according to the equation CP = model + propagation + search, we give an overview of state-of-art models, propagation/bounding techniques and search strategies.     

人工智能中的合作分布式规划

人工智能中的合作分布式规划是分布式问题求解的一个特例,同时又具有规划问题的特征,其应用范围广,研究意义大。文章对该领域近来的发展进行了总结,以规划任务表达和分解、任务分派、任务完成为主线,给出已有的研究成果、目前的研究现状,并着重讨论了任务完成阶段运用的各种协调技术,文末还指出未来的研究方向。     

Learning curve models and applications: Literature review and research directions

Learning curves (LCs) are deemed effective tools for monitoring the performance of workers exposed to a new task. LCs provide a mathematical representation of the learning process that takes place as task repetition occurs. These curves were originally proposed by Wright in 1936 upon observing cost reduction due to repetitive procedures in production plants. Since then, LCs have been used to estimate the time required to complete production runs and the reduction in production costs as learning takes place, as well as to assign workers to tasks based on their performance profile. Further, effects of task interruption on workers’ performance have also being modeled by modifications on the LCs. This wide variety of applications justifies the relevance of LCs in industrial applications. This paper presents the state of the art in the literature on learning and forgetting curves, describing the existing models, their limitations, and reported applications. Directions for future research on the subject are eventually proposed.

Relevance to industry

The Learning Curve (LC) models described here can be used in a wide variety of industrial applications where workers endeavor new tasks. LC modeling enables better assignment of tasks to workers and more efficient production planning, and reduces production costs.     

A methodology for knowledge based decision support for facilities layout planning

The area of facilities layout planning contains a mix of specialized models in addition to a requirement for human experience and subjective judgments in the decision process. In this paper, we recognize that the task of choosing a suitable facilities layout planning model for a given problem is a non-trivial one, due to the unique requirements of individual situations, and the varying nature of the available models. To facilitate the selection of an appropriate model for facilities layout problems, we propose a methodology for the design of a knowledge based Decision Support System (DSS) which is calibrated with heuristics elicited from experienced layout planners. The rationale and structure of the model are discussed, as are its mode of usage and future research issues.     

Designing and managing local area networks: A survey of LAN personnel

A survey was conducted of planners of 45 local area networks (LANS) of personal computers used for office work found in a large metropolitan area. Usable responses were obtained from the planners of 27 of the LANS, a response rate of 60%. The planners were asked to rank 79 LAN planning criteria and to identify problems they encountered in managing the LANS.Twenty-one planning criteria were identified by planners as important or very important in the design of LANS. Five planning criteria were identified as unimportant by the planners. The planning criteria considered important varied between general purpose LANS and LANS primarily serving special applications. The planning criteria considered important also varied with LAN size.Few problems were encountered by the planners in this survey and the amount of time the planners felt should be committed to managing the LANS was modest. Installation problems were the most frequently cited.     

Sequential Monte Carlo in reachability heuristics for probabilistic planning

Some of the current best conformant probabilistic planners focus on finding a fixed length plan with maximal probability. While these approaches can find optimal solutions, they often do not scale for large problems or plan lengths. As has been shown in classical planning, heuristic search outperforms bounded length search (especially when an appropriate plan length is not given a priori). The problem with applying heuristic search in probabilistic planning is that effective heuristics are as yet lacking.In this work, we apply heuristic search to conformant probabilistic planning by adapting planning graph heuristics developed for non-deterministic planning. We evaluate a straight-forward application of these planning graph techniques, which amounts to exactly computing a distribution over many relaxed planning graphs (one planning graph for each joint outcome of uncertain actions at each time step). Computing this distribution is costly, so we apply Sequential Monte Carlo (SMC) to approximate it. One important issue that we explore in this work is how to automatically determine the number of samples required for effective heuristic computation. We empirically demonstrate on several domains how our efficient, but sometimes suboptimal, approach enables our planner to solve much larger problems than an existing optimal bounded length probabilistic planner and still find reasonable quality solutions.     

Ad hoc teamwork by learning teammates’ task

This paper addresses the problem of ad hoc teamwork, where a learning agent engages in a cooperative task with other (unknown) agents. The agent must effectively coordinate with the other agents towards completion of the intended task, not relying on any pre-defined coordination strategy. We contribute a new perspective on the ad hoc teamwork problem and propose that, in general, the learning agent should not only identify (and coordinate with) the teammates’ strategy but also identify the task to be completed. In our approach to the ad hoc teamwork problem, we represent tasks as fully cooperative matrix games. Relying exclusively on observations of the behavior of the teammates, the learning agent must identify the task at hand (namely, the corresponding payoff function) from a set of possible tasks and adapt to the teammates’ behavior. Teammates are assumed to follow a bounded-rationality best-response model and thus also adapt their behavior to that of the learning agent. We formalize the ad hoc teamwork problem as a sequential decision problem and propose two novel approaches to address it. In particular, we propose (i) the use of an online learning approach that considers the different tasks depending on their ability to predict the behavior of the teammate; and (ii) a decision-theoretic approach that models the ad hoc teamwork problem as a partially observable Markov decision problem. We provide theoretical bounds of the performance of both approaches and evaluate their performance in several domains of different complexity.     

SimMan—A simulation model for workforce capacity planning

Motivated by the difficulty in predicting the actual performance of workforce capacity planning solutions obtained from mathematical models, we develop a simulator, SimMan, to serve as a test bed for evaluating the effectiveness and robustness of different planning options and assignment rules. To facilitate the ease of use, the simulator is developed in C++ language with a modular structure so that different users or planners can customize the modules according to their specifications. We further develop a mathematical model for the workforce capacity planning problem, where the uncertainty of the demand is handled by the safety stock concepts. We also suggest a number of practical planning alternatives and assignment rules based on information from IBM. These rules are implemented as modules in SimMan and tested numerically to provide managerial insights.     

Face-to-face versus computer-mediated communication in a primary school setting

Computer-mediated communication is increasingly being used to support cooperative problem solving and decision making in schools. Despite the large body of literature on cooperative or collaborative learning, few studies have explicitly compared peer learning in face-to-face (FTF) versus computer-mediated communication (CMC) situations. In the present study, the effects of the use of cooperative FTF groups versus and CMC groups on the interactive behavior and task performance of 42 dyads of sixth grade Dutch primary school students working collaboratively on a mathematics task were examined. The results show the FTF dyads to provide significantly more high-level elaborations than the CMC dyads when solving the mathematics problems. In contrast, the CMC dyads provided about three times as many regulative utterances and about twice as many affective utterances as the FTF dyads. The FTF dyads attained higher performance scores than the CMC dyads, and they were also relatively more satisfied with their cooperation.     

Adaptive task assignment for multiple mobile robots via swarm intelligence approach

This paper describes an adaptive task assignment method for a team of fully distributed mobile robots with initially identical functionalities in unknown task environments. A hierarchical assignment architecture is established for each individual robot. In the higher hierarchy, we employ a simple self-reinforcement learning model inspired by the behavior of social insects to differentiate the initially identical robots into “specialists” of different task types, resulting in stable and flexible division of labor; on the other hand, in dealing with the cooperation problem of the robots engaged in the same type of task, Ant System algorithm is adopted to organize low-level task assignment. To avoid using a centralized component, a “local blackboard” communication mechanism is utilized for knowledge sharing. The proposed method allows the robot team members to adapt themselves to the unknown dynamic environments, respond flexibly to the environmental perturbations and robustly to the modifications in the team arising from mechanical failure. The effectiveness of the presented method is validated in two different task domains: a cooperative concurrent foraging task and a cooperative collection task.