Learning Task Specifications from Demonstrations

Real world applications often naturally decompose into several sub-tasks. In many settings (e.g., robotics) demonstrations provide a natural way to specify the sub-tasks. However, most methods for learning from demonstrations either do not provide guarantees that the artifacts learned for the sub-tasks can be safely recombined or limit the types of composition available. Motivated by this deficit, we consider the problem of inferring Boolean non-Markovian rewards (also known as logical trace properties or specifications) from demonstrations provided by an agent operating in an uncertain, stochastic environment. Crucially, specifications admit well-defined composition rules that are typically easy to interpret. In this paper, we formulate the specification inference task as a maximum a posteriori (MAP) probability inference problem, apply the principle of maximum entropy to derive an analytic demonstration likelihood model and give an efficient approach to search for the most likely specification in a large candidate pool of specifications. In our experiments, we demonstrate how learning specifications can help avoid common problems that often arise due to ad-hoc reward composition.Comment: NIPS 201

A simple novel device for air sampling by electrokinetic capture.

BackgroundA variety of different sampling devices are currently available to acquire air samples for the study of the microbiome of the air. All have a degree of technical complexity that limits deployment. Here, we evaluate the use of a novel device, which has no technical complexity and is easily deployable.ResultsAn air-cleaning device powered by electrokinetic propulsion has been adapted to provide a universal method for collecting samples of the aerobiome. Plasma-induced charge in aerosol particles causes propulsion to and capture on a counter-electrode. The flow of ions creates net bulk airflow, with no moving parts. A device and electrode assembly have been re-designed from air-cleaning technology to provide an average air flow of 120 lpm. This compares favorably with current air sampling devices based on physical air pumping. Capture efficiency was determined by comparison with a 0.4 μm polycarbonate reference filter, using fluorescent latex particles in a controlled environment chamber. Performance was compared with the same reference filter method in field studies in three different environments. For 23 common fungal species by quantitative polymerase chain reaction (qPCR), there was 100 % sensitivity and apparent specificity of 87 %, with the reference filter taken as "gold standard." Further, bacterial analysis of 16S RNA by amplicon sequencing showed equivalent community structure captured by the electrokinetic device and the reference filter. Unlike other current air sampling methods, capture of particles is determined by charge and so is not controlled by particle mass. We analyzed particle sizes captured from air, without regard to specific analyte by atomic force microscopy: particles at least as low as 100 nM could be captured from ambient air.ConclusionsThis work introduces a very simple plug-and-play device that can sample air at a high-volume flow rate with no moving parts and collect particles down to the sub-micron range. The performance of the device is substantially equivalent to capture by pumping through a filter for microbiome analysis by quantitative PCR and amplicon sequencing

ANALISIS KELAYAKAN LOKASI TEMPAT PEMROSESAN AKHIR SAMPAH AIRMADIDI BAWAH KABUPATEN MINAHASA UTARA

Waste Treatment Location (TPA) in North Minahasa Regency need attention, because of the existing TPA has been ineffective so that will impact on the operational feasibility of the TPA. The current TPA is a reservoir of garbage from all over North Minahasa Regency. The management of waste in the TPA is only dredging and filling with the new waste. A new TPA becomes an urgency as a replacement for the old, because without the a new waste disposal location , may cause contamination. This study aimed to evaluate the feasibility of Waste Disposal Location  (TPA) in  Airmadidi Regency based on eligibility criteria of  Indonesia National Standard. The benefits of this research that it can be use as a reference for the Planning and Regional Development studies in North Minahasa Regency to determent the worthiness of the existing waste Treatment location, and can be taken into consideration for regional planning regarding the worthiness of the existing waste treatment location. Research results show that in TPA Airmadidi the level of feasibility is enough or qualified in terms with minor repairs. In the aspect of social and community perceptions  show that the level of eligibility Very Good, whereas in the physical aspects show that the aspects of waste management are at the feasibility level is less. It can be predicted that the capacity of Waste Treatment Location (TPA) in Airmadidi Regency until the end of 2025 is 458,121.9 metrics. This shows at the end of 2025 could still accommodate a charge of 8479.75 metrics with a high pile has reached 9.81 meter. It means that the existing TPA can still operate above 10 years but with less remaining operating time. However at the end of 2030, the existing TPA is already over load of 173,911.82 metrics with a height of pile 13.8 meter

Learning Formal Specifications from Membership and Preference Queries

Active learning is a well-studied approach to learning formal specifications, such as automata. In this work, we extend active specification learning by proposing a novel framework that strategically requests a combination of membership labels and pair-wise preferences, a popular alternative to membership labels. The combination of pair-wise preferences and membership labels allows for a more flexible approach to active specification learning, which previously relied on membership labels only. We instantiate our framework in two different domains, demonstrating the generality of our approach. Our results suggest that learning from both modalities allows us to robustly and conveniently identify specifications via membership and preferences.Comment: 6 pages, Presented at ICML 2023 Workshop on The Many Facets of Preference-Based Learnin