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What is the central question of this study? How does the 6-hydroxydopamine (6-OHDA)-induced Parkinson's disease model affect the respiratory response in female rats? What effect does ovariectomy have on that response? What is the main finding and its importance? The results suggest a protective effect of ovarian hormones in maintaining normal neuroanatomical integrity of the medullary respiratory nucleus in females. It was observed that ovariectomy alone reduced neurokinin-1 density in the pre-Bötzinger complex and Bötzinger complex, and there was an incremental effect of 6-OHDA and ovariectomy on retrotrapezoid nucleus neurons.Emerging evidence indicates that the course of Parkinson's disease (PD) includes autonomic and respiratory deficiencies in addition to the classical motor symptoms. The prevalence of PD is lower in women, and it has been hypothesized that neuroprotection by ovarian hormones can explain this difference. While male PD animal models present changes in the central respiratory control areas, as well as ventilatory parameters under normoxia and hypercapnia, little is known about sex differences regarding respiratory deficits in this disease background. This study aimed to explore the neuroanatomical and functional respiratory changes in intact and ovariectomized (OVX) female rats subjected to chemically induced PD via a bilateral intrastriatal injection of 6-hydroxydopamine (6-OHDA). The respiratory parameters were evaluated by whole-body plethysmography, and the neuroanatomy was monitored using immunohistochemistry. It was found that dopaminergic neurons in the substantia nigra and neurokinin-1 receptor density in the rostral ventrolateral respiratory group, Bötzinger and pre-Bötzinger complex were reduced in the chemically induced PD animals. Additionally, reduced numbers of Phox2b neurons were only observed in the retrotrapezoid nucleus of PD-OVX rats. Concerning respiratory parameters, in OVX rats, the resting and hypercapnia-induced tidal volume (V

Planning deals with the task of finding an ordered set of actions that achieves some goals from an initial state. In many real-world applications it is unfeasible to find a plan achieving all goals due to limitations in the available resources. A common case consists of having a bound on a given cost measure that is less than the optimal cost needed to achieve all goals. Oversubscription planning (OSP) is the field of Automated Planning dealing with such kinds of problems. Usually, OSP generates plans that achieve only a subset of the goals set. In this paper we present a new technique to a priori select goals in no-hard-goals satisficing OSP by searching in the space of subsets of goals. A key property of the proposed approach is that it is planner-independent once the goals have been selected; it creates a new non-OSP problem that can be solved using off-the-shelf planners. Extensive experimental results show that the proposed approach outperforms state-of-the-art OSP techniques in several domains of the International Planning Competition. This work has been partially funded by the European Union ECHORD++ project (FP7-ICT-601116), by FEDER/Ministerio de Ciencia, Innovación y Universidades/Agencia Estatal de Investigación TIN2017-88476-C2-2-R, RTC-2016-5407-4 and RTI2018-099522-B-C43 projects and the ESA GOTCHA project (4000117648/16/NL/GLC/fk). We would like to thank Michael Katz, Daniel Muller and Nir Lipovetzky for supporting us in using their planners and the reviewers for their helpful comments.

The Amazon rodent Proechimys guyannensis is widely studied for hosting various pathogens, though rarely getting sick. Previous studies on male Proechimys have revealed an endogenous resistance to epilepsy. Here, we assess in female Proechimys, whether sex hormones and biochemical aspects can interfere with the induction of status epilepticus (SE). The lithium-pilocarpine ramp-up protocol was used to induce SE, and blood sera were collected at 30 and 90 min after SE, alongside brains, for biochemical, western blot and immunohistochemical analyses. Results from non-ovariectomised (NOVX) Proechimys were compared to ovariectomised (OVX) animals. Data from female Wistars were used as a positive control of SE inductions. SE latency was similar in NOVX, OVX, and female Wistars groups. However, the pilocarpine dose required to induce SE in Proechimys was higher (25- to 50-folds more). Despite a higher dose, Proechimys did not show strong SE like Wistars; they only reached stage 2 of the Racine scale. These data suggest that female Proechimys are resistant to SE induction. Glucose and progesterone levels increased at 30 min and returned to normal at 90 min after SE. A relevant fact because in humans and rodents, SE leads to hypoglycaemia after 30 min of SE and does not return to normal levels in a short time, a typical adverse effect of SE. In OVX animals, a decrease in GABAergic receptors within 90 min of SE may suggest that ovariectomy produces changes in the hippocampus, including a certain vulnerability to seizures. We speculate that progesterone and glucose increases form part of the compensatory mechanisms that provide resistance in Proechimys against SE induction.

In this paper we present techniques for reasoning natively with quantitative/qualitative interval constraints in statebased PDDL planners. While these are considered important in modeling and solving problems in timeline based planners; reasoning with these in PDDL planners has seen relatively little attention, yet is a crucial step towards making PDDL planners applicable in real-world scenarios, such as space missions. Our main contribution is to extend the planner OPTIC to reason natively with Allen interval constraints. We show that our approach outperforms both MTP, the only PDDL planner capable of handling similar constraints and a compilation to PDDL 2.1, by an order of magnitude. We go on to present initial results indicating that our approach is competitive with a timeline based planner on a Mars rover domain, showing the potential of PDDL planners in this setting.

In this paper we describe ENSEMBLE-ROLLER, a learning-based automated planner that uses a bagging approach to enhance existing techniques for learning planning policies. Previous policy-style planning and learning systems sort state successors based on action predictions from a relational classifier. However, these learning-based planners can produce several plans of bad quality, since it is very difficult to encode in a single classifier all possible situations occurring in a planning domain. We propose to use ensembles of relational classifiers to generate more robust policies. As in other applications of machine learning, the idea of the ensembles of classifiers consists of providing accuracy for particular scenarios and diversity to cover a wide range of situations. In particular, ENSEMBLE-ROLLER learns ensembles of relational decision trees for each planning domain. The control knowledge from different sets of trees is aggregated as a single prediction or applied separately in a multiple-queue search algorithm. Experimental results show that both ways of using new policies produce on average plans of better quality.

Space observation has become one of the most common fields for showcasing planning and scheduling technology. This paper describes the solutions adopted in the solver of the Sensor Planning System, part of the Space Situational Awareness (SSA) Preparatory Programme of the European Space Agency (ESA). The goal of this solver is to find a feasible conflict-free assignment of a set of objects to be tracked by a set of sensors, given several constraints. We use an off-the-shelf temporal planner to solve this problem, Optic. A key aspect of this task is that problems are usually huge. Thus, current temporal planners can not solve those problems directly. We explain first how to model the problem as a temporal planning problem, and then we present an approach for partitioning the problem in order to be able to solve it that allows a multi-core processing. The experimental results, both with simulated and real data, confirm the approach is useful for solving target problems of realistic size.

Males of Proechimys guyannensis, a rodent living in the Amazon rainforest are studied in biomedical research because of their antiepileptogenic mechanism. Females are usually taken from experimental designs, because of limited data of this sex. This study aimed to characterize the estrous cycle to include females together with males in research in a more balanced approach. The estrous cycle of P. guyannensis based through exfoliative cytology, determination of the vaginal occlusion membrane state, and hormonal analysis. In this study, cytological analyses of vaginal smears were performed for three months, three times a day. The observed length of the estrous cycle was 247 ± 81 h (mean ± SD) with a reproductive phase of 27.08 ± 17.39 h (estrus stage). We observed a frequent presence of both the open and closed states of the vaginal membrane in the estrus stage (fertile period) although only the open stage is a prerequisite for successful copulation. High levels of progesterone and estradiol were detected in proestrus. Levels of follicle-stimulating hormone peaked at the estrus stage. These data will establish the parameters and subsidies to set the grounds for future research either for investigating the biology of this species or to use P. guyannensis in research that previously excluded females. Information regarding female Proechimys is relevant to not only describe the species but also explain the interaction between sex hormones and physiological responses. Moreover, the present results will enhance rigor and reproducibility in preclinical studies. In conclusion, P. guyannensis reproductive cycles can occur spontaneously and cyclically independent of mating stimulation and the high levels of FSH in the estrus stage, suggest that ovulation occurs in the late phase of the estrus.

This paper presents a distributed architecture for automating data mining (DM) processes using standard languages. DM is a difficult task that relies on an exploratory and analytic process of processing large quantities of data in order to discover meaningful patterns. The increasing heterogeneity and complexity of available data requires some expert knowledge on how to combine the multiple and alternative DM tasks to process the data. Here, we describe DM tasks in terms of Automated Planning, which allows us to automate the DM knowledge flow construction. The work is based on the use of standards that have been defined in both DM and automated-planning communities. Thus, we use PMML (Predictive Model Markup Language) to describe DM tasks. From the PMML, a problem description in PDDL (Planning Domain Definition Language) can be generated, so any current planning system can be used to generate a plan. This plan is, again, translated to a DM workflow description, Knowledge Flow for Machine Learning format (Knowledge Flow file for the WEKA (Waikato Environment for Knowledge Analysis) tool), so the plan or DM workflow can be executed in WEKA.

Most of the great success of heuristic search as an approach to AI Planning is due to the right design of domain-independent heuristics. Although many heuristic planners perform reasonably well, the computational cost of computing the heuristic function in every search node is very high, causing the planner to scale poorly when increasing the size of the planning tasks. For tackling this problem, planners can incorporate additional domain-dependent heuristics in order to improve their performance. Learning-based planners try to automatically acquire these domain-dependent heuristics using previous solved problems. In this work, we present a case-based reasoning approach that learns abstracted state transitions that serve as domain control knowledge for improving the planning process. The recommendations from the retrieved cases are used as guidance for pruning or ordering nodes in different heuristic search algorithms applied to planning tasks. We show that the CBR guidance is appropriate for a considerable number of planning benchmarks. This work has been partially supported by the Spanish MEC projects PELEA: TIN2008-6701-C03-03 and PlanInteraction: TIN2011-27652-C03-02. Publicado

Sequential planning portfolios are very powerful in exploiting the complementary strength of different automated planners. The main challenge of a portfolio planner is to define which base planners to run, to assign the running time for each planner and to decide in what order they should be carried out to optimize a planning metric. Portfolio configurations are usually derived empirically from training benchmarks and remain fixed for an evaluation phase. In this work, we create a per-instance configurable portfolio, which is able to adapt itself to every planning task. The proposed system pre-selects a group of candidate planners using a Pareto-dominance filtering approach and then it decides which planners to include and the time assigned according to predictive models. These models estimate whether a base planner will be able to solve the given problem and, if so, how long it will take. We define different portfolio strategies to combine the knowledge generated by the models. The experimental evaluation shows that the resulting portfolios provide an improvement when compared with non-informed strategies. One of the proposed portfolios was the winner of the Sequential Satisficing Track of the International Planning Competition held in 2014. We thank the authors of the base planners because our work is based largely on their previous effort. This work has been partially supported by the Spanish projects TIN2011-27652-C03-02, TIN2012-38079-C03-02 and TIN2014-55637-C2-1-R.

Recent discoveries in automated planning are broadening the scope of planners, from toy problems to real applications. However, applying automated planners to real-world problems is far from simple. On the one hand, the definition of accurate action models for planning is still a bottleneck. On the other hand, off-the-shelf planners fail to scale-up and to provide good solutions in many domains. In these problematic domains, planners can exploit domain-specific control knowledge to improve their performance in terms of both speed and quality of the solutions. However, manual definition of control knowledge is quite difficult. This paper reviews recent techniques in machine learning for the automatic definition of planning knowledge. It has been organized according to the target of the learning process: automatic definition of planning action models and automatic definition of planning control knowledge. In addition, the paper reviews the advances in the related field of reinforcement learning.

Current evaluation functions for heuristic planning are expensive to compute. In numerous planning problems these functions provide good guidance to the solution, so they are worth the expense. However, when evaluation functions are misguiding or when planning problems are large enough, lots of node evaluations must be computed, which severely limits the scalability of heuristic planners. In this paper, we present a novel solution for reducing node evaluations in heuristic planning based on machine learning. Particularly, we define the task of learning search control for heuristic planning as a relational classification task, and we use an off-the-shelf relational classification tool to address this learning task. Our relational classification task captures the preferred action to select in the different planning contexts of a specific planning domain. These planning contexts are defined by the set of helpful actions of the current state, the goals remaining to be achieved, and the static predicates of the planning task. This paper shows two methods for guiding the search of a heuristic planner with the learned classifiers. The first one consists of using the resulting classifier as an action policy. The second one consists of applying the classifier to generate lookahead states within a Best First Search algorithm. Experiments over a variety of domains reveal that our heuristic planner using the learned classifiers solves larger problems than state-of-the-art planners.

This chapter reports the last machine learning techniques for the assistance of automated planning. Recent discoveries in automated planning have opened the scope of planners, from toy problems to real-world applications, making new challenges come into focus. The planning community believes that machine learning can assist to address these new challenges. The chapter collects the last machine learning techniques for assisting automated planners classified in: techniques for the improvement of the planning search processes and techniques for the automatic definition of planning action models. For each technique, the chapter provides an in-depth analysis of their domain, advantages and disadvantages. Finally, the chapter draws the outline of the new promising avenues for research in learning for planning systems.

Oversubscription planning (OSP) appears in many real problems where nding a plan achieving all goals is infeasi- ble. The objective is to nd a feasible plan reaching a goal sub- set while maximizing some measure of utility. In this paper, we present a new technique to select goals \a priori" for problems in which a cost bound prevents all the goals from being achieved. It uses estimations of distances between goals, which are com- puted using relaxed plans. Using these distances, a search in the space of subsets of goals is performed, yielding a new set of goals to plan for. A revised planning problem can be created and solved, taking into account only the selected goals. We present experiments in six di erent domains with good results. This work has been partially supported by MICIIN TIN2008-06701-C03-03 and CCG10-UC3M/TIC-5597 projects. Publicado