Input For Planners Research Articles

Land Use Predictions To The Response Of Kediri Airport

Kediri Regency is a district with sufficient development with the existence of PSN for the construction of Kediri Airport. Kediri Airport was finally designated as PSN. This Rp 10 trillion airport is included in PSN in accordance with Presidential Regulation Number 56 of 2018. After Kediri Airport, Immediately Build the Kertosono-Tulungagung Toll Road. Land Acquisition for the Kediri Section is Completed in 2021. With this national strategic project, investment development in Kediri Regency will definitely increase. Based on the above, it is very necessary to predict future land use to be able to know the response of land use to the integrated airport and toll road project. The methods used in this research are remote sensing, GIS analysis, cellular automata analysis and descriptive analysis. Knowing the spatial response will be a very meaningful input for planners, especially in formulating plans to maximize the multiplier effect caused by the airport and toll roads on the surrounding space.

Specification Patterns for Robotic Missions

Mobile and general-purpose robots increasingly support everyday life, requiring dependable robotics control software. Creating such software mainly amounts to implementing complex behaviors known as missions. Recognizing this need, a large number of domain-specific specification languages has been proposed. These, in addition to traditional logical languages, allow the use of formally specified missions for synthesis, verification, simulation or guiding implementation. For instance, the logical language LTL is commonly used by experts to specify missions as an input for planners, which synthesize a robot's required behavior. Unfortunately, domain-specific languages are usually tied to specific robot models, while logical languages such as LTL are difficult to use by non-experts. We present a catalog of 22 mission specification patterns for mobile robots, together with tooling for instantiating, composing, and compiling the patterns to create mission specifications. The patterns provide solutions for recurrent specification problems; each pattern details the usage intent, known uses, relationships to other patterns, and—most importantly—a template mission specification in temporal logic. Our tooling produces specifications expressed in the temporal logics LTL and CTL to be used by planners, simulators or model checkers. The patterns originate from 245 mission requirements extracted from the robotics literature, and they are evaluated upon a total of 441 real-world mission requirements and 1251 mission specifications. Five of these reflect scenarios defined with two well-known industrial partners developing human-size robots. We further validate our patterns’ correctness with simulators and two different types of real robots.

The General Mediation Engine

The General Mediation Engine is an architecture for producing game experiences with world mechanics drawn from a planning domain and problem. The system models possible player actions, state updates, and system responses in a graph representation derived from planner input. Paired with an interface that displays state and action information to the player, GME structures gameplay as an on-line expansion of its graph representation.

Combining field phosphorus runoff risk assessments with whole-farm phosphorus balances to guide manure management decisions.

Phosphorus (P) loss from agricultural fields contributes to water quality degradation. A phosphorus index (PI) is a tool that scores fields based on P loss potential. Recently, a new transport × best or beneficial management practice (BMP) approach was proposed for the New York PI (NY-PI), which first scores fields using landscape-based transport factors (raw scores) and then offers various BMPs to reduce the score (i.e., risk of P transport). The final score is assigned a management implication (N needs based, P removal based, or zero P application), taking into account field-specific soil-test P (STP) and the farm's whole-farm P balance. With farmer and nutrient management planner input and data on field-specific transport factors and whole-farm P balances of 18 New York dairy farms, we set coefficients for transport factors, BMPs related to P application, and STP limits and determined the impact of implementation of the new NY-PI on manure management options. Based on raw scores, the proposed NY-PI initially limited manure application to 51% of the total cropland area of the participating farms (28% N-based, 23% P-based). Implementation of BMPs (i.e., changing the method and ground cover or timing of P application) allowed 43-98% of the land area to receive manure at N-based rates. For farms with whole-farm P balances within the feasible limits set for New York, an additional 0-50% of the land base was classified as N-based management, depending on BMP selection. These results show the ability of the new NY-PI to limit P applications on fields with high transport risk while incentivizing adoption of BMPs and improvements in whole-farm P management.

Inner entanglements: Narrowing the search in classical planning by problem reformulation

AbstractIn the field of automated planning, the central research focus is on domain‐independent planning engines that accept planning tasks (domain models and problem descriptions) in a description language, such as Planning Domain Definition Language, and return solution plans. The performance of planning engines can be improved by gathering additional knowledge about specific planning domain models/tasks (such as control rules) that can narrow the search for a solution plan. Such knowledge is often learned from training plans and solutions of simple tasks. Using techniques to reformulate the given planning task to incorporate additional knowledge, while keeping to the same input language, allows to exploit off‐the‐shelf planning engines. In this paper, we present inner entanglements that are relations between pairs of operators and predicates that represent the exclusivity of predicate achievement or requirement between the given operators. Inner entanglements can be encoded into a planner's input language by transforming the original planning task; hence, planning engines can exploit them. The contribution of this paper is to provide an in‐depth analysis and evaluation of inner entanglements, covering theoretical aspects such as complexity results, and an extensive empirical study using International Planning Competition benchmarks and state‐of‐the‐art planning engines.

Adaptation Strategy of the Bajo Fishermen towards Climate Change

Climate change currently becomes the concern of policymakers and academics because the impact is already extensive. The Bajo People Community, located in Wakatobi, Southeast Sulawesi is a coastal community with a significant portion of its population living as fishermen. The people and fishermen in the Bajo people also felt the impact of climate change on the coastal and marine environment. The effect on the environment is very influential on the economic and social community of the Bajo People. This study aims to define the alternative to propose adaptation strategy to deal with climate change for fishermen. The benefit of this study is to provide input for planners, policymakers, and practitioners related to fisheries in the territory of Indonesia. Climate change adaptation is necessary to maintain the quality of life of fishers and to achieve the stability of available resources. This study used a literature study and Analytical Hierarchy Process (AHP) as its method to find the alternative strategy in order to face climate change. The plan proposed in this research are three, namely increasing knowledge and information about climate change, resource management, and livelihood diversification. The result of this research is that Bajo Fishermen need an appropriate adaptation strategy to mitigate the situation towards climate change issues, which is necessary to prevent the disruption of economic and social activities, focusing on the cognitive, practical and structured aspects that base on cultural values, norms, and customs of the Bajo people. This study found four useful variables to be used as coping strategies for climate change, which are social, ecological, economic and political alternatives. The four variables are strengthening the surveillance of epidemic disease, developing fish gear technology, improving proper water treatment management, and creating awareness of climate change for the fishermen community.

WE‐DE‐201‐04: Cross Validation of Knowledge‐Based Treatment Planning for Prostate LDR Brachytherapy Using Principle Component Analysis

Purpose:To validate a knowledge‐based algorithm for prostate LDR brachytherapy treatment planning.Methods:A dataset of 100 cases was compiled from an active prostate seed implant service. Cases were randomized into 10 subsets. For each subset, the 90 remaining library cases were registered to a common reference frame and then characterized on a point by point basis using principle component analysis (PCA). Each test case was converted to PCA vectors using the same process and compared with each library case using a Mahalanobis distance to evaluate similarity. Rank order PCA scores were used to select the best‐matched library case. The seed arrangement was extracted from the best‐matched case and used as a starting point for planning the test case. Any subsequent modifications were recorded that required input from a treatment planner to achieve V100>95%, V150<60%, V200<20%. To simulate operating‐room planning constraints, seed activity was held constant, and the seed count could not increase.Results:The computational time required to register test‐case contours and evaluate PCA similarity across the library was 10s. Preliminary analysis of 2 subsets shows that 9 of 20 test cases did not require any seed modifications to obtain an acceptable plan. Five test cases required fewer than 10 seed modifications or a grid shift. Another 5 test cases required approximately 20 seed modifications. An acceptable plan was not achieved for 1 outlier, which was substantially larger than its best match. Modifications took between 5s and 6min.Conclusion:A knowledge‐based treatment planning algorithm for prostate LDR brachytherapy is being cross validated using 100 prior cases. Preliminary results suggest that for this size library, acceptable plans can be achieved without planner input in about half of the cases while varying amounts of planner input are needed in remaining cases. Computational time and planning time are compatible with clinical practice.

Sustainable City-Plan Based on Planning Algorithm, Planners’ Heuristics and Transportation Aspects

A multi-agent model is applied to develop and asses the impact of urban forms in terms of sustainability measures. The aim of this model is to understand and draw conclusions concerning the planner's options to arrive at a plan, which reflects a sustainable built environment in terms of transportation, distribution of facilities, and minimization of pollution emission from vehicles. The relationships between transportation, land-use, and the environment are key factors for developing a sustainable built environment. The fundamental question is: What is the optimal city-form where equilibrium is reached? Of course, no single answer can be given due to the many aspects which come into play, such as city size, distance to other cities or to service centres. However for each city size and population preferences, a specific city form should lead to a sustainable environment. In this study, we suggest that the multi-agent model can be a useful support tool for planners to assess sustainability issues in their plan development. We examine city plans that differ in their main road-network. Two city forms/structures are considered, and these are the “compact city” and the “multi-nuclear city”. The land-use distributions that are obtained reflect these road/city-form alternatives, combined with input data about people preferences. This leads to the possibility to simulate distribution of facilities that takes into account predicted activity-travel patterns of individuals, using an activity-based model of transport demand, estimated on the basis of a comprehensive survey. The analysis of travel in the city, in turn, leads to the evaluation of parameters such as total travel distance per day, number of daily trips as well as accessibility characteristics of the planned city. This enables an analysis of pollution emission from vehicles and comfort of accessibility. Planner heuristics is incorporated in between calculation stages so that the procedure as a whole would lead to a realistic and desirable result. The presented model which is a combination of a theoretical simulation and realistic planner input can be used in urban planning actual practice. Relevant planning examples where this procedure can be used are new master plans for actual cities, in which major population increase is expected, or in a new development (neighborhood, city etc).

A heuristic search approach to planning with temporally extended preferences

Planning with preferences involves not only finding a plan that achieves the goal, it requires finding a preferred plan that achieves the goal, where preferences over plans are specified as part of the planner's input. In this paper we provide a technique for accomplishing this objective. Our technique can deal with a rich class of preferences, including so-called temporally extended preferences (TEPs). Unlike simple preferences which express desired properties of the final state achieved by a plan, TEPs can express desired properties of the entire sequence of states traversed by a plan, allowing the user to express a much richer set of preferences. Our technique involves converting a planning problem with TEPs into an equivalent planning problem containing only simple preferences. This conversion is accomplished by augmenting the inputed planning domain with a new set of predicates and actions for updating these predicates. We then provide a collection of new heuristics and a specialized search algorithm that can guide the planner towards preferred plans. Under some fairly general conditions our method is able to find a most preferred plan—i.e., an optimal plan. It can accomplish this without having to resort to admissible heuristics, which often perform poorly in practice. Nor does our technique require an assumption of restricted plan length or make-span. We have implemented our approach in the HPlan-P planning system and used it to compete in the 5th International Planning Competition, where it achieved distinguished performance in the Qualitative Preferences track.

Are Automated Planners up to Solve Real Problems?

It is a well known fact that the AI planning community is very committed to apply the developments already achieved in this area to real complex applications. However realistic planning problems bring great challenges not only for the designers during design processes but also for the automated planners during the planning process itself. In addition, it is quite common to face issues about whether the available planners will be up to solve the problem being modeled during the initial design stages. In this paper we present the experience, results and issues that emerged from testing the performance of the recent planners when solving a real and complex problem such as the planning of daily activities of a petroleum plant for docking, storing and distributing oil. Due to the complexity of this real planning problem, the KE tool itSIMPLE was used in order to support all the design processes such as specification, modeling and domain model analysis that resulted in a PDDL model, automatically generated by the tool, which was used as input for planners. In addition, we present the main modeling process performed for the domain model construction.

Robust Transportation Network Design Under Demand Uncertainty

Abstract: This article addresses the problem of a traffic network design problem (NDP) under demand uncertainty. The origin–destination trip matrices are taken as random variables with known probability distributions. Instead of finding optimal network design solutions for a given future scenario, we are concerned with solutions that are in some sense “good” for a variety of demand realizations. We introduce a definition of robustness accounting for the planner's required degree of robustness. We propose a formulation of the robust network design problem (RNDP) and develop a methodology based on genetic algorithm (GA) to solve the RNDP. The proposed model generates globally near‐optimal network design solutions, f, based on the planner's input for robustness. The study makes two important contributions to the network design literature. First, robust network design solutions are significantly different from the deterministic NDPs and not accounting for them could potentially underestimate the network‐wide impacts. Second, systematic evaluation of the performance of the model and solution algorithm is conducted on different test networks and budget levels to explore the efficacy of this approach. The results highlight the importance of accounting for robustness in transportation planning and the proposed approach is capable of producing high‐quality solutions.