scholarly journals Managing Different Sources of Uncertainty in a BDI Framework in a Principled Way with Tractable Fragments

2017 ◽  
Vol 58 ◽  
pp. 731-775
Author(s):  
Kim Bauters ◽  
Kevin McAreavey ◽  
Weiru Liu ◽  
Jun Hong ◽  
Lluís Godo ◽  
...  
Keyword(s):  
Intelligent Systems ◽  
Large Scale ◽  
Operational Semantics ◽  
Epistemic State ◽  
Bdi Agents ◽  
Epistemic States ◽  
Interacting Agents ◽  
Different Types ◽  
Syntactic Approach ◽  
Different Sources

The Belief-Desire-Intention (BDI) architecture is a practical approach for modelling large-scale intelligent systems. In the BDI setting, a complex system is represented as a network of interacting agents - or components - each one modelled based on its beliefs, desires and intentions. However, current BDI implementations are not well-suited for modelling more realistic intelligent systems which operate in environments pervaded by different types of uncertainty. Furthermore, existing approaches for dealing with uncertainty typically do not offer syntactical or tractable ways of reasoning about uncertainty. This complicates their integration with BDI implementations, which heavily rely on fast and reactive decisions. In this paper, we advance the state-of-the-art w.r.t. handling different types of uncertainty in BDI agents. The contributions of this paper are, first, a new way of modelling the beliefs of an agent as a set of epistemic states. Each epistemic state can use a distinct underlying uncertainty theory and revision strategy, and commensurability between epistemic states is achieved through a stratification approach. Second, we present a novel syntactic approach to revising beliefs given unreliable input. We prove that this syntactic approach agrees with the semantic definition, and we identify expressive fragments that are particularly useful for resource-bounded agents. Third, we introduce full operational semantics that extend CAN, a popular semantics for BDI, to establish how reasoning about uncertainty can be tightly integrated into the BDI framework. Fourth, we provide comprehensive experimental results to highlight the usefulness and feasibility of our approach, and explain how the generic epistemic state can be instantiated into various representations.

Overview of Subsynchronous Oscillation in Grid-connected Wind Farm

2020 ◽  
Vol 13 (7) ◽  
pp. 969-979
Author(s):  
Xu Pei-Zhen ◽  
Lu Yong-Geng ◽  
Cao Xi-Min
Keyword(s):  
Power Systems ◽  
Wind Turbine ◽  
Large Scale ◽  
Wind Farm ◽  
Wind Farms ◽  
Induction Generator ◽  
Future Research ◽  
Stable Operation ◽  
Subsynchronous Oscillation ◽  
Different Types

Background: Over the past few years, the subsynchronous oscillation (SSO) caused by the grid-connected wind farm had a bad influence on the stable operation of the system and has now become a bottleneck factor restricting the efficient utilization of wind power. How to mitigate and suppress the phenomenon of SSO of wind farms has become the focus of power system research. Methods: This paper first analyzes the SSO of different types of wind turbines, including squirrelcage induction generator based wind turbine (SCIG-WT), permanent magnet synchronous generator- based wind turbine (PMSG-WT), and doubly-fed induction generator based wind turbine (DFIG-WT). Then, the mechanisms of different types of SSO are proposed with the aim to better understand SSO in large-scale wind integrated power systems, and the main analytical methods suitable for studying the SSO of wind farms are summarized. Results: On the basis of results, using additional damping control suppression methods to solve SSO caused by the flexible power transmission devices and the wind turbine converter is recommended. Conclusion: The current development direction of the SSO of large-scale wind farm grid-connected systems is summarized and the current challenges and recommendations for future research and development are discussed.

Research on highly parallel embedded control system design and implementation method

Impact ◽  
2019 ◽  
Vol 2019 (10) ◽  
pp. 44-46
Author(s):  
Masato Edahiro ◽  
Masaki Gondo
Keyword(s):  
Computer Architecture ◽  
Intelligent Systems ◽  
Large Scale ◽  
General Purpose ◽  
Heterogeneous Structure ◽  
Single Chip ◽  
Powertrain Control ◽  
Processing Power ◽  
Hardware Description ◽  
Many Core

The pace of technology's advancements is ever-increasing and intelligent systems, such as those found in robots and vehicles, have become larger and more complex. These intelligent systems have a heterogeneous structure, comprising a mixture of modules such as artificial intelligence (AI) and powertrain control modules that facilitate large-scale numerical calculation and real-time periodic processing functions. Information technology expert Professor Masato Edahiro, from the Graduate School of Informatics at the Nagoya University in Japan, explains that concurrent advances in semiconductor research have led to the miniaturisation of semiconductors, allowing a greater number of processors to be mounted on a single chip, increasing potential processing power. 'In addition to general-purpose processors such as CPUs, a mixture of multiple types of accelerators such as GPGPU and FPGA has evolved, producing a more complex and heterogeneous computer architecture,' he says. Edahiro and his partners have been working on the eMBP, a model-based parallelizer (MBP) that offers a mapping system as an efficient way of automatically generating parallel code for multi- and many-core systems. This ensures that once the hardware description is written, eMBP can bridge the gap between software and hardware to ensure that not only is an efficient ecosystem achieved for hardware vendors, but the need for different software vendors to adapt code for their particular platforms is also eliminated.

Situational Breakdowns

2019 ◽  
Author(s):  
Anne Nassauer
Keyword(s):  
United States ◽  
Large Scale ◽  
Contextual Factors ◽  
The United States ◽  
Social Outcomes ◽  
Dynamic Processes ◽  
Collective Decisions ◽  
Different Types ◽  
The Impact

This book provides an account of how and why routine interactions break down and how such situational breakdowns lead to protest violence and other types of surprising social outcomes. It takes a close-up look at the dynamic processes of how situations unfold and compares their role to that of motivations, strategies, and other contextual factors. The book discusses factors that can draw us into violent situations and describes how and why we make uncommon individual and collective decisions. Covering different types of surprise outcomes from protest marches and uprisings turning violent to robbers failing to rob a store at gunpoint, it shows how unfolding situations can override our motivations and strategies and how emotions and culture, as well as rational thinking, still play a part in these events. The first chapters study protest violence in Germany and the United States from 1960 until 2010, taking a detailed look at what happens between the start of a protest and the eruption of violence or its peaceful conclusion. They compare the impact of such dynamics to the role of police strategies and culture, protesters’ claims and violent motivations, the black bloc and agents provocateurs. The analysis shows how violence is triggered, what determines its intensity, and which measures can avoid its outbreak. The book explores whether we find similar situational patterns leading to surprising outcomes in other types of small- and large-scale events: uprisings turning violent, such as Ferguson in 2014 and Baltimore in 2015, and failed armed store robberies.

scholarly journals How to measure uncertainty in uncertainty sampling for active learning

2021 ◽  
Author(s):  
Vu-Linh Nguyen ◽  
Mohammad Hossein Shaker ◽  
Eyke Hüllermeier
Keyword(s):  
Machine Learning ◽  
Active Learning ◽  
Sampling Strategies ◽  
Total Uncertainty ◽  
Uncertainty Sampling ◽  
Different Types ◽  
Alternative Approaches ◽  
Active Learner ◽  
Probabilistic Nature ◽  
Different Sources

AbstractVarious strategies for active learning have been proposed in the machine learning literature. In uncertainty sampling, which is among the most popular approaches, the active learner sequentially queries the label of those instances for which its current prediction is maximally uncertain. The predictions as well as the measures used to quantify the degree of uncertainty, such as entropy, are traditionally of a probabilistic nature. Yet, alternative approaches to capturing uncertainty in machine learning, alongside with corresponding uncertainty measures, have been proposed in recent years. In particular, some of these measures seek to distinguish different sources and to separate different types of uncertainty, such as the reducible (epistemic) and the irreducible (aleatoric) part of the total uncertainty in a prediction. The goal of this paper is to elaborate on the usefulness of such measures for uncertainty sampling, and to compare their performance in active learning. To this end, we instantiate uncertainty sampling with different measures, analyze the properties of the sampling strategies thus obtained, and compare them in an experimental study.

scholarly journals Model-Based Probabilistic Inversion Using Magnetic Data: A Case Study on the Kevitsa Deposit

Geosciences ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 150
Author(s):  
Nilgün Güdük ◽  
Miguel de la Varga ◽  
Janne Kaukolinna ◽  
Florian Wellmann
Keyword(s):  
Structural Parameters ◽  
Rock Properties ◽  
Magnetic Data ◽  
Geological Model ◽  
Borehole Data ◽  
Likelihood Functions ◽  
Different Types ◽  
Geophysical Information ◽  
Different Sources

Structural geological models are widely used to represent relevant geological interfaces and property distributions in the subsurface. Considering the inherent uncertainty of these models, the non-uniqueness of geophysical inverse problems, and the growing availability of data, there is a need for methods that integrate different types of data consistently and consider the uncertainties quantitatively. Probabilistic inference provides a suitable tool for this purpose. Using a Bayesian framework, geological modeling can be considered as an integral part of the inversion and thereby naturally constrain geophysical inversion procedures. This integration prevents geologically unrealistic results and provides the opportunity to include geological and geophysical information in the inversion. This information can be from different sources and is added to the framework through likelihood functions. We applied this methodology to the structurally complex Kevitsa deposit in Finland. We started with an interpretation-based 3D geological model and defined the uncertainties in our geological model through probability density functions. Airborne magnetic data and geological interpretations of borehole data were used to define geophysical and geological likelihoods, respectively. The geophysical data were linked to the uncertain structural parameters through the rock properties. The result of the inverse problem was an ensemble of realized models. These structural models and their uncertainties are visualized using information entropy, which allows for quantitative analysis. Our results show that with our methodology, we can use well-defined likelihood functions to add meaningful information to our initial model without requiring a computationally-heavy full grid inversion, discrepancies between model and data are spotted more easily, and the complementary strength of different types of data can be integrated into one framework.

scholarly journals Transcriptional and morphological profiling of parvalbumin interneuron subpopulations in the mouse hippocampus

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Lin Que ◽  
David Lukacsovich ◽  
Wenshu Luo ◽  
Csaba Földy
Keyword(s):  
Large Scale ◽  
Cell Types ◽  
Rna Seq ◽  
Neuronal Identity ◽  
Parvalbumin Interneurons ◽  
Different Types ◽  
Parvalbumin Interneuron ◽  
Cam Profile ◽  
Developmental Domains

AbstractThe diversity reflected by >100 different neural cell types fundamentally contributes to brain function and a central idea is that neuronal identity can be inferred from genetic information. Recent large-scale transcriptomic assays seem to confirm this hypothesis, but a lack of morphological information has limited the identification of several known cell types. In this study, we used single-cell RNA-seq in morphologically identified parvalbumin interneurons (PV-INs), and studied their transcriptomic states in the morphological, physiological, and developmental domains. Overall, we find high transcriptomic similarity among PV-INs, with few genes showing divergent expression between morphologically different types. Furthermore, PV-INs show a uniform synaptic cell adhesion molecule (CAM) profile, suggesting that CAM expression in mature PV cells does not reflect wiring specificity after development. Together, our results suggest that while PV-INs differ in anatomy and in vivo activity, their continuous transcriptomic and homogenous biophysical landscapes are not predictive of these distinct identities.

scholarly journals FlsnRNA-seq: protoplasting-free full-length single-nucleus RNA profiling in plants

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Yanping Long ◽  
Zhijian Liu ◽  
Jinbu Jia ◽  
Weipeng Mo ◽  
Liang Fang ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Walls ◽  
Large Scale ◽  
Full Length ◽  
Cell Level ◽  
Root Cells ◽  
Rna Profiling ◽  
Different Types ◽  
Long Read ◽  
Single Nucleus

AbstractThe broad application of single-cell RNA profiling in plants has been hindered by the prerequisite of protoplasting that requires digesting the cell walls from different types of plant tissues. Here, we present a protoplasting-free approach, flsnRNA-seq, for large-scale full-length RNA profiling at a single-nucleus level in plants using isolated nuclei. Combined with 10x Genomics and Nanopore long-read sequencing, we validate the robustness of this approach in Arabidopsis root cells and the developing endosperm. Sequencing results demonstrate that it allows for uncovering alternative splicing and polyadenylation-related RNA isoform information at the single-cell level, which facilitates characterizing cell identities.

scholarly journals Accessible Routes Integrating Data from Multiple Sources

2020 ◽  
Vol 10 (1) ◽  
pp. 7
Author(s):  
Miguel R. Luaces ◽  
Jesús A. Fisteus ◽  
Luis Sánchez-Fernández ◽  
Mario Munoz-Organero ◽  
Jesús Balado ◽  
...  
Keyword(s):  
Information System ◽  
Data Model ◽  
Large Scale ◽  
Heterogeneous Data ◽  
Multiple Sources ◽  
Heterogeneous Data Sources ◽  
Different Types ◽  
Software Sensors ◽  
The City

Providing citizens with the ability to move around in an accessible way is a requirement for all cities today. However, modeling city infrastructures so that accessible routes can be computed is a challenge because it involves collecting information from multiple, large-scale and heterogeneous data sources. In this paper, we propose and validate the architecture of an information system that creates an accessibility data model for cities by ingesting data from different types of sources and provides an application that can be used by people with different abilities to compute accessible routes. The article describes the processes that allow building a network of pedestrian infrastructures from the OpenStreetMap information (i.e., sidewalks and pedestrian crossings), improving the network with information extracted obtained from mobile-sensed LiDAR data (i.e., ramps, steps, and pedestrian crossings), detecting obstacles using volunteered information collected from the hardware sensors of the mobile devices of the citizens (i.e., ramps and steps), and detecting accessibility problems with software sensors in social networks (i.e., Twitter). The information system is validated through its application in a case study in the city of Vigo (Spain).

scholarly journals Posture and Health: Are the Biomechanical Postural Evaluation and the Postural Evaluation Questionnaire Comparable to and Predictive of the Digitized Biometrics Examination?

2021 ◽  
Vol 18 (7) ◽  
pp. 3507
Author(s):  
Giovanni Barassi ◽  
Edoardo Di Simone ◽  
Piero Galasso ◽  
Salvatore Cristiani ◽  
Marco Supplizi ◽  
...  
Keyword(s):  
Emotional Disorders ◽  
Healthy Subjects ◽  
Large Scale ◽  
Quantitative Investigation ◽  
Evaluation Questionnaire ◽  
Evaluation Form ◽  
Margin Of Error ◽  
Different Types ◽  
Biomechanical Evaluation ◽  
Postural Tone

Background: Postural tone alterations are expressions of myofascial and, therefore, of structural, visceral, and emotional disorders. To prevent these disorders, this study proposes a quantitative investigation method which administers a postural evaluation questionnaire and a postural biomechanical evaluation to 100 healthy subjects. Methods: The reliability of the method is studied by comparing both assessments with digitized biometrics. In addition, 50 subjects undergo the biomechanical evaluation form twice, by four different operators, to study the intraoperative repeatability. Results: The results show a satisfactory overlap between the results obtained with the postural evaluation questionnaire and the postural biomechanical evaluation compared to computerized biometrics. Furthermore, intraoperative repeatability in the use of the biomechanical evaluation form is demonstrated thanks to a minimal margin of error. Conclusions: This experience suggests the importance of undertaking this path in both the curative and the preventive sphere on a large scale and on different types of people who easily, and even unknowingly, may face dysfunctional syndromes, not only structural and myofascial but also consequently of the entire body’s homeostasis.

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