bluuebunny/crossref_metadata_2025 huggingface dataset:
A governança global da Covid-19 e as ações emergenciais de países de renda baixa e média
Authors: Schabbach Leticia Maria
Published: September 2022

Resumo A Síndrome Respiratória Aguda Grave denominada Covid-19 representa um fato social dramático que tem impactado profundamente as instituições e populações de todo o mundo. Trata-se de um evento focalizador (Birkland, 1998; Kingdon, 2014) de escala global, ao qual os governos nacionais têm respondido com ações emergenciais de eficácia variável e contingenciadas pela escassez de recursos. No início de 2020 o Banco Mundial (WB) lançou o Programa de Resposta à Crise (PRC), vigente entre abril de 2020 e junho de 2021. A fim de receber os recursos financeiros, os países necessitavam encaminhar projetos voltados a ações emergenciais de enfrentamento à Covid-19. Nesse contexto, o artigo examina a influência das capacidades estatais preexistentes como fator explicativo das razões de chance de um país ter projeto de cooperação aprovado, utilizando-se como proxy a média anual de projetos aprovados, desde o ingresso no WB até 2019, e como variáveis de controle: localização na região africana, existência de casos de Covid-19, índice global de segurança em saúde e renda nacional per capita. Os resultados da regressão logística evidenciaram que as chances de aprovação de projeto Covid-19 pelo Banco Mundial aumentam quanto maiores forem a média anual de projetos anteriormente aprovados pela instituição financeira e a renda per capita do país. Enquanto o primeiro achado confirma a hipótese central, o segundo explicita uma relação causal positiva da variável preditora com a dependente, em sentido distinto do esperado.
As sociologias da pandemia: contribuições sobre a Covid-19 e sociedade
Authors: Santos Priscilla Ribeiro dos , Teixeira Alex Niche
Published: September 2022

Resumo A pandemia da Covid-19 é um evento global sem precedentes, cujos efeitos se prolongarão no tempo nas mais diversas esferas da vida em sociedade. Este dossiê visa superar a descrição dos acontecimentos e avançar na análise empírica sobre os efeitos da pandemia pelo enfrentamento das incertezas, teóricas e metodológicas que atravessam distintas agendas de pesquisas. Apresentamos artigos que, de forma plural e complementar, discutem o papel do Estado na gestão da crise sanitária, a (re)produção de desigualdades, os usos da ciência e as mudanças socioculturais no contexto pandêmico.
Neuropsychiatric symptoms of dementia and caregivers’ burden: a study among Indian caregivers
Authors: Basu Ipsita , Mukhopadhyay Susmita
Published: September 2022

ABSTRACT Dementia is considered a most serious and disabling condition, affecting both the individual suffering from it and their caregiver. Objective: The study aimed to evaluate the relationship between neuropsychiatric problems of dementia and caregiver burden. Methods: A total of 138 caregivers of people with dementia participated in this cross-sectional study. The caregivers completed the questionnaires containing sociodemographic information as well as neuropsychiatric problems of dementia and caregiver burden. Results: The findings showed that all of the care-recipients were suffering from some kind of neuropsychiatric symptoms, the most common being apathy, anxiety, motor disturbance, and hallucination. Out of 12 symptoms, 11 were significantly associated with caregivers’ burden. The most important finding is that the severity of neuropsychiatric symptoms is highly responsible for severe caregivers’ burden. Conclusions: The identification of neuropsychiatric symptoms of dementia that influence caregiver burden is very critical for both caregivers’ and care-recipients’ health perspective. These findings can also be utilized to create care settings for demented people and help determine policies in the future.
INDEPENDÊNCIA, MODERNISMO E AS CIÊNCIAS SOCIAIS: UMA CONVERSA COM LILIA SCHWARCZ E ANDRÉ BOTELHO
Authors: Brasil Jr. Antonio , Schwarcz Lilia , Botelho André
Published: September 2022

Resumo Estimulados pela dupla efeméride do bicentenário da Independência e do centenário da Semana de Arte Moderna, convidamos Lilia Schwarcz e André Botelho para uma conversa sobre as possíveis contribuições das ciências sociais para um enquadramento mais amplo, crítico e reflexivo desses dois eventos fundantes da nacionalidade brasileira.
Fisioterapia em obstetrícia pelos olhos das gestantes: um estudo qualitativo
Authors: Keil Marina Joice , Delgado Alexandre Magno , Xavier Mikaela Aparecida de Oliveira , Nascimento Cassiane Merigo do
Published: September 2022

Resumo Introdução: A fisioterapia em saúde da mulher é uma área que surgiu com o objetivo de promover e manter a saúde física e emocional do início ao final da gestação, exercendo um trabalho preventivo para os possíveis agravos que venham a ocorrer durante a gestação, parto e puerpério. O acesso ao conhecimento das gestantes sobre a atuação do fisioterapeuta na área de obstetrícia vem aumentando a cada dia e isso pode contribuir para uma procura por profissionais capacitados. Objetivo: Analisar a percepção das gestantes sobre a atuação da fisioterapia em obstetrícia. Métodos: Trata-se de um estudo com abordagem qualitativa, realizado no centro Materno Infantil, em Capanema, PR, configurado na forma de entrevista semiestruturada. Utilizou-se análise do conteúdo por meio do método de Bardin para categorizar os eixos temáticos. Resultados: Foram incluídas sete gestantes com idade entre 18 e 24 anos (57%), 23-32 semanas de gestação (42%), multigestas (71%), com ensino médio (42%) e renda de 1 a 3 salários mínimos (86%). Verificou-se que o contato com a fisioterapia é somente imaginável, porque nenhuma das participantes do estudo acessou esse desdobramento das políticas de saúde da mulher gestante. As gestantes afirmaram que o profissional de fisioterapia atua promovendo a diminuição da dor durante o trabalho de parto, porém não possuem conhecimento sobre como o fisioterapeuta atua no puerpério. Conclusão: Conclui-se que é necessário ampliar a informação sobre a atuação do fisioterapeuta durante o pré-natal, parto e pós-parto.
Formação experiencial e aprendizagem biográfica: refletir para atribuir sentidos às experiências?
Authors: Doutor Catarina , Alves Natália
Published: September 2022

Resumo A ideia de que a experiência de vida pode constituir uma fonte de aprendizagem e de desenvolvimento não é, de todo, nova. Aliás, as experiências de vida quando são pensadas e refletidas adquirem uma dimensão formativa. No entanto, a experiência é, por si só, um conceito impreciso e que comporta um conjunto diversificado de significados. Nesse contexto, qual a relação entre experiência e aprendizagem? Acresce a essa questão, outras consideradas, de igual modo, importantes, tais como: Qual o papel formativo das experiências? E qual o papel da reflexão? Essas são algumas das questões que tentaremos responder. O presente artigo, de natureza teórica, procura refletir sobre os conceitos de aprendizagem e/ou formação experiencial e aprendizagem biográfica no quadro teórico-conceptual da educação e formação de adultos. A partir das principais perspectivas teóricas apresentadas e discutidas, os três conceitos assumem um sentido idêntico referente ao papel ativo do indivíduo, assim como à sua capacidade de refletir e, posteriormente, de atribuir sentidos e/ou significados às experiências vivenciadas. Acreditamos que a relevância desses conceitos teóricos se prende, precisamente, com a possibilidade de analisar os múltiplos significados e sentidos que os indivíduos atribuem quer às experiências, quer às aprendizagens adquiridas ao longo das suas vidas.
Q fever and coxiellosis in Brazil: an underestimated disease? A brief review
Authors: Souza Eline Almeida Rodrigues de , André Marcos Rogério , Labruna Marcelo Bahia , Horta Mauricio Claudio
Published: September 2022

Abstract Q fever, caused by the γ-proteobacterium Coxiella burnetii, is a zoonosis of great importance and global impact. This agent has high transmissibility and can spread over long distances via wind, in which a small number of aerosolized particles are needed to infect susceptible hosts. The clinical diagnosis of Q fever is difficult owing to the variety of clinical signs shared with other diseases. In Brazil, studies related to C. burnetii are constantly being conducted, and this review aims to increase the number of approaches already studied, leading to the following question: is Q fever an unknown, neglected disease, or does it have a focal occurrence in certain areas (exotic/rare) in the country?
Long-term persistence of anti-Rickettsia rickettsii antibodies in capybaras, with passive transfer to offspring
Authors: Binder Lina de Campos , Ramírez-Hernández Alejandro , Serpa Maria Carolina de Azevedo , Pinter Adriano , Souza Celso Eduardo , Labruna Marcelo Bahia
Published: September 2022

Abstract The bacterium Rickettsia rickettsii is the etiological agent of Brazilian spotted fever (BSF), and its most important vector to humans in Brazil is the tick Amblyomma sculptum. Capybaras are the main hosts of A. sculptum in many BSF-endemic areas and are considered valuable sentinels for BSF surveillance. This study aimed to assess the persistence of anti-R. rickettsii antibodies for long periods in capybaras and their passive transfer to offspring. For this purpose, three adult capybaras previously exposed to multiple infections with R. rickettsii were followed up until 3.1 years after their last exposure. During the study, one female delivered five cubs, of which three survived. Blood samples were collected monthly from adults and infants, and serum samples were titrated by indirect immunofluorescence assay (IFA) to determine endpoint titers of anti-R. rickettsii antibodies. All three adults remained seroreactive to R. rickettsii with high endpoint titers until the end of the study. All infants were seroreactive to R. rickettsii after birth and remained seroreactive for one to four months. This study showed that exposure of capybaras to R. rickettsii-infected A. sculptum ticks elicits a persistent antibody response. In addition, there was evidence of passive transfer of R. rickettsii-reactive antibodies to offspring.
AVALIAÇÃO DO OMENTO COMO DOADOR DE CÉLULAS TRONCO NO MIOCÁRDIO ISQUÊMICO ATRAVÉS DA ANÁLISE IMUNOHISTOQUÍMICA DO CD34
Authors: Kubrusly Fernando Bermudez , Kubrusly Luiz Fernando , Tabushi Fernando Issamu , Gewehr Douglas Mesadri , Giovanini Allan Fernando , Bufon Carlos Cesar Bof , Waaga-Gasser Ana Maria , Malafaia Osvaldo
Published: September 2022

Racional: As doenças cardiovasculares são a principal causa de morte no mundo. O uso de células precursoras hematopoiéticas e os recentes progressos feitos na bioengenharia de enxertos cardíacos oferecem uma nova modalidade terapêutica para a regeneração do tecido cardíaco pós-infarto do miocárdio (IM). O CD34 é um marcador expresso em todas as células precursoras hematopoiéticas e endoteliais, e funciona como fator de adesão celular. O anticorpo que correspondente a este marcador é utilizado na imunohistoquímica para avaliar a formação de novos vasos e a presença de células-tronco. Objetivo: O estudo teve por objetivo avaliar a eficácia da omentopexia na neovascularização e na doação de células tronco de corações suínos previamente infartados, a partir da análise imunohistoquímica do CD34. Método: O infarto do miocárdio foi gerado em 4 suínos, por ligadura do 1°e 2° ramos marginais da artéria circunflexa. Em 3 animais realizou-se abrasão cuidadosa do epicárdio infartado seguido de múltiplas perfurações miocárdicas e a mobilização do omento da cavidade abdominal para o mediastino, envolvendo a área infartada e as perfurações. No quarto animal não foi realizado a omentopexia sendo realizado apenas a abrasão e perfuração da área infartada. Todos os animais foram eutanasiados ao 30º dia pós operatório e os corações retirados para avaliação macroscópica, microscópica e Imunohistoquímica do CD34. Resultados: Nas amostras do grupo submetido a omentopexia, ocorreu um aumento de 60% da angiogênese, sendo que nas amostras do animal controle houve marcação mínima. Foram avaliadas quatro amostras de diferentes sítios de cada coração dos animais, totalizando 16 amostras histopatológicas. Todas as amostras foram imunomarcadas para CD-34. Conclusões: O omento mostrou-se eficiente na indução de neovascularização pela presença de células tronco, vista através da marcação do CD34, demonstrando grande potencial como futura terapêutica para restaurar áreas de miocárdio isquêmico.
UPMurphi: A Tool for Universal Planning on PDDL+ Problems
Authors: Della Penna Giuseppe , Magazzeni Daniele , Mercorio Fabio , Intrigila Benedetto
Published: September 2022

Systems subject to (continuous) physical effects and controlled by (discrete) digital equipments, are today very common. Thus, many realistic domains where planning is required are represented by hybrid systems, i.e., systems containing both discrete and continuous values, with possibly a nonlinear continuous dynamics. The PDDL+ language allows one to model these domains, however the current tools can generally handle only planning problems on (possibly hybrid) systems with linear dynamics. Therefore, universal planning applied to hybrid systems and, in general, to non-linear systems is completely out of scope for such tools. In this paper, we propose the use of explicit model checking-based techniques to solve universal planning problems on such hardly-approachable domains.
Fast Distributed Multi-agent Plan Execution with Dynamic Task Assignment and Scheduling
Authors: Shah Julie , Conrad Patrick , Williams Brian
Published: September 2022

An essential quality of a good partner is her responsiveness to other team members. Recent work in dynamic plan execution exhibits elements of this quality through the ability to adapt to the temporal uncertainties of others agents and the environment. However, a good teammate also has the ability to adapt on-the-fly through task assignment. We generalize the framework of dynamic execution to perform plan execution with dynamic task assignment as well as scheduling.This paper introduces Chaski, a multi-agent executive for scheduling temporal plans with online task assignment. Chaski enables an agent to dynamically update its plan in response to disturbances in task assignment and the schedule of other agents. The agent then uses the updated plan to choose, schedule and execute actions that are guaranteed to be temporally consistent and logically valid within the multi-agent plan. Chaski is made efficient through an incremental algorithm that compactly encodes all scheduling policies for all possible task assignments. We apply Chaski to perform multi-manipulator coordination using two Barrett Arms within the authors' hardware testbed. We empirically demonstrate up to one order of magnitude improvements in execution latency and solution compactness compared to prior art.
Automatic Derivation of Memoryless Policies and Finite-State Controllers Using Classical Planners
Authors: Bonet Blai , Palacios Héctor , Geffner Héctor
Published: September 2022

Finite-state and memoryless controllers are simple action selection mechanisms widely used in domains such as video-games and mobile robotics.  Memoryless controllers stand for functions that map observations into actions, while finite-state controllers generalize memoryless ones with a finite amount of memory.  In contrast to the policies obtained from MDPs and POMDPs, finite-state controllers have two advantages: they are often extremely compact, involving a small number of controller states or none at all, and they are general, applying to many problems and not just one. A limitation of finite-state controllers is that they must be written by hand. In this work, we address this limitation, and develop a method for deriving finite-state controllers automatically from models. These models represent a class of contingent problems where actions are deterministic and some fluents are observable.  The problem of deriving a controller from such models is converted into a conformant planning problem that is solved using classical planners, taking advantage of a complete translation introduced recently.  The controllers derived in this way are 'general' in the sense that they do not solve the original problem only, but many variations as well, including changes in the size of the problem or in the uncertainty of the initial situation and action effects.  Experiments illustrating the derivation of such controllers are presented.
Semantic Attachments for Domain-Independent Planning Systems
Authors: Dornhege Christian , Eyerich Patrick , Keller Thomas , Trüg Sebastian , Brenner Michael , Nebel Bernhard
Published: September 2022

Solving real-world problems using symbolic planning often requires a simplified formulation of the original problem, since certain subproblems cannot be represented at all or only in a way leading to inefficiency. For example, manipulation planning may appear as a subproblem in a robotic planning context or a packing problem can be part of a logistics task. In this paper we propose an extension of PDDL for specifying semantic attachments. This allows the evaluation of grounded predicates as well as the change of fluents by externally specified functions. Furthermore, we describe a general schema of integrating semantic attachments into a forward-chaining planner and report on our experience of adding this extension to the planners FF and Temporal Fast Downward. Finally, we present some preliminary experiments using semantic attachments.
Integrating Planning and Scheduling in a CP Framework: A Transition-Based Approach
Authors: Banerjee Debdeep
Published: September 2022

Many potential real-world planning applications are on the border of planning and scheduling. To handle the complex choices of actions and temporal and resource constraints of these problems we need to integrate planning and scheduling techniques. Here we propose a transition-based formulation of temporal planning problems, that enables us to represent features like deadlines, time windows, release times etc. in a simple way. We describe a CSP encoding of the transition-based formulation and its potential advantages in integrating planning and scheduling techniques.
Solving Resource-Constrained Project Scheduling Problems with Time-Windows Using Iterative Improvement Algorithms
Authors: Oddi Angelo , Rasconi Riccardo
Published: September 2022

This paper proposes an iterative improvement approach for solving the Resource Constraint Project Scheduling Problem with Time-Windows (RCPSP/max), a well-known and challenging NP-hard scheduling problem. The algorithm is based on Iterative Flattening Search (IFS), an effective heuristic strategy for solving multi-capacity optimization scheduling problems. Given an initial solution, IFS iteratively performs two-steps: a relaxation-step, that randomly removes a subset of solution constraints and a solving-step, that incrementally recomputes a new solution. At the end, the best solution found is returned. The main contribution of this paper is the extension to RCPSP/max of the IFS optimization procedures developed for solving scheduling problems without time-windows. An experimental evaluation performed on medium-large size and web-available benchmark sets confirms the effectiveness of the proposed procedures. In particular, we have improved the average quality w.r.t. the current bests, while discovering three new optimal solutions, thus demonstrating the general efficacy of IFS.
Focused Topological Value Iteration
Authors: Dai Peng , Mausam  , Weld Daniel
Published: September 2022

Topological value iteration (TVI) is an effective algorithm for solving Markov decision processes (MDPs) optimally, which 1) divides an MDP into strongly-connected components, and 2) solves these components sequentially. Yet, TVI’s usefulness tends to degrade if an MDP has large components, because the cost of the division process isn’t offset by gains during solution. This paper presents a new algorithm to solve MDPs optimally, focused topological value iteration (FTVI). FTVI addresses TVI’s limitations by restricting its attention to connected components that are relevant for solving the MDP. Specifically, FTVI uses a small amount of heuristic search to eliminate provably sub-optimal actions; this pruning allows FTVI to find smaller connected components, thus running faster. We demonstrate that our new algorithm outperforms TVI by an order of magnitude, averaged across several domains. Surprisingly, FTVI also significantly outperforms popular ‘heuristically-informed’ MDP algorithms such as LAO*, LRTDP, and BRTDP in many domains, sometimes by as much as two orders of magnitude. Finally, we characterize the type of domains where FTVI excels — suggesting a way to an informed choice of solver.
Construction Management Applications: Challenges in Developing Execution Control Plans
Authors: Onder Nilufer , Mukherjee Amlan , Tang Pei
Published: September 2022

The objective of automated planners is to synthesize sequences of actions (called policies in MDP frameworks) that will achieve a predetermined goal given a fully or partially observable formal representation of the domain. In contrast, the main characteristic of project management is the greater emphasis on plan execution under uncertainty as opposed to plan synthesis. This paper explains the need to transition from automated plan synthesis to plan management and identifies the challenges for the planning and scheduling communities using examples of construction projects.
Preface
Authors: Brafman Ronen , Geffner Hector , Hofman Joerg , Kautz Henry
Published: September 2022

This volume contains the papers accepted for presentation at ICAPS 2010, the Twentieth International Conference on Automated Planning and Scheduling, held in Toronto, Ontario, Canada, May 12–16, 2010. The annual ICAPS conference series was established in 2003 through the merger of two pre-existing biennial conferences, the International Conference on Artificial Intelligence Planning and Scheduling (AIPS) and the European Conference on Planning (ECP). ICAPS continues the traditional high standards of AIPS and ECP as an archival forum for new research in the field of automated planning and scheduling.
An Evolutionary Metaheuristic Based on State Decomposition for Domain-Independent Satisficing Planning
Authors: Bibaï Jacques , Savéant Pierre , Schoenauer Marc , Vidal Vincent
Published: September 2022

DAEX is a metaheuristic designed to improve the plan quality and the scalability of an encapsulated planning system. DAEX is based on a state decomposition strategy, driven by an evolutionary algorithm, which benefits from the use of a classical planning heuristic to maintain an ordering of atoms within the individuals. The proof of concept is achieved by embedding the domain-independent satisficing YAHSP planner and using the critical path h1 heuristic. Experiments with the resulting algorithm are performed on a selection of IPC benchmarks from classical, cost-based and temporal domains. Under the experimental conditions of the IPC, and in particular with a universal parameter setting common to all domains, DAEYAHSP is compared to the best planner for each type of domain. Results show that DAEYAHSP performs very well both on coverage and quality metrics. It is particularly noticeable that DAEX improves a lot on plan quality when compared to YAHSP, which is known to provide largely suboptimal solutions; making it competitive with state-of-the-art planners. This article gives a full account of the algorithm, reports on the experiments and provides some insights on the algorithm behavior.
Planning for Concurrent Action Executions Under Action Duration Uncertainty Using Dynamically Generated Bayesian Networks
Authors: Beaudry Eric , Kabanza Froduald , Michaud Francois
Published: September 2022

An interesting class of planning domains, including planning for daily activities of Mars rovers, involves achievement of goals with time constraints and concurrent actions with probabilistic durations. Current probabilistic approaches, which rely on a discrete time model, introduce a blow up in the search state-space when the two factors of action concurrency and action duration uncertainty are combined. Simulation-based and sampling probabilistic planning approaches would cope with this state explosion by avoiding storing all the explored states in memory, but they remain approximate solution approaches. In this paper, we present an alternative approach relying on a continuous time model which avoids the state explosion caused by time stamping in the presence of action concurrency and action duration uncertainty. Time is represented as a continuous random variable. The dependency between state time variables is conveyed by a Bayesian network, which is dynamically generated by a state-based forward-chaining search based on the action descriptions. A generated plan is characterized by a probability of satisfying a goal. The evaluation of this probability is done by making a query the Bayesian network.
Towards Finding Robust Execution Strategies for RCPSP/max with Durational Uncertainty
Authors: Fu Na , Varakantham Pradeep , Lau Hoong Chuin
Published: September 2022

Resource Constrained Project Scheduling Problems with minimum and maximum time lags (RCPSP/max) have been studied extensively in the literature. However, the more realistic RCPSP/max problems — ones where durations of activities are not known with certainty – have received scant interest and hence are the main focus of the paper. Towards addressing the significant computational complexity involved in tackling RCPSP/max with durational uncertainty, we employ a local search mechanism to generate robust schedules. In this regard, we make two key contributions: (a) Introducing and studying the key properties of a new decision rule to specify start times of activities with respect to dynamic realizations of the duration uncertainty; and (b) Deriving the fitness function that is used to guide the local search towards robust schedules. Experimental results show that the performance of local search is improved with the new fitness evaluation over the best known existing approach.
Inﬂuence-Based Policy Abstraction for Weakly-Coupled Dec-POMDPs
Authors: Witwicki Stefan , Durfee Edmund
Published: September 2022

Decentralized POMDPs are powerful theoretical models for coordinating agents’ decisions in uncertain environments, but the generally-intractable complexity of optimal joint policy construction presents a signiﬁcant obstacle in applying Dec-POMDPs to problems where many agents face many policy choices. Here, we argue that when most agent choices are independent of other agents’ choices, much of this complexity can be avoided: instead of coordinating full policies, agents need only coordinate policy abstractions that explicitly convey the essential interaction inﬂuences. To this end, we develop a novel framework for inﬂuence-based policy abstraction for weakly-coupled transition-dependent Dec-POMDP problems that subsumes several existing approaches. In addition to formally characterizing the space of transition-dependent inﬂuences, we provide a method for computing optimal and approximately-optimal joint policies. We present an initial empirical analysis, over problems with commonly-studied ﬂavors of transition-dependent inﬂuences, that demonstrates the potential computational beneﬁts of inﬂuence-based abstraction over state-of-the-art optimal policy search methods.
Simultaneously Searching with Multiple Settings: An Alternative to Parameter Tuning for Suboptimal Single-Agent Search Algorithms
Authors: Valenzano Richard , Sturtevant Nathan , Schaeffer Jonathan , Buro Karen , Kishimoto Akihiro
Published: September 2022

Many search algorithms have parameters that need to be tuned to get the best performance. Typically, the parameters are tuned offline, resulting in a generic setting that is supposed to be effective on all problem instances. For suboptimal single-agent search, problem-instance-specific parameter settings can result in substantially reduced search effort. We consider the use of dovetailing as a way to take advantage of this fact. Dovetailing is a procedure that performs search with multiple parameter settings simultaneously. Dovetailing is shown to improve the search speed of weighted IDA* by several orders of magnitude and to generally enhance the performance of weighted RBFS. This procedure is trivially parallelizable and is shown to be an effective form of parallelization for WA* and BULB. In particular, using WA* with parallel dovetailing yields good speedups in the sliding-tile puzzle domain, and increases the number of problems solved when used in an automated planning system.
Planning and Acting in Incomplete Domains
Authors: Weber Christopher , Bryce Daniel
Published: September 2022

Engineering complete planning domain descriptions is often very costly because of human error or lack of domain knowl- edge. Learning complete domain descriptions is also very challenging because many features are irrelevant to achieving the goals and data may be scarce. We present a planner and agent that respectively plan and act in incomplete domains by i) synthesizing plans to avoid execution failure due to ignorance of the domain model, and ii) passively learning about the domain model during execution to improve later re-planning attempts. Our planner DeFault is the first to reason about a domain’s incompleteness to avoid potential plan failure. DeFault computes failure explanations for each action and state in the plan and counts the number of interpretations of the incomplete domain where failure will occur. We show that DeFault performs best by counting prime implicants (failure diagnoses) rather than propositional models. Our agent Goalie learns about the preconditions and effects of incompletely-specified actions while monitoring its state and, in conjunction with DeFault plan failure explanations, can diagnose past and future action failures. We show that by reasoning about incompleteness (as opposed to ignoring it) Goalie fails and re-plans less and executes fewer actions.
Computing All-Pairs Shortest Paths by Leveraging Low Treewidth
Authors: Planken Léon , De Weerdt Mathijs , Van der Krogt Roman
Published: September 2022

Considering directed graphs on n vertices and m edges with real (possibly negative) weights, we present two new, efficient algorithms for computing all-pairs shortest paths (APSP). These algorithms make use of directed path consistency (DPC) along a vertex ordering d. The algorithms run in O(n2wd) time, where wd is the graph width induced by this vertex ordering. For graphs of constant treewidth, this yields O(n2) time, which is optimal. On chordal graphs, the algorithms run in O(nm) time. We show empirically that also in many general cases, both constructed and from realistic benchmarks, the algorithms often outperform Johnson's algorithm, which represents the current state of the art with a run time of O(nm + n2log n). These algorithms can be used for temporal and spatial reasoning, e.g. for the Simple Temporal Problem (STP), which underlines its relevance to the planning and scheduling community.
Planning Multi-Modal Transportation Problems
Authors: Flórez José , Torralba Arias de Reyna Álvaro , García Javier , Linares López Carlos , García-Olaya Ángel , Borrajo Daniel
Published: September 2022

Multi-modal transportation is a logistics problem in which a set of goods have to be transported to different places, with the combination of at least two modes of transport, without a change of container for the goods. The goal of this paper is to describe TIMIPLAN, a system that solves multi-modal transportation problems in the context of a project for a big company. In this paper, we combine Linear Programming (LP) with automated planning techniques in order to obtain good quality solutions. The direct use of classical LP techniques is difficult in this domain, because of the non-linearity of the optimization function and constraints; and planning algorithms cannot deal with the entire problem due to the large number of resources involved. We propose a new hybrid algorithm, combining LP and planning to tackle the multi-modal transportation problem, exploiting the benefits of both kinds of techniques. The system also integrates an execution component that monitors the execution, keeping track of failures and replans if necessary, maintaining most of the plan in execution. We also present some experimental results that show the performance of the system.
Planning to Perceive: Exploiting Mobility for Robust Object Detection
Authors: Velez Javier , Hemann Garrett , Huang Albert , Posner Ingmar , Roy Nicholas
Published: September 2022

Consider the task of a mobile robot autonomously navigating through an environment while detecting and mapping objects of interest using a noisy object detector. The robot must reach its destination in a timely manner, but is rewarded for correctly detecting recognizable objects to be added to the map, and penalized for false alarms. However, detector performance typically varies with vantage point, so the robot benefits from planning trajectories which maximize the efficacy of the recognition system. This work describes an online, any-time planning framework enabling the active exploration of possible detections provided by an off-the-shelf object detector. We present a probabilistic approach where vantage points are identified which provide a more informative view of a potential object. The agent then weighs the benefit of increasing its confidence against the cost of taking a detour to reach each identified vantage point. The system is demonstrated to significantly improve detection and trajectory length in both simulated and real robot experiments.
Effective Heuristics and Belief Tracking for Planning with Incomplete Information
Authors: Albore Alexandre , Ramírez Miquel , Geffner Hector
Published: September 2022

Conformant planning can be formulated as a path-finding problem in belief space where the two main challenges are the heuristics to guide the search, and the representation and update of beliefs. In the translation-based approach recently introduced by Palacios and Geffner, the two aspects are handled together by translating conformant problems into classical ones that are solved with classical planners. While competitive with state-of-the-art methods, the translation-based approach runs however into three difficulties. First, complete translations are expensive for problems with high width; second, incomplete translations can generate infinite heuristic values for problems that are solvable; and third, aspects that are specific to the conformant setting, such as the cardinality of beliefs, are not accounted for.In this work, we build on the translation-based approach but not for solving conformant problems with a classical planner but for deriving heuristics and computing beliefs in the context of a standard belief-space planner. For this, a novel translation KSi is introduced that is always complete, but which is sound for problems with width bounded by i. A new conformant planner, called T1, builds then on this translation for i=1, extending the heuristic that results with a second heuristic obtained from invariant "oneof expressions". A number of experiments is performed to compare T1 with state-of-the-art conformant planners.
Sample-Based Planning for Continuous Action Markov Decision Processes
Authors: Mansley Chris , Weinstein Ari , Littman Michael
Published: September 2022

In this paper, we present a new algorithm that integrates recent advances in solving continuous bandit problems with sample-based rollout methods for planning in Markov Decision Processes (MDPs). Our algorithm, Hierarchical Optimistic Optimization applied to Trees (HOOT) addresses planning in continuous-action MDPs. Empirical results are given that show that the performance of our algorithm meets or exceeds that of a similar discrete action planner by eliminating the problem of manual discretization of the action space.
Optimal Search with Inadmissible Heuristics
Authors: Karpas Erez , Domshlak Carmel
Published: September 2022

Considering cost-optimal heuristic search, we introduce the notion of global admissibility of a heuristic, a property weaker than standard admissibility, yet sufficient for guaranteeing solution optimality within forward search. We describe a concrete approach for creating globally admissible heuristics for domain independent planning; it is based on exploiting information gradually gathered by the search via a new form of reasoning about what we call existential optimal-plan landmarks. We evaluate our approach on some state-of-the-art heuristic search tools for cost-optimal planning, and discuss the results of this evaluation.
Bandit-Based Planning and Learning in Continuous-Action Markov Decision Processes
Authors: Weinstein Ari , Littman Michael
Published: September 2022

Recent research leverages results from the continuous-armed bandit literature to create a reinforcement-learning algorithm for continuous state and action spaces. Initially proposed in a theoretical setting, we provide the first examination of the empirical properties of the algorithm. Through experimentation, we demonstrate the effectiveness of this planning method when coupled with exploration and model learning and show that, in addition to its formal guarantees, the approach is very competitive with other continuous-action reinforcement learners.
Faster Bounded-Cost Search Using Inadmissible Estimates
Authors: Thayer Jordan , Stern Roni , Felner Ariel , Ruml Wheeler
Published: September 2022

Many important problems are too difficult to solve optimally. A traditional approach to such problems is bounded suboptimal search, which guarantees solution costs within a user-specified factor of optimal. Recently, a complementary approach has been proposed: bounded-cost search, where solution cost is required to be below a user-specified absolute bound. In this paper, we show how bounded-cost search can incorporate inadmissible estimates of solution cost and solution length. This information has previously been shown to improve bounded suboptimal search and, in an empirical evaluation over five benchmark domains, we find that our new algorithms surpass the state-of-the-art in bounded-cost search as well, particularly for domains where action costs differ.
PROST: Probabilistic Planning Based on UCT
Authors: Keller Thomas , Eyerich Patrick
Published: September 2022

We present PROST, a probabilistic planning system that is based on the UCT algorithm by Kocsis and Szepesvari (2006), which has been applied successfully to many areas of planning and acting under uncertainty. The objective of this paper is to show the application of UCT to domain- independent probabilistic planning, an area it had not been applied to before. We furthermore present several enhance- ments to the algorithm, including a method that is able to drastically reduce the branching factor by identifying super- fluous actions. We show how search depth limitation leads to a more thoroughly investigated search space in parts that are influential on the quality of a policy, and present a sound and polynomially computable detection of reward locks, states that correspond to, e.g., dead ends or goals. We describe a general Q-value initialization for unvisited nodes in the search tree that circumvents the initial random walks inher- ent to UCT, and leads to a faster convergence on average. We demonstrate the significant influence of the enhancements by providing a comparison on the IPPC benchmark domains.
About Partial Order Reduction in Planning and Computer Aided Verification
Authors: Wehrle Martin , Helmert Malte
Published: September 2022

Partial order reduction is a state space pruning approach that has been originally introduced in computer aided verification. Recently, various partial order reduction techniques have also been proposed for planning. Despite very similar underlying ideas, the relevant literature from computer aided verification has hardly been analyzed in the planning area so far, and it is unclear how these techniques are formally related. We provide an analysis of existing partial order reduction techniques and their relationships. We show that recently proposed approaches in planning are instances of general partial order reduction approaches from computer aided verification. Our analysis reveals a hierarchy of dominance relationships and shows that there is still room for improvement for partial order reduction techniques in planning. Overall, we provide a first step towards a better understanding and a unifying theory of partial order reduction techniques from different areas.
Sampling-Based Coverage Path Planning for Inspection of Complex Structures
Authors: Englot Brendan , Hover Franz
Published: September 2022

We present several new contributions in sampling-based coverage path planning, the task of finding feasible paths that give 100% sensor coverage of complex structures in obstaclefilled and visually occluded environments. First, we establish a framework for analyzing the probabilistic completeness of a sampling-based coverage algorithm, and derive results on the completeness and convergence of existing algorithms. Second, we introduce a new algorithm for the iterative improvement of a feasible coverage path; this relies on a samplingbased subroutine that makes asymptotically optimal local improvements to a feasible coverage path based on a strong generalization of the RRT* algorithm. We then apply the algorithm to the real-world task of autonomous in-water ship hull inspection. We use our improvement algorithm in conjunction with redundant roadmap coverage planning algorithm to produce paths that cover complex 3D environments with unprecedented efficiency.
Better Time Constrained Search via Randomization and Postprocessing
Authors: Xie Fan , Valenzano Richard , Müller Martin
Published: September 2022

Most of the satisficing planners which are based on heuristic search iteratively improve their solution quality through an anytime approach. Typically, the lowest-cost solution found so far is used to constrain the search. This avoids areas of the state space which cannot directly lead to lower cost solutions. However, in this paper we show that when used in conjunction with a post-processing plan improvement system such as ARAS, this bounding approach can harm a planner’s performance since the bound may prevent the search from ever finding additional plans for the post-processor to improve.The new anytime search framework of Diverse Any-Time Search addresses this issue through the use of restarts, randomization, and by not bounding as strictly as is done by previous approaches. Below, we will show that by using these techniques, the framework is able to generate a more diverse set of “raw" input plans for the post-processor to work on. We then show that when adding both Diverse Any-Time Search and the ARAS post-processor to LAMA-2011, the winner of the most recent IPC planning competition, the performance according to the IPC scoring metric improves from 511 points to over 570 points when tested on the 550 problems from IPC 2008 and IPC 2011. Performance gains are also seen when these techniques are added to Anytime Explicit Estimation Algorithm (AEES), as the performance improves from 440 points to over 513 points on the same problem set.
Smart Urban Signal Networks: Initial Application of the SURTRAC Adaptive Traffic Signal Control System
Authors: Smith Stephen , Barlow Gregory , Xie Xiao-Feng , Rubinstein Zachary
Published: September 2022

In this paper, we describe a pilot implementation and field test of a recently developed approach to real-time adaptive traffic signal control. The pilot system, called SURTRAC (Scalable Urban Traffic Control), follows the perspective of recent work in multi-agent planning and implements a decentralized, schedule-driven approach to traffic signal control. Under this approach, each intersection independently (and asynchronously) computes a schedule that optimizes the flow of currently approaching traffic through that intersection, and uses this schedule to decide when to switch green phases. The traffic outflows projected by this schedule are then communicated to the intersection's downstream neighbors, to increase visibility of vehicles entering their respective planning horizons. This process is repeated as frequently as once per second in rolling horizon fashion, to provide real-time responsiveness to changing traffic conditions and coordinated signal network behavior. After summarizing this basic approach to adaptive traffic signal control and the domain challenges it is intended to address, we describe the pilot implementation of SURTRAC and its application to a nine-intersection road network in Pittsburgh, Pennsylvania. Both the SURTRAC architecture for interfacing with the detection equipment, hardware controller and communication network at a given intersection and the extensions required to account for unreliable sensor data are discussed. Finally, we present the results of a pilot test of the system, where SURTRAC is seen to achieve major reductions in travel times and vehicle emissions over pre-existing signal timings.
Domain Predictive Control Under Uncertain Numerical State Information
Authors: Löhr Johannes , Eyerich Patrick , Winkler Stefan , Nebel Bernhard
Published: September 2022

In planning, hybrid system states consisting of logical and numerical variables are usually assumed to be completely known. In particular, for numerical state variables full knowledge of their exact values is assumed. However, in real world applications states are results of noisy measurements and imperfect actuators. Therefore, a planned sequence of state transitions might fail to lead a hybrid system to the desired goal. We show how to propagate and reason about uncertain state information directly in the planning process, enabling hybrid systems to find plans that satisfy numerical goals with predefined confidence.
Preface
Authors: Chien Steve , Do Minh , Fern Alan , Ruml Wheeler
Published: September 2022

The papers in this proceedings present the latest advances in the field of automated planning and scheduling, ranging in scope from theoretical analyses of planning and scheduling problems and processes, to new algorithms for planning and scheduling under various constraints and assumptions, and the empirical evaluation of planning and scheduling techniques. They reflect recent research trends in subareas such as optimal planning, probabilistic and nondeterministic planning, path planning, multiagent planning, and new developments in heuristics and their analysis for planning algorithms.
Program Committee
Authors: Chien Steve , Do Minh , Fern Alan , Ruml Wheeler
Published: September 2022

List of program committee members for the Twenty-Fourth International Conference on Automated Planning and Scheduling.
Spatially Distributed Multiagent Path Planning
Authors: Wilt Christopher , Botea Adi
Published: September 2022

Multiagent path planning is important in a variety of fields, ranging from games to robotics and warehouse management. Although centralized control in the joint action space can provide optimal plans, this often is computationally infeasi- ble. Decoupled planning is much more scalable. Traditional decoupled approaches perform a unit-centric decomposition, replacing a multi-agent search with a series of single-agent searches, one for each mobile unit. We introduce an orthogonal, significantly different approach, following a spatial distribution that partitions a map into high- contention, bottleneck areas and low-contention areas. Lo- cal agents called controllers are in charge with one local area each, routing mobile units in their corresponding area. Dis- tributing the knowledge across the map, each controller can observe only the state of its own area. Adjacent controllers can communicate to negotiate the transfer of mobile units. We evaluate our implemented algorithm, SDP, on real game maps with a mixture of larger areas and narrow, bottleneck gateways. The results demonstrate that spatially distributed planning can have substantial benefits in terms of makespan quality and computation speed.