Towards the planning of actions in complex environments

This paper investigates the requirements for planning systems that must plan actions in complex environments. First the general characteristics of planning systems are described which are then compared to the properties of classical planners. It appears that most of these planning systems are not capable of planning actions in non trivial environments. Therefore an alternative approach is proposed where planning is no longer independent of the execution of the plan. Essential to this process is the ability of the system to observe its environment and adapt its behaviour according to sensory data. These characteristics make the system an open system contrary to traditional planners that are characterised by a closed system architecture.     

Toward a situation model in a cognitive architecture

The ability to coherently represent information that is situationally relevant is vitally important to perform any complex task, especially when that task involves coordinating with team members. This paper introduces an approach to dynamically represent situation information within the ACT-R cognitive architecture in the context of a synthetic teammate project. The situation model represents the synthetic teammate’s mental model of the objects, events, actions, and relationships encountered in a complex task simulation. The situation model grounds textual information from the language analysis component into knowledge usable by the agent-environment interaction component. The situation model is a key component of the synthetic teammate as it provides the primary interface between arguably distinct cognitive processes modeled within the synthetic teammate (e.g., language processing and interactions with the task environment). This work has provided some evidence that reasoning about complex situations requires more than simple mental representations and requires mental processes involving multiple steps. Additionally, the work has revealed an initial method for reasoning across the various dimensions of situations. One purpose of the research is to demonstrate that this approach to implementing a situation model provides a robust capability to handle tasks in which an agent must construct a mental model from textual information, reason about complex relationships between objects, events, and actions in its environment, and appropriately communicate with task participants using natural language. In this paper we describe an approach for modeling situationally relevant information, provide a detailed example, discuss challenges faced, and present research plans for the situation model.     

Deliberation scheduling using GSMDPs in stochastic asynchronous domains

We propose a new decision-theoretic approach for solving execution-time deliberation scheduling problems using recent advances in Generalized Semi-Markov Decision Processes (GSMDPs). In particular, we use GSMDPs to more accurately model domains in which planning and execution occur concurrently, plan improvement actions have uncertain effects and duration, and events (such as threats) occur asynchronously and stochastically. In this way, agents develop a continuous-time deliberation policy offline which can then be consulted to dynamically select deliberation-level and domain-level actions at plan execution-time. We demonstrate a significant improvement in expressibility over previous discrete-time approximate models in which mission phase duration was fixed, failure events were synchronized with phase transitions, and planning time was discretized into constant-sized planning quanta.     

The relationship of strategic and marketing plans

Results from four company surveys were used to examine the relationship of strategic and marketing plans, by comparing the results with a standardised relationship attained through a search of the literature. Although many differences are to be found in the utilisation of planning terminology, a standardised planning framework was established in order to present and discuss the results. Less than half the companies prepare their strategic plan first as a framework for the marketing plan, just over one tenth explained the strategic plan as giving overall company direction, while only less than half were able to explain marketing inputs to the strategic plan. The results suggest a total of seven implications for companies and two for the body of knowledge within the literature.     

A cognitive model of spatial path-planning

Planning a path to a destination, given a number of options and obstacles, is a common task. We suggest a two-component cognitive model that combines retrieval of knowledge about the environment with search guided by visual perception. In the first component, subsymbolic information, acquired during navigation, aids in the retrieval of declarative information representing possible paths to take. In the second component, visual information directs the search, which in turn creates knowledge for the first component. The model is implemented using the ACT-R cognitive architecture and makes realistic assumptions about memory access and shifts in visual attention. We present simulation results for memory-based high-level navigation in grid and tree structures, and visual navigation in mazes, varying relevant cognitive (retrieval noise and visual finsts) and environmental (maze and path size) parameters. The visual component is evaluated with data from a multi-robot control experiment, where subjects planned paths for robots to explore a building. We describe a method to compare trajectories without referring to aligned points in the itinerary. The evaluation shows that the model provides a good fit, but also that planning strategies may vary with task loads.     

电热耦合综合能源系统双阶段多目标规划研究

多能耦合系统是未来分布式能源供给方式的重要发展方向。为了实现电热耦合能源供给系统的合理规划、促进能源供给与消费的经济与环保的协调发展,提出一种面向电热耦合能源系统的综合能源系统双阶段规划优化方法。模型的第一阶段是在投资和环境最优的目标下实现电热耦合综合能源系统的合理规划,第二阶段是在考虑设备运行特性的基础上对规划的结果进行运行优化,以获得能源系统的最优运行方案,并从多个指标验证规划方案的合理性。利用NSGA-II算法求得模型进的帕累托解集,使用多准则妥协优化法从帕累托解集中决策出最优配置方案。仿真结果表明,提出的双阶段多目标综合能源规划方法能够实现能源供给系统的经济与环保双优。     

Process planning for circuit card assembly on a series of dual head placement machines

This paper proposes an approach for prescribing the inter-related decisions that prescribe a process plan for a series of dual head placement machines. The goal is to provide a means of rapidly prescribing a process plan that seeks to minimize cycle time (equivalently, maximize throughput rate) by balancing workloads assigned to heads when assembling a given type of circuit card. Our approach decomposes process planning decisions into four related problems. This paper explores list-processing heuristics for the first of these problems and adapts an optimizing method for the fourth. It also integrates these two methods with others to optimize the second and third of these problems, reporting computational tests that evaluate the overall approach to all four problems and assessing the degree to which the decomposition approach is able to balance workloads assigned and the run time required to do so. Resulting cycle times are also analyzed. These test results demonstrate that the overall approach provides a means of prescribing effective process plans within a run time acceptable to process planners.     

Water resources planning based on complex system dynamics: A case study of Tianjin city

A complex system dynamic (SD) model focusing on water resources, termed as TianjinSD, is developed for the integrated and scientific management of the water resources of Tianjin, which contains information feedback that governs interactions in the system and is capable of synthesizing component-level knowledge into system behavior simulation at an integrated level, thus presenting reasonable predictive results for policy-making on water resources allocation and management. As for the Tianjin city, interactions among 96 components for 12 years are explored and four planning alternatives are chosen, one of which is based on the conventional mode assuming that the existing pattern of human activities will be prevailed, while the others are alternative planning designs based on the interaction of local authorities and planning researchers. Optimal mode is therefore obtained according to different scenarios when compared the simulation results for evaluation of different decisions and dynamic consequences.     

Bilattice logic of epistemic actions and knowledge

Baltag, Moss, and Solecki proposed an expansion of classical modal logic, called logic of epistemic actions and knowledge (EAK), in which one can reason about knowledge and change of knowledge. Kurz and Palmigiano showed how duality theory provides a flexible framework for modeling such epistemic changes, allowing one to develop dynamic epistemic logics on a weaker propositional basis than classical logic (for example an intuitionistic basis). In this paper we show how the techniques of Kurz and Palmigiano can be further extended to define and axiomatize a bilattice logic of epistemic actions and knowledge (BEAK). Our propositional basis is a modal expansion of the well-known four-valued logic of Belnap and Dunn, which is a system designed for handling inconsistent as well as potentially conflicting information. These features, we believe, make our framework particularly promising from a computer science perspective.     

Linking stakeholder visions with resource allocation scenarios and multi-criteria assessment

Stakeholders and decision makers often develop visions of the ideal-type future as a response to complex societal problems and design their actions accordingly. However, these actors sometimes have a limited understanding as to whether their visions are feasible, what action is required and what the potential consequences are. This paper presents a methodology for linking visions with quantitative resource allocation scenarios which show different options in implementing the visions. The consequences are then appraised by multi-criteria assessment in order to find optimal and acceptable ways of implementation. As a result, stakeholders and decision makers learn about their visions and may even rethink them before decision making. The methodology thus couples visionary ideas with analytical information, providing a novel approach using quantitative techniques in a soft framework. The methodology is illustrated via a real-world case study concerning the future energy system in a small Swiss community.     

An estimate of the objective function optimum for the network Steiner problem

The study area is La Colacha sub-basins from Arroyos Menores basins, natural areas at West and South of Río Cuarto in Province of Córdoba of Argentina, fertile with loess soils and monsoon temperate climate, but with soil erosions including regressive gullies that degrade them progressively. Cultivated gently since some hundred 60 years coordinated action planning has become necessary to conserve lands while keeping good agro-production. Some of the authors were engineers trained here and other had former experience in Salta, Argentina, using a set of Decision Theory Methods, (DTM), to consider the possible systems of soil uses and actions to be recommended. After having improved data on soils and on hydrology for the study area, they have adapted as academic study these DTM for Arroyos Menores areas where gully erosions are severe, also intending to offer better view for the necessary planning to have policies of actions and uses, done by the local society decision and services, and in part by land owners. This paper concentrates on the application of a set of discrete multi-criteria models (MCDM), where alternatives were global, considering soil conservation and hydrologic management actions, and main types of use of soil, and where criteria were about the diverse consequences, grouped as environmental, economic, and social. As methods they have used for the sub areas PROMETHEE with sets of weights, comparing results with ELECTRE with same data and weights, and with AHP method needing comparisons of criteria by authors, getting somehow compatible results, but finding that the “weighted PROMETHEE II” was the more adapted for the present study. The system and results are very global, and are offered as indications for planning, that will require using detailed engineering and administration aspects for series of local singular actions or for management pursued in time.     

Decision architectures for production planning in multi-stage multi-product manufacturing systems

Recent trends in automated manufacturing call for hierarchical decision architectures for production planning, suitable for integration with part flow controls. Different design approaches are currently adopted for implementing production planning architectures, depending either on the objective of defining a centralized production plan for the whole manufacturing system (as in the case of MRP and OPT), or on the desire of coordinating local plans for the component work cells (as for JIT). The paper analyzes such approaches by use of a unifying mathematical formulation of the production plan optimization problem, to recognize the main features of the existing planning approaches, and compare their usefulness in different manufacturing processes.     

Concepts and fuzzy models for behavior-based robotics

In this paper, we propose a modeling paradigm that uses fuzzy sets to represent concepts on which control modules of a behavior-based autonomous robot operate. The primitives defined in the modeling paradigm are expressive enough to represent the knowledge needed by planning, coordination, and reactive control of a multi-robot control system. At the same time, it provides a well-founded tool to represent in a compact way the data interpretations needed to reason effectively about what is happening in the world and what is desired to happen. This modeling paradigm makes the design of behavior, planning, and coordination modules easy, since its primitives are simple and expressive. Moreover, it provides a sound framework to deal with uncertainty in sensing and world modeling.     

Handling fuzzy temporal constraints in a planning environment

An interleaved integration of the planning and scheduling process is presented with the idea of including soft temporal constraints in a partial order planner that is being used as the core module of an intelligent decision support system for the design forest fire fighting plans. These soft temporal constraints have been defined through fuzzy sets. This representation allows us a flexible representation and handling of temporal information. The scheduler model consists of a fuzzy temporal constraints network whose main goal is the consistency checking of the network associated to each partial order plan. Moreover, we present a model of estimating this consistency, and show the monitoring and rescheduling capabilities of the system. The resulting approach is able to tackle problems with ill defined knowledge, to obtain plans that are approximately consistent and to adapt the execution of plans to unexpected delays. This work has been partially supported under the project MCyT TIC2002-04146-C05-2 and the contract NET033957 with the Andalusian Regional Government.     

Smart charging profiles for electric vehicles

Electric vehicles (EVs) can help decarbonise the transportation sector, which is responsible for a great share of greenhouse gas emissions. Although different measures have been introduced to foster the penetration of EVs in the society, they have not been deployed at a large scale yet. Electric companies are concerned about the effects of introducing EVs into the grid, especially with a large amount. The charging pattern of EVs is the main factor that determines these effects. Unregulated charging (probably when returning home) would have undesirable consequences (e.g. increase in variable costs, emissions, reduction of reliability) for the system, it is therefore necessary to develop an “intelligent” charging strategy. These characteristics justify the existence of different smart charging profiles. It is also important to assess the effect of using day-ahead management systems instead of pre-set profiles. This document compares different possible strategies for charging EVs and their consequences in the power system. The impact on variable costs, emissions and renewable energy sources integration will be obtained using an operation planning model. The Spanish power system for 2020 is analysed under different EV penetration levels and charging strategies. The results show the benefits of using smart charging profiles instead of an unregulated profile, obtaining large cost reductions and maintaining system reliability levels. Moreover, the benefits of using a day-ahead management system are also evaluated, resulting in a small reduction of system variable cost compared to the use of pre-defined charging profiles.     

Additive relations and function tables

We present work with a second grade classroom where we carried out a teaching experiment that attempted to bring out the algebraic character of arithmetic. In this paper, we specifically illustrate our work with the second graders on additive relations, through the children’s work with function tables. We explore the different ways in which the children represented the information of a problem in the form of a self-designed function table. We argue that the choices children make about the kind of information to represent or not, as well as the way in which they constructed their tables, highlight some of the issues that children may find relevant in their construction of function tables. This open-ended format pointed to how they were understanding and appropriating tables into their thinking about additive relations.     

The signal model: A model for competing risks of opportunistic maintenance

This paper presents a competing risks reliability model for a system that releases signals each time its condition deteriorates. The released signals are used to inform opportunistic maintenance. The model provides a framework for the determination of the underlying system lifetime from right-censored data, without requiring explicit assumptions about the type of censoring to be made. The parameters of the model are estimated from observational data by using maximum likelihood estimation. We illustrate the estimation process through a simulation study. The proposed signal model can be used to support decision-making in optimising preventive maintenance: at a component level, estimates of the underlying failure distribution can be used to identify the critical signal that would trigger maintenance of the individual component; at a multi-component system level, accurate estimates of the component underlying lifetimes are important when making general maintenance decisions. The benefit of good estimation from censored data, when adequate knowledge about the dependence structure is not available, may justify the additional data collection cost in cases where full signal data is not available.     

Forest planning at the tactical level

Forest management and planning can involve large tracts of land involving numerous areal units. One approach to plan activities for a forested region over decades involves breaking down decision making into three components: strategic, tactical, and operational. Each level of the management hierarchy can involve the development and application of optimization models. These models typically aid in exploring management alternatives as well as multi-objective tradeoffs. Even though a strategic model can provide support for long term management at a broad scale, solutions may not be feasible at an operational level. Tactical level modeling helps to bridge solutions reached at a strategic level using operational planning information. This paper presents several tactical level planning models that have been developed as a part of a research effort supported by the US Forest Service. These models have been utilized in land use management and planning by the US Forest Service through a specially developed spatial decision support system.