1,720,993 research outputs found
Mapping social protection statistics
Full text linkMonitoring welfare systems requires investigating several aspects of social protection activities such as the amount, kind and quality of services delivered, or the features of the demand coming from people. The geographical area where beneficiaries live, represents a further key dimension which must be considered since, in some countries, local governments are assigned managing and, sometimes, legislative competencies on social protection areas. This chapter explores the availability of comparable statistics on social protection at the sub-national level in Europe and in Italy with a special focus on social protection expenditure. The objective is assessing the possibility to derive quantitative indicators to characterize the European and Italian welfare systems at the local level. An empirical analysis of some aspects concerning social protection services, delivered by municipalities in Italy, in 2012, is here reported
Exploiting Symbolic Heuristics for the Synthesis of Domain-Specific Temporal Planning Guidance Using Reinforcement Learning
No full textRecent work investigated the use of Reinforcement Learning (RL) for the synthesis of heuristic guidance to improve the performance of temporal planners when a domain is fixed and a set of training problems (not plans) is given. The idea is to extract a heuristic from the value function of a particular (possibly infinite-state) MDP constructed over the training problems.
In this paper, we propose an evolution of this learning and planning framework that focuses on exploiting the information provided by symbolic heuristics during both the RL and planning phases. First, we formalize different reward schemata for the synthesis and use symbolic heuristics to mitigate the problems caused by the truncation of episodes needed to deal with the potentially infinite MDP. Second, we propose learning a residual of an existing symbolic heuristic, which is a "correction" of the heuristic value, instead of eagerly learning the whole heuristic from scratch. Finally, we use the learned heuristic in combination with a symbolic heuristic using a multiple-queue planning approach to balance systematic search with imperfect learned information.
We experimentally compare all the approaches, highlighting their strengths and weaknesses and significantly advancing the state of the art for this planning and learning schema
Learning of Lifted Macro-Events for Heuristic-Search Temporal Planning
No full textLearning domain knowledge from small training problems to improve planning performance on arbitrarily sized problems is a highly active research area. Many works explored the use of macro-actions to create "shortcuts" in the search space, at the cost of increasing the branching factor of the problem. In temporal planning, a recent technique proposes to equip a heuristic-search temporal planner with selected "macro-events": a "shortcut" mechanism similar to macro-actions but with state-dependent semantics.
In this paper, we generalize macro-events to a lifted representation, making them independent of specific problem objects. We devise a fully automated framework that, given a domain and a collection of small training problems, constructs and selects a suitable set of lifted macro-events. We define a learning pipeline that mixes the optimization of the statistical expectation on an abstraction of the problem with an empirical refinement of the selection on a validation set. We experimentally show that the proposed approach scales to complex problems, yielding substantial improvements over the baseline
Automatic Selection of Macro-Events for Heuristic-Search Temporal Planning
No full textOne of the major techniques to tackle temporal planning problems is heuristic search augmented with a symbolic representation of time in the states. Augmenting the problem with composite actions (macro-actions) is a simple and powerful approach to create "shortcuts" in the search space, at the cost of augmenting the branching factor of the problem and thus the expansion time of a heuristic search planner. Hence, it is of paramount importance to select the right macro-actions and minimize the number of such actions to optimize the planner performance. In this paper, we first discuss a simple, yet powerful, model similar to macro-actions for the case of temporal planning, and we call these macro-events. Then, we present a novel ranking function to extract and select a suitable set of macro-events from a dataset of valid plans. In our ranking approach, we consider an estimation of the hypothetical search space for a blind search including a candidate set of macro-events under four different exploitation schemata. Finally, we experimentally demonstrate that the proposed approach yields a substantial performance improvement for a state-of-the-art temporal planner
Temporal Task and Motion Planning with Metric Time for Multiple Object Navigation
No full textIntegrating metric time into Task And Motion Planning (TAMP) is challenging, especially with simultaneous object motion. Existing work focuses on classical and numeric TAMP, not considering deadlines, motions overlapping in time, and other temporal constraints. In this paper, we fill this gap by formalizing Temporal Task and Motion Planning (TTAMP) for multi-object navigation. We propose a novel interleaved planning technique for this problem, which leverages incremental Satisfiability Modulo Theory to ensure efficient reasoning on deadlines and action duration coupled with a motion planner supporting simultaneous object motion. Geometric data on encountered obstacles prunes unreachable symbolic regions, while temporal bounds limit the geometric search space. For multiple moving objects, our algorithm contextualizes the conflicts learned from the motion planner on overlapping actions so that entire classes of temporal plans are pruned from the search space of the task planner, ensuring the eventual termination of the interplay. We provide a comprehensive benchmark suite and demonstrate the effectiveness of our solver in leveraging these scenarios
Synthesis of Search Heuristics for Temporal Planning via Reinforcement Learning
No full textAutomated temporal planning is the problem of synthesizing, starting from a model of a system, a course of actions to achieve a desired goal when temporal constraints, such as deadlines, are present in the problem. Despite considerable successes in the literature, scalability is still a severe limitation for existing planners, especially when confronted with real-world, industrial scenarios. In this paper, we aim at exploiting recent advances in reinforcement learning, for the synthesis of heuristics for temporal planning. Starting from a set of problems of interest for a specific domain, we use a customized reinforcement learning algorithm to construct a value function that is able to estimate the expected reward for as many problems as possible. We use a reward schema that captures the semantics of the temporal planning problem and we show how the value function can be transformed in a planning heuristic for a semi-symbolic heuristic search exploration of the planning model. We show on two case-studies how this method can widen the reach of current temporal planners with encouraging results
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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