Valid Plan Research Articles

Deep-water red coral from the island of Sardinia (north-western Mediterranean): a local example of sustainable management

Since 1979, red coral harvesting in Sardinia has been regulated by regional laws. The regulations, including a minimum take size of 10-mm basal diameter, were imposed to limit and control harvesting. Our study indicated that >75% of the red coral colonies harvested in 2007–08 in the north-western coast were above the legal minimum size for exploitation. Moreover, extensive surveys with remotely operated vehicles allowed us to document the real structure of wild populations. Large colonies (>10 mm) represented a big portion of the population (~38% of the total), suggesting that harvesting effort did not affect yet the maximum size of colonies. All these results seem to be confirmed by the application of the model of Beverton and Holt. This model shows that the fishery of red coral in Sardinia reaches its maximum yield at an age at first capture of 20 years, an age significantly lower than the average age of 30 years calculated for the colonies actually harvested in the Sardinian coast. Our data confirmed that the measures enacted in the Sardinian regulations provide for an adequate management of this resource and, in addition, can represent a guideline for other countries that still lack a valid management plan.

On the valid deterministic localization plan problem

Grid graphs are popular testbeds for planning with incomplete information. In particular, it is studied a fundamental planning problem, localization, to investigate whether gridworlds make good testbeds for planning with incomplete information. It is found empirically that greedy planning methods that interleave planning and plan execution can localize robots very quickly on random gridworlds or mazes. Thus, they may not provide adequately challenging testbeds. On the other hand, it is showed that finding localization plans that are within a log factor of optimal is NP-hard. Thus there are instances of gridworlds on which all greedy planning methods perform very poorly. These theoretical results help empirical researchers to select appropriate planning methods for planning with incomplete information as well as testbeds to demonstrate them. However, for practical application of difficult instances we need a method for their fast decision. In this paper, we consider an approach to solve localization problem. In particular, we consider an explicit polynomial reduction from the decision version of the valid deterministic localization plan problem to the 3-satisfiability problem.

Spatially constrained coordinated navigation for a multi-robot system

In this paper we present a method for navigating a multi-robot system through an environment while additionally maintaining a predefined set of constraints. Examples for constraints are the requirement to maintain a direct line-of-sight between robots or to ensure that the multi-robot system maintains communication. Our approach is based on graph structures that model movements and constraints separately, in order to cover different kind of robots and a large class of possible constraints. Additionally, the separation of movement and constraint graph allows us to use known graph algorithms like Steiner tree heuristics or the multi-point relay algorithm to solve the problem of finding a target configuration for the robots. To connect the movements of the robots with the given constraints, we introduce separated connection graphs which allow assembling valid navigation plans fast. This paper presents some theoretical insight into the problem of coordinated navigation for multi-robot systems with spatial constraints as well as a practical solution. Experiments in simulation and with real robots show the feasibility of the approach.

On Computing Conformant Plans Using Classical Planners: A Generate-And-Complete Approach

The paper illustrates a novel approach to conformant planning using classical planners. The approach relies on two core ideas developed to deal with incomplete information in the initial situation: the use of a classical planner to solve non-classical planning problems, and the reduction of the size of the initial belief state. Differently from previous uses of classical planners to solve non-classical planning problems, the approach proposed in this paper creates a valid plan from a possible plan---by inserting actions into the possible plan and maintaining only one level of non-deterministic choice (i.e., the initial plan being modified). The algorithm can be instantiated with different classical planners---the paper presents the GC[LAMA] implementation, whose classical planner is LAMA. We investigate properties of the approach, including conditions for completeness. GC[LAMA] is empirically evaluated against state-of-the-art conformant planners, using benchmarks from the literature. The experimental results show that GC[LAMA] is superior to other planners, in both performance and scalability. GC[LAMA] is the only planner that can solve the largest instances from several domains. The paper investigates the reasons behind the good performance and the challenges encountered in GC[LAMA].

Enhanced Symmetry Breaking in Cost-Optimal Planning as Forward Search

The paper illustrates a novel approach to conformant planning using classical planners. The approach relies on two core ideas developed to deal with incomplete information in the initial situation: the use of a classical planner to solve non-classical planning problems, and the reduction of the size of the initial belief state. Differently from previous uses of classical planners to solve non-classical planning problems, the approach proposed in this paper creates a valid plan from a possible plan---by inserting actions into the possible plan and maintaining only one level of non-deterministic choice (i.e., the initial plan being modified). The algorithm can be instantiated with different classical planners---the paper presents the GC[LAMA] implementation, whose classical planner is LAMA. We investigate properties of the approach, including conditions for completeness. GC[LAMA] is empirically evaluated against state-of-the-art conformant planners, using benchmarks from the literature. The experimental results show that GC[LAMA] is superior to other planners, in both performance and scalability. GC[LAMA] is the only planner that can solve the largest instances from several domains. The paper investigates the reasons behind the good performance and the challenges encountered in GC[LAMA].

Boost the Integration of Planning and Scheduling: a Heuristics Approach

Temporal planning embodies aspects of both planning and scheduling. Many temporal planners handle these two subproblems in a loose coupling way. This way simplifies the temporal planning problem but restricts the modeling power. In particular, the simplification fails to handle such temporal planning problems that require concurrency, where actions must execute concurrently to achieve expected effects. For those temporally expressive planning problems, the problem of how to integrate planning with scheduling is emphasized for the sake of both finding a valid plan and further, in an effective way. This paper examines three factors that affect the integrated system's efficiency: information sharing, computation burden balance and interaction frequency. The approach attributes to designing a set of heuristics. By conducting preliminary experiments, the results show good performance of those heuristics compared with the start-of-the-art planner VHPOP.

Robots That Create Alternative Plans against Failures

Automated action planning is crucial for efficient execution of mobile robot missions. Automated planners use complete domain descriptions to construct plans. Nevertheless, there is usually a gap between the real world and its representation. Therefore, there is another source of uncertainty for mobile robot systems due to the impossibility of perfectly representing action descriptions (e.g., preconditions and effects) in all circumstances. Incomplete domain representations may lead a planner to fail constructing a valid plan when unforeseen events are encountered. We investigate these types of situations, especially the failure cases and how robots can recover from real-time execution failures. The main focus of our research is to design a dynamic planning framework which can generate alternative plans by applying generic updates in the domain representation when the execution of a plan fails. Our proposed method constructs new feasible plans by using the updated domain representations even if the outcomes of the operators are partially known in advance or feasible plans are not possible with the original representation of the domain. Besides updating the domain representation, our method manipulates the planner by using a reasoning mechanism so that it chooses more relevant actions to recover from failures. This is achieved by considering the effects of the failed action and trying to accomplish these effects with alternative actions.

Flask Management in the Foundry Moulding Plan

There are many studies about foundry process design, foundry quality control, and foundry methods selection; however few researches have been published regarding foundry production scheduling management and expert system and flask management in casting production in particular. It is difficult for a production planner to judge whether there are enough flasks to meet the moulding plan in casting companies, and it is a highly troublesome problem to provide the planned castings with the compatible flasks as well. In order to address these two issues, in this paper we analyzes the cause of the problem and finds that the difficulty is to manage assembling flasks, and then constructs a decision analysis method. The method can make a validity analysis to the made moulding plan, and make up a flask assignment solution for the valid plan. Finally, a foundry moulding plan expert system based on the method is developed to prove this study.

Planning in domains with derived predicates through rule-action graphs and local search

The ability to express derived predicates in the formalization of a planning domain is both practically and theoretically important. In this paper, we propose an approach to planning with derived predicates where the search space consists of "Rule-Action Graphs", particular graphs of actions and rules representing derived predicates. We propose some techniques for representing such rules and reasoning with them, which are integrated into a framework for planning through local search and rule-action graphs. We also present some heuristics for guiding the search of a rule-action graph representing a valid plan. Finally, we analyze our approach through an extensive experimental study aimed at evaluating the importance of some specific techniques for the performance of the approach. The results of our experiments also show that our planner performs quite well compared to other state-of-the-art planners handling derived predicates.

Trattamento endodontico di un incisivo centrale calcificato

Objectives To describe the endodontic treatment of a central maxillary incisor with two root canals, characterized by wide calcifications, showing that it can be faced and that finding a complex anatomy without any reference can be very difficult. Materials and methods Sometimes, a radiograph is not enough to lead to the solution of complex cases, because in case of extensive calcifications it can be insufficient to correctly construe the root canal anatomy, even after an accurate analysis. It is then mandatory to clinically verify the conditions of the tooth, if possible using a microscope that with its strong magnification allows to appreciate all the features that lead us to identify an anatomy that cannot be otherwise recognized. Together with the microscope, ultrasonic tips allow to work keeping a good visibility of the operative field. Once the endodontic pathways were found, the tooth was treated according to the crown-down technique and filled with warm gutta percha (continuous wave of condensation). Result and conclusions In complex cases characterized by extended calcifications, it is useful to pay attention to the diagnostic phase in order to formulate a valid treatment plan for the tooth, and it is mandatory to consider the individual case report using the most suitable instruments, such as operative microscope and ultrasonic tips. Indeed, these instruments can be indispensable in order to finish the endodontic treatment of calcified teeth.

Nutrient data for whole, large eggs from a USDA nationwide sampling

The Nutrient Data Laboratory (NDL) collaborated with Egg Nutrition Center (ENC) in 2010 to obtain and analyze a nationwide sampling of whole, large eggs to update the egg nutrient profile in the USDA National Nutrient Database for Standard Reference (SR) (http://www.ars.usda.gov/nutrientdata). Sample units of retail whole, large eggs were obtained from 12 locations using USDA's statistically valid sampling plans previously adopted. Collaborators at Virginia Tech prepared composited samples for analysis. Sample units from each location were prepared for analysis of proximates, fatty acids, and cholesterol; composite samples from randomly selected city pairs were prepared for analysis of vitamins, minerals, and sugars. QC materials (standard reference materials and matrix-specific control materials prepared at Virginia Tech) were also analyzed to monitor accuracy and precision of measurements. Based on these analyses, the cholesterol content of large fresh eggs decreased from 423 mg/100 g in SR 22 to 372 mg/100g in SR 23, 12% lower than the previous value from a 2001/2002 sampling. Over the same time period, the level of vitamin D (82 IU [2.0 mcg]/100g) increased by 64%, and the level of vitamin B12 went down by 31%. Values in SR are used to support the combined USDA/NHANES food consumption surveys, food and nutrition policy, and consumer education. Supported by USDA/ARS, ENC & NIH.

Intensity-Modulated Arc Therapy with Simultaneous Integrated Boost in the Treatment of Primary Irresectable Cervical Cancer

To report on the planning procedure, quality control, and clinical implementation of intensity-modulated arc therapy (IMAT) delivering a simultaneous integrated boost (SIB) in patients with primary irresectable cervix carcinoma. Six patients underwent PET-CT (positron emission tomography-computed tomography) and MRI (magnetic resonance imaging) before treatment planning. Prescription (25 fractions) was (1) a median dose (D(50)) of 62, 58 and 56 Gy to the primary tumor (GTV_cervix), primary clinical target volume (CTV_cervix) and its planning target volume (PTV_cervix), respectively; (2) a D(50) of 60 Gy to the PET-positive lymph nodes (GTV_nodes); (3) a minimal dose (D(98)) of 45 Gy to the planning target volume of the elective lymph nodes (PTV_nodes). IMAT plans were generated using an anatomy-based exclusion tool with the aid of weight and leaf position optimization. The dosimetric delivery of IMAT was validated preclinically using radiochromic film dosimetry. Five to nine arcs were needed to create valid IMAT plans. Dose constraints on D(50) were not met in two patients (both GTV_cervix: 1 Gy and 3 Gy less). D(98) for PTV_nodes was not met in three patients (1 Gy each). Film dosimetry showed excellent gamma evaluation. There were no treatment interruptions. IMAT allows delivering an SIB to the macroscopic tumor without compromising the dose to the elective lymph nodes or the organs at risk. The clinical implementation is feasible.

Neurology Diagnostics Security and Terminal Adaptation for PocketNeuro Project

This paper presents new approaches of medical information security and terminal mobile phone adaptation for the PocketNeuro project. The latter term refers to a project created for the management of neurological diseases. It consists of transmitting information about patients ("desk of patients") to a doctor's mobile phone during a visit and examination of a patient. These new approaches for the PocketNeuro project were analyzed in terms of medical information security and adaptation of the diagnostic images to the doctor's mobile phone. Images were extracted from a DICOM library. Matlab and its library were used as software to test our approaches and to validate our results. Experiments performed on a database of 30 256 x 256 pixel-sized neuronal medical images indicated that our new approaches for PocketNeuro project are valid and support plans for large-scale studies between French and Swiss hospitals using secured connections.

Effects of hydrological connectivity on the substrate and understory structure of riparian wetlands in the Middle Ebro River (NE Spain): Implications for restoration and management

The hydroperiod, flooded sediments and riparian understory were examined for eight riparian wetlands of one Ebro River reach (NE Spain) to relate river-floodplain interactions at different spatio-temporal scales with wetland strucuture. This analysis served as a basis for assessing the ecological status of the study reach and proposing a valid restoration plan. A comparison of water-level fluctuations in riparian wetlands with that in the river channel during an ordinary flood was use to characterize the hydroperiod. This characterization was further linked with the results of a multivariate analysis performed using sediment physico-chemistry. Moreover, different measures of understory diversity were used to estimate the successional stage of eight riparian wetlands located in the same reach of the Ebro River. We described four hydroperiod types from the examined flood, from disconnected oxbow lakes to backflow channels fluctuating in concert with the Ebro River. Also three types of sediment differ in their organic matter content. Both results were closely related reflecting the dominance of endogenous or allogeneous processes. However, such heterogeneity was interpreted as being variable over longer spatiotemporal scales. In addition, the riverscape was found to be homogenous and dominated by wetlands at mature successional stages. Consequently, the lack of erosive floods within the reach seems to make inclusion of ecological restoration of geomorphological dynamics a highly appropriate management objective. Alternative strategies at both reach and site scales are proposed.

Heuristics for planning with penalties and rewards formulated in logic and computed through circuits

The automatic derivation of heuristic functions for guiding the search for plans is a fundamental technique in planning. The type of heuristics that have been considered so far, however, deal only with simple planning models where costs are associated with actions but not with states. In this work we address this limitation by formulating a more expressive planning model and a corresponding heuristic where preferences in the form of penalties and rewards are associated with fluents as well. The heuristic, that is a generalization of the well-known delete-relaxation heuristic, is admissible, informative, but intractable. Exploiting a correspondence between heuristics and preferred models, and a property of formulas compiled in d-DNNF, we show however that if a suitable relaxation of the domain, expressed as the strong completion of a logic program with no time indices or horizon is compiled into d-DNNF, the heuristic can be computed for any search state in time that is linear in the size of the compiled representation. This representation defines an evaluation network or circuit that maps states into heuristic values in linear-time. While this circuit may have exponential size in the worst case, as for OBDDs, this is not necessarily so. We report empirical results, discuss the application of the framework in settings where there are no goals but just preferences, and illustrate the versatility of the account by developing a new heuristic that overcomes limitations of delete-based relaxations through the use of valid but implicit plan constraints. In particular, for the Traveling Salesman Problem, the new heuristic captures the exact cost while the delete-relaxation heuristic, which is also exponential in the worst case, captures only the Minimum Spanning Tree lower bound.

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.

The National Food and Nutrient Analysis Program: A decade of progress

The National Food and Nutrient Analysis Program (NFNAP) was designed to expand the quantity and improve the quality of data in the United States Department of Agriculture (USDA) food composition databases through the collection and analysis of nationally representative samples of foods and beverages. This paper describes some of the findings from the NFNAP and its impact on the food composition databases produced by USDA. The NFNAP employs statistically valid sampling plans, comprehensive quality control, and USDA analytical oversight to generate new and updated analytical data for food components. USDA food consumption and composition data were used to target those foods that are major contributors of nutrients of public health significance to the U.S. diet (454 Key Foods). Foods were ranked using a scoring system, divided into quartiles, and reviewed to determine the impact of changes in their composition compared to historical values. Foods were purchased from several types of locations, such as retail outlets and fast food restaurants in different geographic areas as determined by the sampling plan, then composited and sent for analysis to commercial laboratories and cooperators, along with quality control materials. Comparisons were made to assess differences between new NFNAP means generated from original analytical data and historical means. Recently generated results for nationally representative food samples show marked changes compared to database values for selected nutrients from unknown or non-representative sampling. A number of changes were observed in many high consumption foods, e.g. the vitamin A value for cooked carrots decreased from 1225 to 860 RAE/100 g; the fat value for fast food French fried potatoes increased by 21% (14.08–17.06 g/100 g). Trans fatty acids in margarine have decreased as companies reformulate their products in response to the required addition of trans fatty acids content on the nutrition label. Values decreased from 19.7 g/100 in 2002 to 14.8 g/100 in 2006 for 80%-fat stick margarines and to 4.52 g/100 g for 80%-fat tub margarines. These changes reflect improved strategies for sampling and analysis of representative food samples, which enhance the reliability of nutrient estimates for Key Foods and subsequent assessments of nutrient intake.

Recognizing the agent’s goals incrementally: planning graph as a basis

Plan recognition, the inverse problem of plan synthesis, is important wherever a system is expected to produce a kind of cooperative or competitive behavior. Most plan recognizers, however, suffer the problem of acquisition and hand-coding a larger plan library. This paper is aims to show that modern planning techniques can help build plan recognition systems without suffering such problems. Specifically, we show that the planning graph, which is an important component of the classical planning system Graph-plan, can be used as an implicit, dynamic planning library to represent actions, plans and goals. We also show that modern plan generating technology can be used to find valid plans in this framework. In this sense, this method can be regarded as a bridge that connects these two research fields. Empirical and theoretical results also show that the method is efficient and scalable.

Respiratory Guidelines Implementation in Canada

Practice guidelines on respiratory diseases have been published in Canada over the past two decades. Canada has led the way with regard to the production of evidence-based recommendations on optimal care, and guidelines are now available in a large variety of domains, both nationally and internationally. There was, however, until recently, no structured plan to ensure timely revision of Canadian respiratory guidelines, their rapid and effective dissemination, and particularly, no joint implementation strategy to help translate the recommendations into current care in the most effective way. Recognizing these limitations, the Canadian Thoracic Society (CTS) has therefore established a new strategy to address those needs, with a particular focus on collaborative work to more efficiently review the guidelines so that more resources are devoted to their implementation, particularly in primary care.

Automatic assembly sequence planning from STEP CAD files

In recent years, many research studies related to theoretical and applied assembly sequence planning have been conducted. However, with most prior proposed assembly planning methods, users need to manually enter all or part of the geometrical information needed to find valid assembly sequence plans for a given product design. In this paper, we propose a fully automated assembly sequence planner, which directly extracts geometrical information from an input CAD file and then finds assembly sequences, which require a minimum or near-minimum number of assembly direction reorientations, for a given product design. To make the method compatible with different CAD software packages, the proposed assembly planner takes, as input, a CAD STEP (STandard for the Exchange of Product model data) file. The planner uses a powerful and efficient genetic algorithm to find assembly sequences with a minimum or near-minimum number of reorientations.