Regression with respect to sensing actions and partial states

In this paper, we present a state-based regression function for planning domains where an agent does not have complete information and may have sensing actions. We consider binary domains and employ the 0-approximation [Son & Baral 2001] to define the regression function. In binary domains, the use of 0-approximation means using 3-valued states. Although planning using this approach is incomplete with respect to the full semantics, we adopt it to have a lower complexity. We prove the soundness and completeness of our regression formulation with respect to the definition of progression. More specifically, we show that (i) a plan obtained through regression for a planning problem is indeed a progression solution of that planning problem, and that (ii) for each plan found through progression, using regression one obtains that plan or an equivalent one. We then develop a conditional planner that utilizes our regression function. We prove the soundness and completeness of our planning algorithm and present experimental results with respect to several well known planning problems in the literature.Comment: 38 page

A State-Based Regression Formulation for Domains with Sensing Actions<br> and Incomplete Information

We present a state-based regression function for planning domains where an agent does not have complete information and may have sensing actions. We consider binary domains and employ a three-valued characterization of domains with sensing actions to define the regression function. We prove the soundness and completeness of our regression formulation with respect to the definition of progression. More specifically, we show that (i) a plan obtained through regression for a planning problem is indeed a progression solution of that planning problem, and that (ii) for each plan found through progression, using regression one obtains that plan or an equivalent one.Comment: 34 pages, 7 Figure

ASPER: Answer Set Programming Enhanced Neural Network Models for Joint Entity-Relation Extraction

A plethora of approaches have been proposed for joint entity-relation (ER) extraction. Most of these methods largely depend on a large amount of manually annotated training data. However, manual data annotation is time consuming, labor intensive, and error prone. Human beings learn using both data (through induction) and knowledge (through deduction). Answer Set Programming (ASP) has been a widely utilized approach for knowledge representation and reasoning that is elaboration tolerant and adept at reasoning with incomplete information. This paper proposes a new approach, ASP-enhanced Entity-Relation extraction (ASPER), to jointly recognize entities and relations by learning from both data and domain knowledge. In particular, ASPER takes advantage of the factual knowledge (represented as facts in ASP) and derived knowledge (represented as rules in ASP) in the learning process of neural network models. We have conducted experiments on two real datasets and compare our method with three baselines. The results show that our ASPER model consistently outperforms the baselines

Strahlungseffekte bei instationären Heizdrahtmessungen an porösen Wärmedämmstoffen

Die Integral-Differentialgleichung zur Beschreibung des Energietransports der gekoppelten Strahlung/Leitung in einem grauen, absorbierenden und emittierenden, von zwei koaxial zylindrischen Oberflächen begrenzten Medium wird mit der Methode der Finiten Differenzen numerisch gelöst, um Strahlungseffekte bei instationären Heizdrahtmessungen an porösen Wärmedämmstoffen theoretisch zu analysieren. Der Einfluss des Extinktionskoeffizienten des Probenmediums, der Emissivität des Heizdrahtes, der Messtemperatur, der Heizleistung und des thermischen Kontaktwiderstandes wird untersucht. Aufgrund der Berechnungsergebnisse wird die effektive Wärmeleitfähigkeit poröser Wärmedämmstoffe unterbestimmt, wenn deren Extinktionskoeffizienten klein sind. Die Untergrenze des erlaubten Extinktionsbereiches verschiebt sich zu größeren Werten mit zunehmender Messtemperatur. Der thermische Kontaktwiderstand bedeutet eine Verfälschungsgefahr von Messergebnissen, insbesondere wenn der Heizdraht stark emittiert und die Probenoberfläche reflektiert

Current status and behavior modeling on household solid-waste separation: a case study in Da Nang city, Vietnam

This study focused on household solid-waste recycling in Da Nang city, Vietnam to assess the existing separation behavior and clarify the factors influencing the separation behavior. The authors conducted a questionnaire survey for 150 households in 6 urban districts, which consisted of household attributes, separation behavior, and the household's attitude on recycling and the environment. The waste separation rates were determined for leftover food and 13 recyclable items and the recyclable disposal habit was also assessed. The separation rate of leftover food was 77.3%. Among 13 surveyed recyclable items, plastic bottles and metal cans were two popular items with higher separation rate (72.5% and 63.8%, respectively). To identify the conscious structure and determinants of separation behavior, the authors developed a predictive model on the separation behavior of leftover food and recyclables by logistic and multiple linear regression analyses. The positive factors included behavior intention, sympathy for the collector, incentive brought by recycling, goal intention, internal norm, and perception of responsibility and seriousness. The negative factor was evaluation of trouble. The authors also analyzed the differences in separation rates among attributes. Based on the significant influence factors and attributes, the authors suggested how to promote separation behavior