An Introduction to Interactive Discourse Processing from the Perspective of Plan Recognition and Text Planning

People engage in task-oriented dialogues to carry out or plan a task. Each participant in such an interaction must be capable of processing plans in two ways. First, each participant must be capable of understanding the plans that the other participant is using. Researchers have developed theories and models about how computational systems should go about recognizing the plans and goals of another participant, both at the subject-matter level and at the level of the communication. This area of research is called plan recognition. Secondly, each participant must be able to make their owns plans to communicate. This area of natural language research is called text planning.Interactive systems -- systems that understand natural language and that can produce natural language to engage in a task-related interaction -- must address the issue of how understanding plans (the process of plan recognition) relates to making plans for the interaction (the process of text planning). We provide an introduction to these two research areas in natural language processing. Those who need to be familiar with both areas -- to conduct research at their intersection -- will find this introduction useful for building systems that both understand what people are trying to do when they speak and that can actively participate in the interaction.     

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.     

A systems framework for environmental-research planning

Environmental-research planning can be important in providing for industrial and urban growth while protecting public health and valuable natural resources. A systems framework for anticipating future changes and for gathering germane information is one basis for developing informed long-range plans. The planning approach described here emphasizes the evaluation of patterns of social and technical information exchange using examples of the natural and man-dominated Puerto Rico island system.The patterns of energy and materials flows in Puerto Rico have undergone distinctive changes during the pre-Columbian period, the agricultural period and the early industrialization and post World War II periods. By anticipating the range of potential future changes in energy and materials supplies, the kinds of future environmental problems may be anticipated. This may be useful for identifying possible sources and distribution of environmental pollution and point to research areas for assessing potential effects. In addition, a systems framework provides a basis for developing cost-effective pollution-control strategies that recognize the interdependence among urban industrial and natural resources and that may utilize diverse environmental capabilities for distributing and assimilating pollutant loads.     

An approach to efficient planning with numerical fluents and multi-criteria plan quality

Dealing with numerical information is practically important in many real-world planning domains where the executability of an action can depend on certain numerical conditions, and the action effects can consume or renew some critical continuous resources, which in pddl can be represented by numerical fluents. When a planning problem involves numerical fluents, the quality of the solutions can be expressed by an objective function that can take different plan quality criteria into account.We propose an incremental approach to automated planning with numerical fluents and multi-criteria objective functions for pddl numerical planning problems. The techniques in this paper significantly extend the framework of planning with action graphs and local search implemented in the lpg planner. We define the numerical action graph (NA-graph) representation for numerical plans and we propose some new local search techniques using this representation, including a heuristic search neighborhood for NA-graphs, a heuristic evaluation function based on relaxed numerical plans, and an incremental method for plan quality optimization based on particular search restarts. Moreover, we analyze our approach through an extensive experimental study aimed at evaluating the importance of some specific techniques for the performance of the approach, and at analyzing its effectiveness in terms of fast computation of a valid plan and quality of the best plan that can be generated within a given CPU-time limit. Overall, the results show that our planner performs quite well compared to other state-of-the-art planners handling numerical fluents.     

Workload-based yard-planning system in container terminals

Traditionally, the yard-planning problem has been considered to be the assignment of yard spaces to arriving vessels in practices of container terminals. This study proposes an integrated decision-making framework for the yard-planning that simultaneously considers various resources such as storage space, yard cranes, and traffic area in container terminals for planning. The decision-making framework in this study is based on a mathematical model, which supports intelligent yard-planning activities considering work-loads on various related resources. Further, it is shown that the decision-making framework for the yard-planning can be extended to that for simultaneous decisions on yard plans and handling capacity of yard cranes per block. A heuristic algorithm is also proposed in order to reduce the computational time for planning. Numerical experiments are conducted to validate the models and algorithms proposed in this study.     

Ein Planungsansatz zum Umgang mit Störungen bei der Produktion: Die flexible Produktionsfeinplanung mithilfe von Planungsschritten

Disruptions like the breakdown of machines or delays in the delivery of materials cause generated production plans to be useless, at least in some parts. A new planning approach for coping with this uncertainty caused by disruptions is presented. Planning steps that are described by properties, preconditions and consequences are used for material requirements planning and scheduling. If their preconditions are satisfied in a certain state of environment, planning steps will be applicable. The consequences of planning steps change the state of environment. Each customer order establishes a goal of a new planning problem to be achieved by applying planning steps. Plan execution concerns all planning steps applicable in a certain state of environment. In choosing among applicable planning steps and among available resources the flexibility of plan execution is preserved.     

Structured Reactive Controllers

Service robots, such as autonomous office couriers or robot tourguides, must be both reliable and efficient. This requires them to flexibly interleave their tasks, exploit opportunities, quickly plan their course of action, and, if necessary, revise their intended activities. In this paper, we show how structured reactive controllers (SRCs) satisfy these requirements. The novel feature of SRCs is that they employ and reason about plans that specify and synchronize concurrent percept-driven behavior. Powerful control abstractions enable SRCs to integrate physical action, perception, planning, and communication in a uniform framework and to apply fast but imperfect computational methods without sacrificing reliability and flexibility. Concurrent plans are represented in a transparent and modular form so that automatic planning processes can reason about the plans and revise them. We present experiments in which SRCs are used to control two autonomous mobile robots. In one of them an SRC controlled the course of action of a museum tourguide robot that has operated for thirteen days, more than ninetyfour hours, completed 620 tours, and presented 2668 exhibits.     

Adaptive genetic algorithm for advanced planning in manufacturing supply chain

A main function for supporting global objectives in a manufacturing supply chain is planning and scheduling. This is considered such an important function because it is involved in the assignment of factory resources to production tasks. In this paper, an advanced planning model that simultaneously decides process plans and schedules was proposed for the manufacturing supply chain (MSC). The model was formulated with mixed integer programming, which considered alternative resources and sequences, a sequence-dependent setup and transportation times.The objective of the model was to analyze alternative resources and sequences to determine the schedules and operation sequences that minimize makespan. A new adaptive genetic algorithm approach was developed to solve the model. Numerical experiments were carried out to demonstrate the efficiency of the developed approach. Received: June 2005 / Accepted: December 2005     

Application of Landsat imagery in terrain analysis of Mahanadi Basin for rational land-use planning

Abstract

Soil information is a prerequisite for rational land-use planning for sustainable agricultural production. Landsat imagery provides a basic tool to study soil characteristics of judicious utilization of soil resources in the various development plans of a region. The Mahanadi Basin is endowed with a variety of geological formations, varied landscapes and faulted hills, dykes, intervening valleys, plateau and plains with luxuriant forest vegetation and ground water potential. The region remains undeveloped in spite of its rich natural resources. Recently an attempt to use aerial photographs as a base map was used for terrain analysis of the Mahanadi Basin. The results of the study are presented here.     

Multi agent architecture for dynamic incremental process planning in the flexible manufacturing system

Due to rapid changes of markets and pressures of competitions, industries are adopting their production ways to support diversity of customer’s needs and increase of new product developments. This paper deals with development of an agent-based architecture of dynamic systems for process planning in the manufacturing systems. In consideration of alternative manufacturing processes and machine tools, the process plans and the schedules of the manufacturing resources are generated incrementally and dynamically. A previously proposed negotiation protocol is customized and improved to generate suitable process plans for the target products real-timely and dynamically, based on the alternative manufacturing processes. The alternative manufacturing processes are presented by the process plan networks and the suitable process plans are searched and generated to cope with both the dynamic status and disturbances of the manufacturing systems. We initiatively combine the heuristic search algorithms of the process plan networks with the negotiation protocols, in order to generate suitable process plans.     

Output-driven information system planning: a case study

Information system (IS) plans can vary in length and detail. One must, therefore, be able to tailor the existing planning methodologies to produce the desirable outputs. This article proposes a framework of demand-centric adaptive IS planning process and applies it to a case study that demonstrates how to adapt the methodology to produce an IS plan for a small commercial bank. Following the output-driven adaptive approach, the project was completed on time with expected quality. The project document provides the bank’s management with guidelines for allocating their information resources to meet the current and future needs of business.     

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.     

A context for information systems security planning

Management is often rightfully dissatisfied with the performance of many information security efforts. After investment of considerable resources, and prolonged waiting for results, many efforts can demonstrate little if any significant improvement. This is largely due to a lack of planning. Many efforta lack explicitly articulated plans as well as specific performance milestones. Although many are loathe to admit it, information security efforts at many organizations lack formal planning and performance monitoring.Management's dissatisfaction with information security is exemplified by the seriously inadequate staffing levels found at a large number of organizations. When management is convinced that information security is a prudent investment, they will respond with additional resources.This article examines why information security efforts are often ineffective and why more formal planning efforts can alleviate this condition. It discusses tools best used to prepare an action plan for information security and gives some tips on how to sell such a plan to management. Also discussed are organizational design, policies, standards, and guidelines and other elements of a foundation that is required if an effective information security planning process is to be sustained. The article dwells on the establishment of a context for effective information security planning.     

基于预案模板的HTN规划知识建模方法及其应用

应急决策是应急管理的基础和核心,H TN( Hierarchy Mask Nct)规划为应急决策提供了一种有效手段,是目前研究的热点问题。针对基于HTN规划求解应急决策问题存在领域知识难于建模的问题,在利用基于PetriNet的工作流模型对应急预案进行建模的基础上,提出了一种将预案模板转化为H`I'N规划领域知识模型的方法。在此基础上,将基于预案模板的应急决策问题建模为应急任务规划问题,利用HTN规划系统SHOP2 (Simple Hierarchical Ordered Planner 2)进行规划求解,实现复杂应急态势条件下通过选择和组织应急计划片段科学制定应对方案,并以某区域洪灾应急响应为例开展了应用研究。     

Planning in the Real World

In this article, we describe how real world planning problems can be solved by employing Artificial Intelligence planning techniques. We introduce the paradigm of hybrid planning, which is particularly suited for applications where plans are not intended to be automatically executed by systems, but are made for humans. Hybrid planning combines hierarchical planning ?C the stepwise refinement of complex tasks ?C with explicit reasoning about causal dependencies between actions, thereby reflecting exactly the kinds of reasoning humans perform when developing plans. We show how plans are generated and how failed plans are repaired in a way that guarantees stability. Our illustrating examples are taken from a domain model for disaster relief missions enforced upon extensive floods. Finally, we present a tool to support the challenging task of constructing planning domain models. The article ends with an overview of a wide varity of actual planning applications and outlines further such in the area of cognitive technical systems.     

Integration of process planning and scheduling using chaotic particle swarm optimization algorithm

Process planning and scheduling are two of the most important manufacturing functions traditionally performed separately and sequentially. These functions being complementary and interrelated, their integration is essential for the optimal utilization of manufacturing resources. Such integration is also significant for improving the performance of the modern manufacturing system. A variety of alternative manufacturing resources (machine tools, cutting tools, tool access directions, etc.) causes integrated process planning and scheduling (IPPS) problem to be strongly NP-hard (non deterministic polynomial) in terms of combinatorial optimization. Therefore, an optimal solution for the problem is searched in a vast search space. In order to explore the search space comprehensively and avoid being trapped into local optima, this paper focuses on using the method based on the particle swarm optimization algorithm and chaos theory (cPSO). The initial solutions for the IPPS problem are presented in the form of the particles of cPSO algorithm. The particle encoding/decoding scheme is also proposed in this paper. Flexible process and scheduling plans are presented using AND/OR network and five flexibility types: machine, tool, tool access direction (TAD), process, and sequence flexibility. Optimal process plans are obtained by multi-objective optimization of production time and production cost. On the other hand, optimal scheduling plans are generated based on three objective functions: makespan, balanced level of machine utilization, and mean flow time. The proposed cPSO algorithm is implemented in Matlab environment and verified extensively using five experimental studies. The experimental results show that the proposed algorithm outperforms genetic algorithm (GA), simulated annealing (SA) based approach, and hybrid algorithm. Moreover, the scheduling plans obtained by the proposed methodology are additionally tested by Khepera II mobile robot using a laboratory model of manufacturing environment.     

A grammatical optimization approach for integrated process planning and scheduling

Rapid developments in computerized manufacturing environments and increasing overlapping in the capability of manufacturing resources provoked integration of many manufacturing functions including process planning scheduling. Several approaches have been developed in the literature in order to integrate process planning and scheduling. In this paper a novel approach which makes use of grammatical representation of generic process plans is used within a multiple objective tabu search framework in order to integrate process planning and scheduling effectively. Detailed explanations along with an example problem are presented in the paper. Proposed approach is tested on literature problems and also hypothetically generated flexible job shop scheduling problems with alternative process plans to analyze its performance and efficiency.     

An integrated multifractal modelling to urban and regional planning

In this paper, we propose a conceptual framework for the design of multifractal urban or regional development plans that adhere to five planning principles: hierarchical polycentric urban development; transit-oriented development; locally dense residential development; penetration of green areas into built-up areas across several nested scales; preservation of interconnected networks of natural and green areas having various sizes. This conceptual planning framework is based on multifractal spatial modelling, which is intrinsically multiscalar. The GIS-based software application Fractalopolis (current version 1.0) is used to apply this conceptual framework to real-world case studies. Fractalopolis helps to define where to create new housing units and new facilities in accordance with the planning principles set out above. We use Fractalopolis to create a multifractal development plan for a medium-sized French metropolitan area, namely Besançon, for the year 2026. This plan allows a realistic “soft” transformation the Besançon metropolitan area in keeping with the five planning principles set out above and makes the region more multifractal.     

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.     

Field trials of an energy‐aware mission planner implemented on an autonomous surface vehicle

Mission planning for autonomous marine vehicles is nontrivial due to the dynamic and uncertain nature of the marine environment. Communication can be low‐bandwidth and is not always guaranteed, so the operator must rely on the vehicles to adjust their plans according to the realized state of the environment. This paper presents the improvements made to an energy‐aware mission planner that allows it to generate and adjust plans for an autonomous surface vehicle (ASV) operating in an uncertain environment. The energy‐aware mission planning problem was redefined as a stochastic programming problem, and a two‐stage solver was developed to provide an initial plan for the ASV and then adjust it during run‐time according to predefined recourse actions. The mission planner and ASV were trialed in Lake Waverley, Tasmania. Adjusting the recourse action criteria demonstrated that the ASV could exhibit conservative or opportunistic behaviors according to the operator's preference of safety margin. In the pursuit of extending the planner's second‐stage so that it can predict a suitable recourse action ahead of time, a hybrid long short‐term memory energy forecaster was trained from the Waverley mission data. Comparison of the error between the forecaster and the test data shows that the forecaster has a reliable forecast horizon of about 10 s.