Planning Domain Research Articles

Urban remote sensing with lidar for the Smart City Concept implementation

Introduction of the problem. The paper emphasizes that the key features of the contemporary urban development have caused a number of challengers, which require the innovative technological introductions in urban studies. The research goal of this paper means representing a multifunctional approach, which combines author’s urbogeosystem (UGS) theory with the URS (Urban Remote Sensing) technique for LiDAR (Light Detection And Ranging) data processing. The key elements of the Smart City concept within a geospatial perspective. Three basic assumptions are implied due to the affiliation “a geospatial perspective ó the Smart City concept” (SCC). The five key elements of the SCC have been outlined: Innovations; Scalability; Data gathering, measuring, and mining; Addressing environmental challengers; Interlink between the smart meter information and the geo-sensor information. The urbogeosystemic approach as a tool for simulating the “smart urban environment” – a core node of the Smart City hierarchy. The urbogeosystemic ontological model has been introduced as a trinity-tripod (urban citizens, municipal infrastructure, urbanistic processes and phenomena). The “smart urban environment” is a core node of an urbogeosystem. Processing results of LiDAR surveying technique. With increasing availability of LiDAR data, 3D city models of robust topology and correct geometry have become the most prominent features of the urban environment. Three key advantages of the LiDAR surveying technique have been introduced. The flowchart of the operational URS / LiDAR / GIS workflow for the Smart City implementation has been depicted. Urban Remote Sensing for data mining / city analytics and the EOS LiDAR Tool. ELiT (EOS LiDAR Tool) software is both a separate web-based (network) generator (an engine) – ELiT Server, and an integrated component of EOS Platform-as-a-Service software – ELiT Cloud. The allied one to these two products is our desktop ElitCore software, that possesses even broader functionality. The paper outlines the whole framework of urban data mining / city analytics relevant to the mentioned applications. The ELiT software use cases for the Smart Cities. A number of use cases that can be completed with the ELiT software in the common urban planning domain have been described and illustrated. Each from five scenarios presented suggests some unique solution within the frameworks of the SCC implementation. Conclusion, future research and developments. The completed research results have been summarized. An entity of the urban geoinformation space has been introduced. A geodatabase of ELiT 3D city models has been assigned a mandatory key component of the urban decision support system.

Representation Space Analytical Method for Path Planning of Free-Floating Space Manipulators

Path planning is necessary for free-floating space manipulators to perform space tasks. However, the coupled motion between manipulator and base directly acts on path planning procedure, which results in current manipulator motion state is dependent on previous motion state, so we must consider the control of base-coupled motion in path planning. In this paper, we propose a general path planning strategy for free-floating space manipulators based on the representation space (RS) analytical method. RS consists of the representation variables which are connected to task attributes and base-coupled motion simultaneously, and it can be utilized to check task realizability and construct a path planning domain for free-floating space manipulators. To analyze the effects of base-coupled motion on path planning, we classify RS into five types according to different base control modes: fixed vehicle RS (FRS), attitude constrained RS (CRS), maximum reachable RS (MRS), guaranteed RS (GRS), and partly guaranteed RS (PGRS). CRS tells us the variation of base attitude (instead of base centroid position) directly influences task realizability and path planning, so we should particularly consider the control of motion of base attitude in path planning. With appropriately considering the control of base attitude and satisfying the base attitude deflection limitation, only PGRS can be utilized to check task realizability and construct a path planning domain for free-floating space manipulators. Then, we design a path planning strategy in PGRS based on A* algorithm. Finally, we apply the RS analytical method to the 3-DOF free-floating space manipulator to visually show RS, and the effectiveness of the method is verified by a simulation experiment. The RS analytical method is appropriate to path planning for any kinds of manipulators and various tasks. We only need to select the suitable representation variables, which can faithfully reflect task and manipulator attributes.

Small ships manoeuvring estimation in planning domain

The estimation of the small ships manoeuvring performance in the planning domain represents an important aspect related to the initial design stage. The turning circle characteristics can be determined by using the method proposed by Mordvinov. Based on this method, a computer code was performed in the Research Centre of the Naval Architecture Faculty from “Dunarea de Jos” University of Galati. Practical result obtained on a planning small ship with outboard engines is presented in this paper. The computer code was integrated in the PHP software platform, dedicated to the estimation of the ships hydrodynamics performances, in the case of didactical applications or research activities.

Comparison of Virtual and Lecture-Based Methods of Education on Learning of Physiotherapy Students: A Post-Test Only Design Study

Background: The ever-increasing development of information and communication technologies has created new opportunities in the domains of curriculum planning and teaching. In this study, an exercise therapy course was held using both lecture-based and virtual teaching methods. Then, the students’ opinions were investigated and their overall levels of satisfaction were measured. Methods: This cross-sectional study was conducted among all physiotherapy students who had enrolled in the fourth semester of 2014 academic year and had selected the course of principles of exercise therapy. This study was conducted during one semester. We evaluated the effect of lecture-based and virtual (multimedia-based) methods of education according to the learning management system (LMS) among 20 students. A researcher-made questionnaire was designed regarding satisfaction with virtual versus lecture-based teaching methods; the questionnaire was scored based on a five-point Likert-type scale. Results: Of 20 students, one individual was single and 19 were married. Moreover, the mean test score was 12.38 ± 3.23 for the virtual teaching method, while it was 14.21 ± 1.88 for the lecture-based one, showing a significant difference (P = 0.03). Generally, 50% of the students preferred the virtual teaching method over the lecture-based one. In total, 75% of the students were satisfied or totally satisfied with the virtual teaching method for the exercise therapy course. Conclusions: In terms of scores, the lecture-based method was better than the virtual one. However, the level of student’s satisfaction in the virtual method was higher than the lecture-based one.

Compara

The OCAD University Visual Analytics Laboratory (VAL) has developed a taxonomy of end users, software systems, data types, tasks and interactivity within the domain of smart city transportation planning. This paper contributes to the taxonomy by creating Compara, an intuitive, interactive and searchable index that visualizes the attributes of software from a wide-range of applications and technologies. The taxonomy began as a spreadsheet that we transformed into a custom interactive data visualization that could help users find and understand existing tools and their attributes.
 The taxonomy and interactive index are a component of the iCity project which brings together academic, government, and industrial partners in order to improve the quality of life for urban residents and visitors through the development and integration of advanced IT infrastructure for the purpose of managing transit and transportation. The taxonomy and resulting tools discussed in this paper have expanded to include resources for urban planning which also impact transit and transportation.
 One of the primary uses of the taxonomy is to help various iCity project teams and stakeholders locate software that may be useful to their design and development process, as well as to understand the end-users of this technology. The taxonomy may be used to develop a city management dashboard for city planners and analysts, or equally, to design a city-services facing mobile service application.

Planning with actively eliciting preferences

Planning with preferences has been employed extensively to quickly generate high-quality plans. However, it may be difficult for the human expert to supply this information without knowledge of the reasoning employed by the planner. We consider the problem of actively eliciting preferences from a human expert during the planning process. Specifically, we study this problem in the context of the Hierarchical Task Network (HTN) planning framework as it allows easy interaction with the human. We propose an approach where the planner identifies when and where expert guidance will be most useful and seeks expert’s preferences accordingly to make better decisions. Our experimental results on several diverse planning domains show that the preferences gathered using the proposed approach improve the quality and speed of the planner, while reducing the burden on the human expert.

Succession Planning in State Health Agencies in the United States: A Brief Report.

Approximately 25% of the public health workforce plans to retire by 2020. Succession planning is a core capability of the governmental public health enterprise; however, limited data are available regarding these efforts in state health agencies (SHAs). We analyzed 2016 Workforce Gaps Survey data regarding succession planning in SHAs using the US Office of Personnel Management's (OPM's) succession planning model, including 6 domains and 27 activities. Descriptive statistics were calculated for all 41 responding SHAs. On average, SHAs self-reported adequately addressing 11 of 27 succession planning activities, with 93% of SHAs adequately addressing 1 or more activities and 61% adequately addressing 1 or more activities in each domain. The majority of OPM-recommended succession planning activities are not being addressed, and limited succession planning occurs across SHAs. Greater activity in the OPM-identified succession planning domains may help SHAs contend with significant turnover and better preserve institutional knowledge.

Posterior sampling for Monte Carlo planning under uncertainty

Monte Carlo tree search (MCTS) has recently been drawing great interest in the domain of planning and learning under uncertainty. One of the fundamental challenges is the trade-off between exploration and exploitation. To address this problem, we propose to balance between exploration and exploitation via posterior sampling in the contexts of Markov decision process (MDP) and partially observable Markov decision process (POMDP). Specifically, we treat the cumulative reward returned by taking an action from a search node in the MCTS search tree as a random variable following an unknown distribution. We parametrize this distribution by introducing necessary hidden parameters, and infer the posterior distribution of the hidden parameters in a Bayesian way. We further expand a node in the search tree by using Thompson sampling to select an action based on its posterior probability of being optimal. Following this idea, we develop Dirichlet-NormalGamma based Monte Carlo tree search (DNG-MCTS) and Dirichlet-Dirichlet-NormalGamma based partially observable Monte Carlo planning (D2NG-POMCP) algorithms respectively for Monte Carlo planning in MDPs and POMDPs. Experimental results show that the proposed algorithms outperform the state-of-the-art with better values on several benchmark problems.

Statistical Relational Learning With Unconventional String Models.

This paper shows how methods from statistical relational learning can be used to address problems in grammatical inference using model-theoretic representations of strings. These model-theoretic representations are the basis of representing formal languages logically. Conventional representations include a binary relation for order and unary relations describing mutually exclusive properties of each position in the string. This paper presents experiments on the learning of formal languages, and their stochastic counterparts, with unconventional models, which relax the mutual exclusivity condition. Unconventional models are motivated by domain-specific knowledge. Comparison of conventional and unconventional word models shows that in the domains of phonology and robotic planning and control, Markov Logic Networks With unconventional models achieve better performance and less runtime with smaller networks than Markov Logic Networks With conventional models.

The effect of degrees of bilingualism on metacognitive linguistic skills

Purpose and research question:The present study investigated whether there was any significant difference between balanced bilinguals, dominant bilinguals, limited bilinguals and monolinguals in the application of metacognitive linguistic skills while doing a reading comprehension task in the additional (English) language. Namely, how do different degrees of bilingualism affect metacognitive linguistic skills?Methodology:The present study examined three domains of metacognitive reading process (planning, monitoring and evaluating), as well as the overall use of metacognitive reading strategies. Four groups of participants (30 seventh grade students in each group) completed a Metacognitive Reading Strategies Questionnaire, aimed at measuring their metacognitive awareness in each domain after fulfilling a reading comprehension task in English.Data analysis:A multivariate analysis of variance (MANOVA) was used in order to test the differences within groups and between groups.Findings and conclusions:Level of metacognitive reading awareness was affected by the degree of bilingualism. There was a positive influence of balanced (reading and writing skills in the second language) and imbalanced (oral skills in the second language) degrees of bilingualism on metacognitive linguistic abilities. A balanced degree of bilingualism exerted the greatest influence on most of the metacognitive reading domains (planning, monitoring and the total use of metacognitive strategies).Significance:A significant advantage of balanced bilinguals over all other groups was revealed. Level of metacognitive reading awareness was affected by the degree of bilingualism. Thus, bilingual education should be taken seriously due to its positive effect on all aspects of thinking and learning. Results provided additional support to the notion that bilingualism had a positive impact on the metacognitive linguistic skills. Moreover, the balance between the languages appeared to be a significant variable. Thus, balanced bilinguals, who possessed the highest competence in the second language, showed the best performance in the domains of planning, monitoring and overall use of metacognitive reading strategies. Limited bilinguals, on the contrary, demonstrated the lowest scores in the mentioned domains. The findings also revealed the absence of any considerable difference between the groups in the domain of evaluating.

2242 Evaluation of a clinical investigation curriculum: Post-graduate outcomes

OBJECTIVES/SPECIFIC AIMS: Many CTSA programs have implemented curricula leading to clinical investigation master’s degrees. Evaluation of long-term outcomes for graduates can support curriculum improvement. METHODS/STUDY POPULATION: We evaluated graduates 1–3 years post completion of an MS in Clinical Investigation at the University of Utah. We administered the 12-item Clinical Research Appraisal Inventory (CRAI-12) describing confidence in ability to perform research tasks; we derived 6 CRAI sub-scales. Additional questionnaire items assessed current engagement in research, including percent of effort devoted to research and level of involvement in research projects using specific research methods. RESULTS/ANTICIPATED RESULTS: Graduates reported high confidence for the CRAI domain of reporting, interpreting, and presenting (on a scale of 0–20, mean 17.9±SD 1.9) and the domain of conceptualizing and collaborating (16.5±2.2) on research projects; confidence was somewhat lower in the domains of planning (14.6±3.3) and funding (14.9±2.8) projects. Graduates’ estimated current professional effort devoted to research had a median of 32%, interquartile range (IQR) 20%–70%; among graduates with clinical responsibilities, median effort devoted to research was 23%, IQR 15%–45%. In total, 74% of graduates reported moderate or high involvement in research using existing large databases, 46% reported moderate or high involvement in comparative effectiveness research, and 54% reported moderate or high involvement in quality improvement. DISCUSSION/SIGNIFICANCE OF IMPACT: A majority of clinical investigation graduates remain engaged in research but most are able to devote less than one-third of professional effort to research. Evaluation of clinical investigation graduates who have moved into their research careers can inform program directors about domains of research expertise and methodological areas that may merit additional emphasis in the curriculum.

The illicit side of urban development: Corruption and organised crime in the field of urban planning

The paper deals with issues of corruption in the planning domain. It centres on thorough analysis of the case of Desio (Milan, Italy), where a recent judicial inquiry discovered several instances of corruption related to the drafting of the local master plan, in an environment characterised by the rooted presence of a mafia-type organisation known as the ’Ndrangheta. The study sheds light on the various types of corrupt practices that prevail in the field of urban planning, the main issues at stake, and the key public agents involved. General hypotheses about the main internal and external factors fostering corruption in the planning domain are presented and discussed.

THE PSYCHOMETRIC PROPERTIES OF A RUSSIAN VERSION OF THE DISEASE-SPECIFIC LUPUSQOL QUESTIONNAIRE ASSESSING THE HEALTH-RELATED QUALITY OF LIFE IN PATIENTS WITH SYSTEMIC LUPUS ERYTHEMATOSUS

Objective: to assess the reliability, validity, and sensitivity of a Russian version of the LupusQol questionnaire. Subjects and methods. The investigation enrolled 328 patients aged 18 years or older with systemic lupus erythematosus (SLE) who met the 2012 Systemic Lupus International Collaborating Clinic (SLICC) criteria. Two tools, such as the 36-item Short-Form Health Survey (SF-36) questionnaire and LupusQol questionnaire filled out by the patients, were used to study their health-related quality of life (HRQOL). Activity was estimated using the Systemic Lupus Erythematosus Disease Activity Index 2000 (SLEDAI-2K); irreversible organ damages were evaluated by the SLICC damage index (DI). Reliability evaluation included a study of the reproducibility and internal consistency of the index. Reproducibility was evaluated using the test-retest analysis; the internal consistency was measured by calculating Cronbach’s alpha; the sensitivity was assessed by the changes in the questionnaire domains during standard therapy for SLE. Construct validity was determined in two ways: by using the known-groups method and factor analysis; criterion validity was measured by assessing the relationship with the external criteria (SF-36). Results and discussion. A total of 108 SLE patients were included to assess the reliability of the questionnaire. The results of assessing the reproducibility of the LupusQol questionnaire by the test-retest analysis showed no significant differences between the initial and repeated measures on all 8 LupusQol domains (p > 0.05). The value of Cronbach’s alpha ranged from 0.8 to 0.9 for each of the 8 questionnaire domains. After 12 months of standard therapy, 128 patients included in the assessment of questionnaire sensitivity showed a significant improvement in HRQOL for all questionnaire domains. The most significant positive changes were achieved on the domains of pain (p = 0.002), intimate relationships (IR; p=0.01), body image (p=0.0003), and fatigue (p=0.006). Validity was assessed in 328 patients with SLE. Construct validity was assessed by comparing the domains of the LupusQol and SF-36 questionnaires. A correlation (r > 0.4) was found on all the 8 LupusQol domains: physical functioning (PF), role-physical functioning (RPF), general health (GH), viability (V), social functioning (SF), and role-emotional functioning (REF) of the SF-36 questionnaire. The factor analysis of the LupusQol questionnaire revealed a satisfactory ratio of the distribution of its questions to its domains and a high correlation between the domains and its factor. Criterion validity was evaluated by comparing the LupuQol domains in 4 groups: 1) SLEDAI-2K 4) group and inactive SLE (SLEDAI-2K <4) group on the domains of PF, pain, planning, IR, dependence, and body image (p=0.07, p=0.007, p=0.0004, p=0.03, and p=0.007, respectively). Significantly lower HRQOL scores were observed in patients with irreversible organ damages. Conclusion. The Russian version of the disease-specific LupusQol questionnaire is a valid, reliable, and sensitive tool assessing HRQOL in SLE patients, which has good psychometric properties.

Modeling and Validation of a Web Ontology Language Based Disassembly Planning Information Model

Disassembly, a process of separating the end of life (EOL) product into discrete components for re-utilizing their associated residual values, is an important enabler for the sustainable manufacturing. This work focuses on the modeling of the disassembly planning related information and develops a disassembly information model (DIM) based on an extensive investigation of various informational aspects in the domain of disassembly planning. The developed DIM, which represents an appropriate systematization and classification of the products, processes, uncertainties, and degradations related information, follows a layered modeling methodology in which DIM is subdivided into layers with the intent to separate general knowledge into different levels of abstractions and reach a balance between information reusability and information usability. Two prototype disassembly planning related applications have been incorporated to validate the usability and reusability of the developed DIM.

Learning Combinatory Categorial Grammars for Plan Recognition

This paper defines a learning algorithm for plan grammars used for plan recognition. The algorithm learns Combinatory Categorial Grammars (CCGs) that capture the structure of plans from a set of successful plan execution traces paired with the goal of the actions. This work is motivated by past work on CCG learning algorithms for natural language processing, and is evaluated on five well know planning domains.

Ontology based knowledge representation technique, domain modeling languages and planners for robotic path planning: A survey

Abstract Knowledge Representation and Reasoning (KR & R) has become one of the promising fields of Artificial Intelligence. KR is dedicated towards representing information about the domain that can be utilized in path planning. Ontology based knowledge representation and reasoning techniques provide sophisticated knowledge about the environment for processing tasks or methods. Ontology helps in representing the knowledge about environment, events and actions that help in path planning and making robots more autonomous. Knowledge reasoning techniques can infer new conclusion and thus aids planning dynamically in a non-deterministic environment. In the initial sections, the representation of knowledge using ontology and the techniques for reasoning that could contribute in path planning are discussed in detail. In the following section, we also provide comparison of various planning domain modeling languages, ontology editors, planners and robot simulation tools.

Action Schema Networks: Generalised Policies With Deep Learning

In this paper, we introduce the Action Schema Network (ASNet): a neural network architecture for learning generalised policies for probabilistic planning problems. By mimicking the relational structure of planning problems, ASNets are able to adopt a weight sharing scheme which allows the network to be applied to any problem from a given planning domain. This allows the cost of training the network to be amortised over all problems in that domain. Further, we propose a training method which balances exploration and supervised training on small problems to produce a policy which remains robust when evaluated on larger problems. In experiments, we show that ASNet's learning capability allows it to significantly outperform traditional non-learning planners in several challenging domains.

Classical Planning in Deep Latent Space: Bridging the Subsymbolic-Symbolic Boundary

Current domain-independent, classical planners require symbolic models of the problem domain and instance as input, resulting in a knowledge acquisition bottleneck. Meanwhile, although deep learning has achieved significant success in many fields, the knowledge is encoded in a subsymbolic representation which is incompatible with symbolic systems such as planners. We propose LatPlan, an unsupervised architecture combining deep learning and classical planning. Given only an unlabeled set of image pairs showing a subset of transitions allowed in the environment (training inputs), and a pair of images representing the initial and the goal states (planning inputs), LatPlan finds a plan to the goal state in a symbolic latent space and returns a visualized plan execution. The contribution of this paper is twofold: (1) State Autoencoder, which finds a propositional state representation of the environment using a Variational Autoencoder. It generates a discrete latent vector from the images, based on which a PDDL model can be constructed and then solved by an off-the-shelf planner. (2) Action Autoencoder / Discriminator, a neural architecture which jointly finds the action symbols and the implicit action models (preconditions/effects), and provides a successor function for the implicit graph search. We evaluate LatPlan using image-based versions of 3 planning domains: 8-puzzle, Towers of Hanoi and LightsOut.

Preallocation and Planning Under Stochastic Resource Constraints

Resource constraints frequently complicate multi-agent planning problems. Existing algorithms for resource-constrained, multi-agent planning problems rely on the assumption that the constraints are deterministic. However, frequently resource constraints are themselves subject to uncertainty from external influences. Uncertainty about constraints is especially challenging when agents must execute in an environment where communication is unreliable, making on-line coordination difficult. In those cases, it is a significant challenge to find coordinated allocations at plan time depending on availability at run time. To address these limitations, we propose to extend algorithms for constrained multi-agent planning problems to handle stochastic resource constraints. We show how to factorize resource limit uncertainty and use this to develop novel algorithms to plan policies for stochastic constraints. We evaluate the algorithms on a search-and-rescue problem and on a power-constrained planning domain where the resource constraints are decided by nature. We show that plans taking into account all potential realizations of the constraint obtain significantly better utility than planning for the expectation, while causing fewer constraint violations.

Plan Recognition in Continuous Domains

Plan recognition is the task of inferring the plan of an agent, based on an incomplete sequence of its observed actions. Previous formulations of plan recognition commit early to discretizations of the environment and the observed agent's actions. This leads to reduced recognition accuracy. To address this, we first provide a formalization of recognition problems which admits continuous environments, as well as discrete domains. We then show that through mirroring---generalizing plan-recognition by planning---we can apply continuous-world motion planners in plan recognition. We provide formal arguments for the usefulness of mirroring, and empirically evaluate mirroring in more than a thousand recognition problems in three continuous domains and six classical planning domains.