bluuebunny/crossref_metadata_2025 huggingface dataset:
CAMINHANDO DE COSTAS: PAU BRASIL(OSWALD DE ANDRADE) E BOITEMPO (CARLOS DRUMMOND DE ANDRADE)
Authors: Said Roberto
Published: September 2022

Resumo Pau-Brasil (1925), de Oswald de Andrade, e Boitempo (1968), de Carlos Drummond de Andrade, são obras limiares, à medida que se situam respectivamente na abertura e no fechamento da intermitente era modernista da literatura brasileira. Além disso, as duas coletâneas de poemas revisitam o passado colonial e oligárquico, construindo intepretações singulares sobre a história do Brasil. Nesse sentido, partimos do pressuposto de que elas constroem internamente, cada uma a seu modo, uma historicidade própria, definida a partir de um tempo compósito. Este artigo pretende realizar uma leitura comparada entre as duas obras, a fim de investigar o modo como elas repensam, em viés poético, elementos estruturais de nossa formação histórica e social.
Do the previous crop and top-dressing nitrogen fertilization change the yield and physiological and sanitary quality of common bean seeds?
Authors: Cunha-Chiamolera Tatiana Pagan Loeiro da , Lemos Leandro Borges , Coelho Anderson Prates , Mingotte Fábio Luiz Checchio
Published: September 2022

Abstract: The aim was to evaluate the effects of previous crop and top-dressing nitrogen fertilization on the yield and physiological and sanitary quality of common bean seeds. The design used was in randomized blocks in a split-plot scheme, with four replications. The plots were represented by three previous crops (sole maize, maize + Urochloa ruziziensis and sole U. ruziziensis), preceding common bean. The subplots consisted of N doses (urea) applied as top-dressing (0, 40, 80, 120 and 160 kg N.ha-1) when the common bean had four expanded leaves. Physiological quality and sanitary quality of the seeds were evaluated through germination tests, first count, emergence speed index, accelerated aging, seedling emergence in the field and pathogen incidence, in addition to crude protein content, 100-seed weight and yield. The study factors did not interfere in the sanitary quality of the seeds. Under U. ruziziensis straw, the highest seed yield was obtained with the N dose of 115 kg.ha-1, while under sole maize and intercropping straw, the increase in yield was linear. Previous crop with U. ruziziensis, whether cultivated as sole crop or intercropped with maize, promotes greater physiological quality of common bean seeds, while top-dressing nitrogen fertilization does not interfere with quality, weight and protein content of the seeds.
Occupational therapy and intersectoral networks: concepts and experiences in debate
Authors: Avelar Mariana Rossi , Malfitano Ana Paula Serrata
Published: September 2022

Abstract The formation of networks as a strategy for professional action is a broadly advocated theme within the framework of social policies, which have been historically organized in sectors and need to have their actions reorganized in view of the increasingly complex social demands. Networking is understood as a resource to confront the social question and its consequences, and as a tool for effecting proposals for professional intervention. Occupational therapy can use the creation and/or strengthening of social networks as processes that seek to ensure greater autonomy and social insertion to individuals or groups who are experiencing processes of rupture in their lives. This study aimed to investigate the strategy of forming intersectoral networks between the services that comprise social policies in the municipality of Campinas, state of São Paulo, Brazil, as a possible support tool focusing on the work of occupational therapists. As a method, 13 district coordinators from the social assistance, education and health areas were interviewed and a questionnaire was applied to 17 of the 36 occupational therapists (47%) working in the municipal network. In addition, two institutional networks were observed for four months. Finally, nine occupational therapists participated in a face-to-face meeting to discuss their experience and role in networks. Here, the data from the questionnaires and the group with the professionals are analyzed. In conclusion, considering the centrality of social (re)insertion as an objective of the occupational therapists, in addition to characteristics such as availability and flexibility, the promotion of intersectoral networks is a part of this work; however, this cannot be considered a specific or exclusive professional action.
Challenges for occupational therapy in mental health: an approach from the teaching experience
Authors: Osorio Diana Milena Ramírez , Montaño Jeannette Amanda Méndez
Published: September 2022

Abstract Reflecting on occupational therapy in mental health from an academic practice while utilizing a critical qualitative approach allows one to identify professional challenges in the daily live contexts of individuals and groups. Prioritizing the clinical context to care has conditioned integral and continuous processes. Occupational therapy in mental health must also transit the daily contexts in which people participate in occupations. This article reflects on occupational therapy challenges in mental health care from academic practice. These reflections are based on the use of qualitative analysis techniques used by two educators in the psychosocial area between 2013 and 2020, among which are included the documentary revision and analysis of the normative and conceptual framework, field journal, documents of systematization of experiences, participant observation and academic spaces for discussion. It allows us to observe that occupational therapy in mental health in Colombia has been structured mainly in clinical contexts, with a resolutive approach. It frequently responds to individual needs related to symptoms associated with prevalent disorders; this institutionalization of the practices limits the continuity of the processes. It is necessary to recognize the current reflections in which occupational therapists are valued professionals who promote the analysis and interaction between systems, contexts, people, populations, and occupations. This dynamic would allow responding to current policy approaches, comprehensive care, and social inclusion. The profession must contemplate subjective occupational needs in daily life contexts, making use of occupation as a tool for autonomy and social inclusion.
Bibliotecas escolares e salas de leitura importam para o aprendizado dos estudantes?
Authors: Miranda Cecília Coutinho de , Braga Daniel Santos , Cavalcanti Ana Paula Campos
Published: September 2022

Resumo O artigo discute a relação entre a existência e a utilização de espaços escolares para práticas de leitura e o desempenho em exames de proficiência em leitura de estudantes de escolas públicas e privadas no Brasil. Neste trabalho, espaços de leitura são compreendidos como bibliotecas escolares e salas de leitura. Foram utilizados os resultados do Sistema de Avaliação da Educação Básica de 2017, de alunos do 9º ano do ensino fundamental. Com base nesses dados e por meio de uma análise fatorial, elaborou-se um indicador de espaços de leitura. A partir desse indicador, foram realizadas estatísticas descritivas e inferenciais que demonstraram a pertinência do constructo. O pressuposto estabelecido é de que apenas a existência dos recursos escolares não é condição suficiente para a garantia do aprendizado, dependendo também do trabalho pedagógico nesses espaços e suas condições de uso. Os achados indicam associação entre a existência e utilização dos espaços de leitura nas escolas com melhores resultados obtidos pelos estudantes no tocante à proficiência em leitura. Os resultados apontam também que os espaços de leitura repercutem mais em escolas de nível socioeconômico mais baixo, nas quais a existência de recursos escolares, associados à utilização adequada, se convertem em vantagens, em termos de aprendizado para os alunos.
Sentidos de educação na publicidade da EaD: do paradigma do processo ao paradigma do produto
Authors: Rodrigues Eduardo Alves , Agustini Cármen L. H.
Published: September 2022

Resumo Neste artigo, com base no quadro teórico-metodológico da Análise de Discurso, analisamos peças publicitárias que colocam em oferta, no mercado, a educação a distância (EaD). Essas peças circulam socialmente – embora não exclusivamente – na modalidade online. Nosso objetivo consistiu em restituir, por meio de um dispositivo de leitura discursivo e materialista, condições de (re)leitura ao processo de produção de efeitos de sentidos evocáveis nas e pelas referidas peças publicitárias, focalizando como os sentidos de educação em jogo na publicidade da EaD deslocam a inscrição da educação (a distância), afastando-a do paradigma do processo e aproximando-a do paradigma do produto. Esse processo de significação produz como efeito a projeção da educação (a distância) ao estatuto de mercadoria. Com as análises, mostramos que, como mercadoria, a publicidade da EaD instaura discursividades que acirram a luta de classes na sociedade capitalista neoliberal pela articulação e transversalidade discursiva, que (re)atualizam a redução discursiva da EaD à profissionalização, ao menor esforço, à instantaneidade, ao digital, ao trabalho, configurando a EaD como o “novo normal” da/na educação. Como consequência dessa prática discursiva e seu impacto sobre a sociedade, as discursividades que funcionam na publicidade da EaD produzem risco à emancipação do sujeito na e pela educação (a distância).
Scalable, Parallel Best-First Search for Optimal Sequential Planning
Authors: Kishimoto Akihiro , Fukunaga Alex , Botea Adi
Published: September 2022

Large-scale, parallel clusters composed of commodity processors are increasingly available, enabling the use of vast processing capabilities and distributed RAM to solve hard search problems.  We investigate parallel algorithms for optimal sequential planning, with an emphasis on exploiting distributed memory computing clusters.  In particular, we focus on an approach which distributes and schedules work among processors based on a hash function of the search state.  We use this approach to parallelize the A* algorithm in the optimal sequential version of the Fast Downward planner.  The scaling behavior of the algorithm is evaluated experimentally on clusters using up to 128 processors, a significant increase compared to previous work in parallelizing planners.  We show that this approach scales well, allowing us to effectively utilize the large amount of distributed memory to optimally solve problems which require hundreds of gigabytes of RAM to solve. We also show that this approach scales  well for a single, shared-memory multicore machine.
Structural-Pattern Databases
Authors: Katz Michael , Domshlak Carmel
Published: September 2022

Explicit abstraction heuristics, notably pattern-database and merge-and-shrink heuristics, are employed by some state-of-the-art optimal heuristic-search planners. The major limitation of these abstraction heuristics is that the size of the abstract space has to be bounded by a (large) constant. Targeting this issue, Katz and Domshlak (2008b) introduced structural, and in particular fork-decomposition, abstractions, in which the planning task is abstracted by an instance of a tractable fragment of optimal planning. At first view, however, the lunch was not free. Some of the power of the explicit abstraction heuristics comes from pre-computing the heuristic function offline, and then determine h(s) for each evaluated state s by a very fast lookup in a "database." In contrast, fork-decomposition offer a poly-time, yet far from being fast, computation. In this contribution, we show that the time-per-node complexity bottleneck of the fork-decomposition heuristics can be successfully overcome. Specifically, we show that an equivalent of the explicit abstractions' notion of "database" exists for the fork-decomposition abstractions as well, and this despite of their exponential-size abstract spaces. Experimentally, we show that heuristic search with such "databased" fork-decomposition heuristics favorably competes with the state-of-the-art of optimal planning.
Composition of Partially Observable Services Exporting their Behaviour
Authors: De Giacomo Giuseppe , De Masellis Riccardo , Patrizi Fabio
Published: September 2022

In this paper we look at the problem of composing services that export their behavior in terms of a transition system, characterizing the choices of actions given to a client at each point in time. The composition consists of synthesizing an orchestrator that coordinates the available services so as to mimic the desired target service asked by the client. Specifically, in this paper we study the "conformant form" of the problem, where available services are partially controllable and partially observable, and hence, the orchestrator has to make its decisions exploiting the observations made so far only. We give a sound and complete procedure to synthesize the orchestrator in such case, and characterize the computational complexity of the problem. The procedure is based on working with belief (or knowledge) states, a standard technique to tackle conformant planning. Moreover we show that, although in general unavoidable, the powerset construction at the base of the belief state approach can be delegated to the symbolic manipulations of the game-structure model checking tool (TLV), which can be used to efficiently implement the orchestrator synthesis procedure.
Minimal Sufficient Explanations for Factored Markov Decision Processes
Authors: Khan Omar , Poupart Pascal , Black James
Published: September 2022

Explaining policies of Markov Decision Processes (MDPs) is complicated due to their probabilistic and sequential nature. We present a technique to explain policies for factored MDP by populating a set of domain-independent templates. We also present a mechanism to determine a minimal set of templates that, viewed together, completely justify the policy. Our explanations can be generated automatically at run-time with no additional effort required from the MDP designer. We demonstrate our technique using the problems of advising undergraduate students in their course selection and assisting people with dementia in completing the task of handwashing. We also evaluate our explanations for course-advising through a user study involving students.
From Discrete Mission Schedule to Continuous Implicit Trajectory using Optimal Time Warping
Authors: Keith Francois , Mansard Nicolas , Miossec Sylvain , Kheddar Abderrahmane
Published: September 2022

This paper presents a generic solution to apply a mission described by a sequence of tasks on a robot while accounting for its physical constraints, without computing explicitly a reference trajectory. A naive solution to this problem would be to schedule the execution of the tasks sequentially, avoiding concurrency. This solution does not exploit fully the robot capabilities such as redundancy and have poor performance in terms of execution time or energy. Our contribution is to determine the time-optimal realization of the mission taking into account robotic constraints that may be as complex as collision avoidance. Our approach achieves more than a simple scheduling; its originality lies in maintaining the task approach in the formulated optimization of the task sequencing problem. This theory is exemplified through a complete experiment on the real HRP-2 robot.
Acquisition of Object-Centred Domain Models from Planning Examples
Authors: Cresswell Stephen , McCluskey Thomas , West Margaret
Published: September 2022

The problem of formulating knowledge bases containing action schema is a central concern in knowledge engineering for AI Planning. This paper describes LOCM, a system which carries out the automated induction of action schema from sets of example plans.  Each plan is assumed to be a sound sequence of actions; each action in a plan is stated as a name and a list of objects that the action refers to. LOCM exploits the assumption that actions change the state of objects, and require objects to be in a certain state before they can be executed.  The novelty of LOCM is that it can induce action schema without being provided with any information about predicates or initial, goal or intermediate state descriptions for the example action sequences.  In this paper we describe the implemented LOCM algorithm, and analyse its performance by its application to the induction of domain models for several domains. To evaluate the algorithm, we used random action sequences from existing models of domains, as well as solutions to past IPC problems.
G-Value Plateaus: A Challenge for Planning
Authors: Benton J. , Talamadupula Kartik , Eyerich Patrick , Mattmuller Robert , Kambhampati Subbarao
Published: September 2022

While the string of successes found in using heuristic, best-first search methods have provided positive reinforcement for continuing work along these lines, fundamental problems arise when handling objectives whose value does not change with search operations. An extreme case of this occurs when handling the objective of generating a temporal plan with short makespan. Typically used heuristic search methods assume strictly positive edge costs for their guarantees on completeness and optimality, while the usual ``fattening'' and ``advance time'' steps of heuristic search for temporal planning have the potential of resulting in ``g-value plateaus''. In this paper we point out some underlying difficulties with using modern heuristic search methods when operating over g-value plateaus and discuss how the presence of these problems contributes to the poor performance of heuristic search planners. To further illustrate this, we show empirical results on recent benchmarks using a planner made with makespan optimization in mind.
Handling Goal Utility Dependencies in a Satisfiability Framework
Authors: Russell Richard , Holden Sean
Published: September 2022

Goal utility dependencies arise when the utility of achieving a goal depends on the other goals that are achieved with it. This complicates the planning procedure because achieving a new goal can potentially alter the utilities of all the other goals currently achieved. In this paper, we present an encoding procedure that enables general-purpose Max-SAT solvers to be used to solve planning problems with goal utility dependencies. We compare this approach to one using integer programming via an empirical evaluation using benchmark problems from past international planning competitions. Our results indicate that this approach is competitive and sometimes more successful than an integer programming one -- solving two to three times more subproblems in some domains, while being outperformed by only a significantly smaller margin in others.
Choosing Path Replanning Strategies for Unmanned Aircraft Systems
Authors: Wzorek Mariusz , Kvarnström Jonas , Doherty Patrick
Published: September 2022

Unmanned aircraft systems use a variety of techniques to plan collision-free flight paths given a map of obstacles and no-fly zones. However, maps are not perfect and obstacles may change over time or be detected during flight, which may invalidate paths that the aircraft is already following. Thus, dynamic in-flight replanning is required. Numerous strategies can be used for replanning, where the time requirements and the plan quality associated with each strategy depend on the environment around the original flight path. In this paper, we investigate the use of machine learning techniques, in particular support vector machines, to choose the best possible replanning strategy depending on the amount of time available. The system has been implemented, integrated and tested in hardware-in-the-loop simulation with a Yamaha RMAX helicopter platform.
Partially Informed Depth-First Search for the Job Shop Problem
Authors: Mencía Carlos , Sierra María , Varela Ramiro
Published: September 2022

We propose a partially informed depth-first search algorithm to cope with the Job Shop Scheduling Problem with makespan minimization. The algorithm is built from the well-known P. Brucker's branch and bound algorithm. We improved the heuristic estimation of Brucker's algorithm by means of constraint propagation rules and so devised a more informed heuristic which is proved to be monotonic. We conducted an experimental study across medium and large instances. The results show that the proposed algorithm reaches optimal solutions for medium instances taking less time than branch and bound and that for large instances it reaches much better lower and upper bounds when both algorithms are given the same amount of time.
When Abstractions Met Landmarks
Authors: Domshlak Carmel , Katz Michael , Lefler Sagi
Published: September 2022

Abstractions and landmarks are two powerful mechanisms for devising admissible heuristics for classical planning. Here we aim at putting them together by integrating landmark information into abstractions, and propose a concrete realization of this direction suitable for structural-pattern abstractions, as well as for other abstraction heuristics. Our empirical evaluation shows that landmark information can substantially improve the quality of abstraction heuristic estimates.
Constraint Propagation in Propositional Planning
Authors: Sideris Andreas , Dimopoulos Yannis
Published: September 2022

Planning as Satisfiability is a most successful approach to optimal propositional planning. It draws its strength from the efficiency of state-of-the-art propositional satisfiability solvers, combined with the utilization of constraints that are inferred from the problem planning graph. One of the recent improvements of the framework is the addition of long-distance mutual exclusion (londex) constraints that relate facts and actions which refer to different time steps. In this paper we compare different encodings of planning as satisfiability wrt the constraint propagation they achieve in a modern SAT solver. This analysis explains some of the differences observed in the performance of different encodings, and leads to some interesting conclusions. For instance, the Blackbox encoding achieves more propagation than the one of Satplan06, and therefore is a stronger formulation of planning as satisfiability. Moreover, our investigation suggests a new more compact and stronger model for the problem. We prove that in this new formulation many of the londex constraints are redundant in the sense that they do not add anything to the constraint propagation achieved by the model. Experimental results suggest that the theoretical results obtained are practically relevant.
Shopper: A System for Executing and Simulating Expressive Plans
Authors: Goldman Robert , Maraist John
Published: September 2022

We present Shopper, a plan execution engine that facilitates experimental evaluation of plans and makes it easier for planning researchers to incorporate replanning. Shopper interprets the LTML plan language, which extends PDDL in two major ways: with more expressive control structures, and with support for semantic web services modeled on OWL-S. LTML's command structures include not only conventional ones such as branching, iteration, and procedure calls, but also features needed to handle HTN plans, such as precondition-filtered method choice. Unlike conventional programming languages, LTML supports interaction with the agent's belief store, so that its execution semantics line up with those assumed by planners. LTML actions extend PDDL actions in having outputs as well as effects, which means that they can support actions that sense the world; an important special case of this is semantic web services, which reveal information about a state hidden from the agent. To support experimentation as well as action in the real world, Shopper accommodates multiple, swappable implementations of its primitive action API. For example, one may interact with real web services through SOAP and WSDL, or with simulated web services through local procedure calls. We describe novel features of LTML, the interpretation strategy, swappable back-ends, and the implementation.
The More, the Merrier: Combining Heuristic Estimators for Satisficing Planning
Authors: Röger Gabriele , Helmert Malte
Published: September 2022

We empirically examine several ways of exploiting the information of multiple heuristics in a satisficing best-first search algorithm, comparing their performance in terms of coverage, plan quality, speed, and search guidance. Our results indicate that using multiple heuristics for satisficing search is indeed useful. Among the combination methods we consider, the best results are obtained by the alternation method of the "Fast Diagonally Downward" planner.
Organizing Committee
Authors: Bacchus Fahiem , Domshlak Carmel , Edelkamp Stefan , Helmert Malte
Published: September 2022

List of organizers of the Twenty-First International Conference on Automated Planning and Scheduling, held in Freiburg, Germany, on June 11–16, 2011.
Scalable Scheduling for Hardware-Accelerated Functional Verification
Authors: Moffitt Michael , Günther Gernot
Published: September 2022

We consider an application of scheduling to hardware-accelerated functional verification, a massively-parallel computational paradigm used in the simulation of complex integrated circuits. Our domain requires the compilation of logical primitives into a set of instruction memories that optimize the concurrency and communication between tightly synchronized processing units. The scheduling process is burdened by a complex model in which all logical dependencies must be resolved by a dynamic network of routes that compete for sparsely distributed resources. We describe a series of optimization steps that cooperate to minimize simulation depth while scaling to problem sizes on the order of a billion gates. Our approach targets an industrial acceleration architecture containing 262,144 parallel processors.
The Multi-Round Balanced Traveling Tournament Problem
Authors: Hoshino Richard , Kawarabayashi Ken-ichi
Published: September 2022

Given an n-team sports league, the Traveling Tournament Problem (TTP) seeks to determine an optimal double round-robin schedule minimizing the sum total of distances traveled by the n teams as they move from city to city. In the TTP, the number of "rounds" is fixed at r = 2. In this paper, we propose the Multi-Round Balanced Traveling Tournament Problem (mb-TTP), inspired by the actual league structure of Japanese professional baseball, where n = 6 teams play 120 intra-league games over r = 8 rounds, subject to various constraints that ensure competitive balance. These additional balancing constraints enable us to reformulate the 2k-round mb-TTP as a shortest path problem on a directed graph, for all k >= 1. We apply our theoretical algorithm to the 6-team Nippon (Japanese) Professional Baseball Central League, creating a distance-optimal schedule with 57836 kilometres of total travel, a 26.8% reduction compared to the 79067 kilometres traveled by these six teams during the 2010 regular season.
Potential Search: A Bounded-Cost Search Algorithm
Authors: Stern Roni , Puzis Rami , Felner Ariel
Published: September 2022

In this paper we address the following search task: find a goal with cost smaller than or equal to a given fixed constant. This task is relevant in scenarios where a fixed budget is available to execute a plan and we would like to find such a plan with minimum search effort. We introduce an algorithm called Potential search (PTS) which is specifically designed to solve this problem. PTS is a best-first search that expands nodes according to the probability that they will be part of a plan whose cost is less than or equal to the given budget. We show that it is possible to implement PTS even without explicitly calculating these probabilities, when a heuristic function and knowledge about the error of this heuristic function are given. In addition, we also show that PTS can be modified to an anytime search algorithm. Experimental results show that PTS outperforms other relevant algorithms in most cases, and is more robust.
Online Planning for a Material Control System for Liquid Crystal Display Manufacturing
Authors: Do Minh , Okajima Kazumichi , Uckun Serdar , Hasegawa Fumio , Kawano Yukihiro , Tanaka Koji , Crawford Lara , Zhang Ying , Ohashi Aki
Published: September 2022

The hyper-modular printer control project at PARC has proven that a tightly integrated model-based planning and control framework can effectively control a complex physical system. Recently, we have successfully applied this framework to another application: planning for the Material Control System (MCS) of Liquid Crystal Display (LCD) manufacturing plant in a joint project between the Embedded Reasoning Area at PARC and the Products Development Center at the IHI Corporation. The model-based planner created at PARC was able to successfully solve a diverse set of test scenarios provided by IHI, including those that were deemed very difficult by the IHI experts. The short projecttime (2 months) proved that model-based planning is a flexible framework that can adapt quickly to novel applications. In this paper, we will introduce this complex domain and describe the adaptation process of the Plantrol online planner. The main contributions are: (1) introducing a successful application of general-purpose planning; (2) outline the timeline-based online temporal planner; and (3) description of a complex warehouse management problem that can serve as an attractive benchmark domain for planning.
Directed Search for Generalized Plans Using Classical Planners
Authors: Srivastava Siddharth , Immerman Neil , Zilberstein Shlomo , Zhang Tianjiao
Published: September 2022

We consider the problem of finding generalized plans for situations where the number of objects may be unknown and unbounded during planning. The input is a domain specification, a goal condition, and a class of concrete problem instances or initial states to be solved, expressed in an abstract first-order representation. Starting with an empty generalized plan, our overall approach is to incrementally increase the applicability of the plan by identifying a problem instance that it cannot solve, invoking a classical planner to solve that problem, generalizing the obtained solution and merging it back into the generalized plan. The main contributions of this paper are methods for (a) generating and solving small problem instances not yet covered by an existing generalized plan, (b) translating between concrete classical plans and abstract plan representations, and (c) extending partial generalized plans and increasing their applicability. We analyze the theoretical properties of these methods, prove their correctness, and illustrate experimentally their scalability. The resulting hybrid approach shows that solving only a few, small, classical planning problems can be sufficient to produce a generalized plan that applies to infinitely many problems with unknown numbers of objects.
A Complete Algorithm for Generating Landmarks
Authors: Bonet Blai , Castillo Julio
Published: September 2022

A collection of landmarks is complete if the cost of a minimum-cost hitting set equals h+ and there is a minimum-cost hitting set that is an optimal relaxed plan. We present an algorithm for generating a complete collection of landmarks and we show that this algorithm can be extended into effective polytime heuristics for optimal and satisficing planning. The new admissible heuristics are compared with current state-of-the-art heuristics for optimal planning on benchmark problems from the IPC.
A Polynomial All Outcome Determinization for Probabilistic Planning
Authors: Keller Thomas , Eyerich Patrick
Published: September 2022

Most predominant approaches in probabilistic planning utilize techniques from the more thoroughly investigated field of classical planning by determinizing the problem at hand. In this paper, we present a method to map probabilistic operators to an equivalent set of probabilistic operators in a novel normal form, requiring polynomial time and space. From this, we directly derive a determinization which can be used for, e.g., replanning strategies incorporating a classical planning system. Unlike previously described all outcome determinizations, the number of deterministic operators is not exponentially but polynomially bounded in the number of parallel probabilistic effects, enabling the use of more sophisticated determinization-based techniques in the future.
Sponsors
Authors: Bonet Blai
Published: September 2022

List of sponsors of the 2012 ICAPS conference.
Special Track on Continuous Planning
Authors: Bonet Blai
Published: September 2022

Program committee members of the ICAPS 2012 Special Track on Continuous Planning
Making Hybrid Plans More Clear to Human Users - A Formal Approach for Generating Sound Explanations
Authors: Seegebarth Bastian , Müller Felix , Schattenberg Bernd , Biundo Susanne
Published: September 2022

Human users who execute an automatically generated plan want to understand the rationale behind it. Knowledge-rich plans are particularly suitable for this purpose, because they provide the means to give reason for causal, temporal, and hierarchical relationships between actions. Based on this information, focused arguments can be generated that constitute explanations on an appropriate level of abstraction. In this paper, we present a formal approach to plan explanation. Information about plans is represented as first-order logic formulae and explanations are constructed as proofs in the resulting axiomatic system. With that, plan explanations are provably correct w.r.t. the planning system that produced the plan. A prototype plan explanation system implements our approach and first experiments give evidence that finding plan explanations is feasible in real-time.
Resource-Constrained Planning: A Monte Carlo Random Walk Approach
Authors: Nakhost Hootan , Hoffmann Joerg , Mueller Martin
Published: September 2022

The need to economize limited resources, such as fuel or money, is aubiquitous feature of planning problems. If the resources cannot bereplenished, the planner must make do with the initial supply. It isthen of paramount importance how constrained the problem is,i.e., whether and to which extent the initial resource supply exceedsthe minimum need. While there is a large body of literature on numericplanning and planning with resources, such resource constrainednesshas only been scantily investigated. We herein start to address thisin more detail. We generalize the previous notion of resourceconstrainedness, characterized through a numeric problem feature C≥1, to the case of multiple resources. We implement an extendedbenchmark suite controlling C. We conduct a large-scale study of thecurrent state of the art as a function of C, highlighting whichtechniques contribute to success. We introduce two new techniques ontop of a recent Monte Carlo Random Walk method, resulting in a plannerthat, in these benchmarks, outperforms previous planners whenresources are scarce (C close to 1). We investigate the parametersinfluencing the performance of that planner, and we show that one ofthe two new techniques works well also on the regular IPC benchmarks.
Fast Incremental Policy Compilation from Plans in Hybrid Probabilistic Domains
Authors: Teichteil-Königsbuch Florent
Published: September 2022

We present the domain-independent HRFF algorithm, which solves goal-oriented HMDPs by incrementally aggregating plans generated by the METRIC-FF planner into a policy defined over discrete and continuous state variables. HRFF takes into account non-monotonic state variables, and complex combinations of many discrete and continuous probability distributions. We introduce new data structures and algorithmic paradigms to deal with continuous state spaces: hybrid hierarchical hash tables, domain determinization based on dynamic domain sampling or on static computation of probability distributions' modes, optimization settings under METRIC-FF based on plan probability and length. We deeply analyze the behavior of HRFF on a probabilistically-interesting structured navigation problem with continuous dead-ends and non-monotonic continuous state variables. We compare with HAO* on the Rover domain and show that HRFF outperforms HAO* by many order of magnitudes in terms of computation time and memory usage. We also experiment challenging and combinatorial HMDP versions of benchmarks from numeric classical planning.
MDD Propagation for Disjunctive Scheduling
Authors: Cire Andre , Van Hoeve Willem-jan
Published: September 2022

Disjunctive scheduling is the problem of scheduling activities that must not overlap in time. Constraint-based techniques, such as edge finding and not first/not-last rules, have been a key element in successfully tackling large and complex disjunctive scheduling problems in recent years. In this work we investigate new propagation methods based on limited-width Multivalued Decision Diagrams (MDDs). We present theoretical properties of the MDD encoding and describe filtering and refinement operations that strengthen the relaxation it provides. Furthermore, we provide an efficient way to integrate the MDD-based reasoning with state-of-the-art propagation techniques for scheduling. Experimental results indicate that the MDD propagation can outperform existing domain filters especially when minimizing sequence dependent setup times, in certain cases by several orders of magnitude.
An Efficient Memetic Algorithm for the Flexible Job Shop with Setup Times
Authors: González Miguel Ángel , Rodríguez Vela Camino , Varela Ramiro
Published: September 2022

This paper addresses the flexible job shop scheduling problem with sequence-dependent setup times (SDSTFJSP). This is an extension of the classical job shop scheduling problem with many applications in real production environments. We propose an effective neighborhood structure for the problem, including feasibility and non improving conditions, as well as procedures for fast neighbor estimation. This neighborhood is embedded into a genetic algorithm hybridized with tabu search. We conducted an experimental study to compare the proposed algorithm with the state-of-the-art in the SDST-FJSP and also in the standard FJSP. In this study, our algorithm has obtained better results than those from other methods. Moreover, it has established new upper bounds for a number of instances.
Behavior Composition as Fully Observable Non-Deterministic Planning
Authors: Ramirez Miquel , Yadav Nitin , Sardina Sebastian
Published: September 2022

The behavior composition problem involves the automatic synthesis of a controller able to “realize” (i.e., implement) a target behavior module by suitably coordinating a collection of partially controllable available behaviors. In this paper, we show that the existence of a composition solution amounts to finding a strong cyclic plan for a special non-deterministic planning problem, thus establishing the formal link between the two synthesis tasks. Importantly, our results support the use of non-deterministic planing systemsfor solving composition problems in an off-the-shelf manner. We then empirically evaluate three state-of-the-art synthesis systems (a domain-independent automated planner and two game solvers based on model checking techniques) on various non-trivial composition instances. Our experiments show that while behavior composition is EXPTIME-complete, the current technology is already able to handle instances of significant complexity. Our work is, as far as we know, the first serious experimental work on behavior composition.
Paper Summary: Probabilistic Planning for Continuous Dynamic Systems under Bounded Risk
Authors: Ono Masahiro , Williams Brian , Blackmore Lars
Published: September 2022

This paper presents a model-based planner called the Probabilistic Sulu Planner or the p-Sulu Planner, which controls stochastic systems in a goal directed manner within user-specified risk bounds. We first develop a new plan representation called a chance-constrained qualitative state plan (CCQSP), through which users can specify the desired evolution of the plant state as well as the acceptable level of risk. We then develop the p-Sulu Planner, which can tractably solve a CCQSP planning problem.
Integrated Operations (Re-)Scheduling from Mine to Ship
Authors: Sampath Kameshwaran , Tezabwala Alfiya , Chabrier Alain , Payne Julain , Tiozzo Fabio
Published: September 2022

Mining companies have complex supply chains that start from the mining location and stretch thousands of kilometers to the end customer in a different country and continent. The logistics of moving the materials from mines to ship is composed of series of optimization problems like berth allocation, ship scheduling, stockyard scheduling, and rail scheduling, which are individually NP-hard. In this paper, we present a scheduling application, called as IBM Optimization: Mine to Ship, for end-to-end integrated operations scheduling. The application is built on IBM ILOG ODM Enterprise with advanced features like rescheduling under deviations and disturbances, and maintenance scheduling. The modeling and computational complexity of integrated scheduling optimization is tamed using hybrid optimization technique that leverages mathematical programming and constraint programming. The application will benefit the mining companies with increased resource usage, higher throughput, reduced cost of operations, and higher revenue.
Challenge: Modelling Unit Commitment as a Planning Problem
Authors: Campion Joshua , Dent Chris , Fox Maria , Long Derek , Magazzeni Daniele
Published: September 2022

Unit Commitment is a fundamental problem in power systems engineering, deciding which generating units to switch on, and when to switch them on, in order to efficiently meet anticipated demand. It has traditionally been solved as a Mixed Integer Programming (MIP) problem but upcoming changes to the power system drastically increase the MIP solution time. In this paper, we discuss the benefits that using planning may have over the established methods.We provide a formal description of Unit Commitment, and we present its formulation as MIP and as a planning problem. This is a novel and interesting application area for planning, with features that make the domain challenging for current planners.
Combining a Temporal Planner with an External Solver for the Power Balancing Problem in an Electricity Network
Authors: Piacentini Chiara , Alimisis Varvara , Fox Maria , Long Derek
Published: September 2022

The electricity network balancing problem consists of ensuring that the electricity demands of the consumers are met by the committed supply. Constraints are imposed on the different elements of the network, so that damage to the equipment is prevented when transformers are stepped up or down, or generation is increased. We consider this problem within zones, which are sub-networks constructed using carefully chosen decomposition principles. The automation of decision making in electricity networks is a step forward in their management which is necessary for coping with the increase in power system complexity that we expect in the near term. In this paper we explore the deployment of planning techniques to solve the zone-balancing problem. Embedding electricity networks in a domain description presents new challenges for planning. The key point is that the propagation of information requires complex updates to the state when an action is applied. We have developed a method in which the computation of the critical numeric quantities is performed calling an external power flow equation solver, demonstrating a clean interface between the planner and this domain-specific computation. This solver allows us to move the power flow computations outside of the planning process and update the values efficiently. We also examine a second important feature of this problem, which is the interaction between exogenous events and constraints over the entire plan trajectory within a zone.
Heuristics for Bounded-Cost Search
Authors: Haslum Patrik
Published: September 2022

The problem of searching for a plan with cost at most equal to a given absolute bound has attracted interest recently, and several search algorithms tailored specifically to this problem have been proposed. We investigate instead how to adapt planning heuristics to this setting. A few of the resulting heuristics, used in a greedy bounded-cost search, perform better than previous and baseline methods, but only by a small margin. Making effective use of the cost bound in bounded-cost planning, it appears, remains a challenge.
Incremental Dynamic Controllability Revisited
Authors: Nilsson Mikael , Kvarnström Jonas , Doherty Patrick
Published: September 2022

Simple Temporal Networks with Uncertainty (STNUs) allow the representation of temporal problems where some durations are determined by nature, as is often the case for actions in planning. As such networks are generated it is essential to verify that they are dynamically controllable -- executable regardless of the outcomes of uncontrollable durations -- and to convert them to a dispatchable form. The previously published FastIDC algorithm achieves this incrementally and can therefore be used efficiently during plan construction. In this paper we show that FastIDC is not sound when new constraints are added, sometimes labeling networks as dynamically controllable when they are not. We analyze the algorithm, pinpoint the cause, and show how the algorithm can be modified to correctly detect uncontrollable networks.
De-Cycling Cyclic Scheduling Problems
Authors: Bonfietti Alessio , Lombardi Michele , Milano Michela
Published: September 2022

An elegant way to tackle a problem that you cannot solve is to cast it to a problem that you can solve very well. Cyclic Scheduling problems are very similar to Resource Constrained Project Scheduling Problems (RCPSP), except that the project activities are repeated over time. Due to the similarity, reducing Cyclic Scheduling problems to RCPSPs seems an appealing approach. In this paper we discuss four methods to perform the reduction. The first two are existing techniques. The remaining ones are novel and include the first (to the best of our knowledge) equivalent RCPSP formulation of a cyclic problem. We compare the presented approaches in an experimental evaluation.
MUM: A Technique for Maximising the Utility of Macro-operators by Constrained Generation and Use
Authors: Chrpa Lukáš , Vallati Mauro , McCluskey Thomas
Published: September 2022

Research into techniques that reformulate problems to make general solvers more efficiently derive solutions has attracted much attention, in particular when the reformulation process is to some degree solver and domain independent. There are major challenges to overcome when applying such techniques to automated planning, however: reformulation methods such as adding macro-operators (macros, for short) can be detrimental because they tend to increase branching factors during solution search, while other methods such as learning entanglements can limit a planner's space of potentially solvable problems (its coverage) through over-pruning. These techniques may therefore work well with some domain-problem-planner combinations, but work poorly with others. In this paper we introduce a new learning technique (MUM) for synthesising macros from training example plans in order to improve the speed and coverage of domain independent automated planning engines. MUM embodies domain – independent constraints for selecting macro candidates, for generating macros, and for limiting the size of the grounding set of learned macros, therefore maximising the utility of used macros. Our empirical results with IPC benchmark domains and a range of state of the art planners demonstrate the advance that MUM makes to the increased coverage and efficiency of the planners. Comparisons with a previous leading macro learning mechanism further demonstrate MUM's capability.