18 research outputs found
Une formulation simplifiée du théorème de Bayes généralisé
International audienceIn this paper we present a simple formulation of the Generalized Bayes' Theorem (GBT) which extends Bayes' theorem in the framework of belief functions. We also present the condition under which this new formulation is valid. We illustrate our theoretical results with simple examples
DECISIVE Benchmarking Data Report: sUAS Performance Results from Phase I
This report reviews all results derived from performance benchmarking
conducted during Phase I of the Development and Execution of Comprehensive and
Integrated Subterranean Intelligent Vehicle Evaluations (DECISIVE) project by
the University of Massachusetts Lowell, using the test methods specified in the
DECISIVE Test Methods Handbook v1.1 for evaluating small unmanned aerial
systems (sUAS) performance in subterranean and constrained indoor environments,
spanning communications, field readiness, interface, obstacle avoidance,
navigation, mapping, autonomy, trust, and situation awareness. Using those 20
test methods, over 230 tests were conducted across 8 sUAS platforms: Cleo
Robotics Dronut X1P (P = prototype), FLIR Black Hornet PRS, Flyability Elios 2
GOV, Lumenier Nighthawk V3, Parrot ANAFI USA GOV, Skydio X2D, Teal Golden
Eagle, and Vantage Robotics Vesper. Best in class criteria is specified for
each applicable test method and the sUAS that match this criteria are named for
each test method, including a high-level executive summary of their
performance.Comment: Approved for public release: PAO #PR2023_74172; arXiv admin note:
substantial text overlap with arXiv:2211.0180
DECISIVE Test Methods Handbook: Test Methods for Evaluating sUAS in Subterranean and Constrained Indoor Environments, Version 1.1
This handbook outlines all test methods developed under the Development and
Execution of Comprehensive and Integrated Subterranean Intelligent Vehicle
Evaluations (DECISIVE) project by the University of Massachusetts Lowell for
evaluating small unmanned aerial systems (sUAS) performance in subterranean and
constrained indoor environments, spanning communications, field readiness,
interface, obstacle avoidance, navigation, mapping, autonomy, trust, and
situation awareness. For sUAS deployment in subterranean and constrained indoor
environments, this puts forth two assumptions about applicable sUAS to be
evaluated using these test methods: (1) able to operate without access to GPS
signal, and (2) width from prop top to prop tip does not exceed 91 cm (36 in)
wide (i.e., can physically fit through a typical doorway, although successful
navigation through is not guaranteed). All test methods are specified using a
common format: Purpose, Summary of Test Method, Apparatus and Artifacts,
Equipment, Metrics, Procedure, and Example Data. All test methods are designed
to be run in real-world environments (e.g., MOUT sites) or using fabricated
apparatuses (e.g., test bays built from wood, or contained inside of one or
more shipping containers).Comment: Approved for public release: PAO #PR2022_4705
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A Belief Theoretic Approach for Automated Collaborative Filtering
Automated Collaborative Filtering (ACF) is one of the most successful strategies available for recommender systems. Application of ACF in more sensitive and critical applications however has been hampered by the absence of better mechanisms to accommodate imperfections (ambiguities and uncertainties in ratings, missing ratings, etc.) that are inherent in user preference ratings and propagate such imperfections throughout the decision making process. Thus one is compelled to make various "assumptions" regarding the user preferences giving rise to predictions that lack sufficient integrity. With its Dempster-Shafer belief theoretic basis, CoFiDS, the automated Collaborative Filtering algorithm proposed in this thesis, can (a) represent a wide variety of data imperfections; (b) propagate the partial knowledge that such data imperfections generate throughout the decision-making process; and (c) conveniently incorporate contextual information from multiple sources. The "soft" predictions that CoFiDS generates provide substantial exibility to the domain expert. Depending on the associated DS theoretic belief-plausibility measures, the domain expert can either render a "hard" decision or narrow down the possible set of predictions to as smaller set as necessary. With its capability to accommodate data imperfections, CoFiDS widens the applicability of ACF, from the more popular domains, such as movie and book recommendations, to more sensitive and critical problem domains, such as medical expert support systems, homeland security and surveillance, etc. We use a benchmark movie dataset and a synthetic dataset to validate CoFiDS and compare it to several existing ACF systems.</p
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An Analytical Framework for Soft and Hard Data Fusion: A Dempster-Shafer Belief Theoretic Approach
The recent experiences of asymmetric urban military operations have highlighted the pressing need for incorporation of soft data, such as informant statements, into the fusion process. Soft data are fundamentally different from hard data (generated by physics-based sensors), in the sense that the information they provide tends to be qualitative and subject to interpretation. These characteristics pose a major obstacle to using existing multi-sensor data fusion frameworks, which are quite well established for hard data. Given the critical and sensitive nature of intended applications, soft/hard data fusion requires a framework that allows for convenient representation of various data uncertainties common in soft/hard data, and provides fusion techniques that are robust, mathematically justifiable, and yet effective. This would allow an analyst to make decisions with a better understanding of the associated uncertainties as well as the fusion mechanism itself. We present here a detailed account of an analytical solution to the task of soft/hard data fusion. The developed analytical framework consists of several main components: (i) a Dempster-Shafer (DS) belief theory based fusion strategy; (ii) a complete characterization of the Fagin-Halpern DS theoretic (DST) conditional notion which forms the basis of the data fusion framework; (iii) an evidence updating strategy for the purpose of consensus generation; (iv) a credibility estimation technique for validation of evidence; and (v) techniques for reducing computational burden associated with the proposed fusion framework. The proposed fusion strategy possesses several intuitively appealing features, and satisfies certain algebraic and fusion properties making it particularly useful in a soft/hard fusion environment. This strategy is based on DS belief theory which allows for convenient representation of uncertainties that are typical of soft/hard domains. The Fagin-Halpern (FH) notion is perhaps the most appropriate DST conditional notion for soft/hard data fusion scenarios. It also forms the basis for our fusion framework. We provide a complete characterization of the FH conditional notion. This constitutes a strong result, that sets the foundation for understanding the FH conditional notions and also establishes the theoretical grounds for development of algorithms for efficient computation of FH conditionals. We also address the converse problem of determining the evidence that may have generated a given change of belief. This converse result can be of significant practical value in certain applications. A consensus control strategy developed based on our fusion technique allows consensus analysis to be carried out in a multitude of applications that call for extended flexibility in uncertainty modeling. We provide a complete theoretical development of the proposed consensus strategy with rigorous proofs. We make use of these consensus notions to establish a data validation technique to assess credibility of evidence in the absence of ground truth. Credibility estimates can be used in fusion equations and also be used to estimate reliability of sources for subsequent fusion operations. Computational overhead is one of the major obstacles associated with data fusion operations, especially in DS theoretic methods. We propose a graphical procedure and its associated message passing scheme for efficient computation of the conditionals, along with the theoretical bounds for computational costs. In addition, we propose a method based on statistical sampling techniques to approximate DST data models. This allows for efficient computational representations as well as further reductions in computational costs associated with DS theoretic fusion operations. We have used several example scenarios throughout the presentation to clarify and validate the proposed notions and techniques. We conclude the dissertation by providing several guidelines for future research and summary of the work that is being presented.</p
Dynamics of Consensus Formation among Agent Opinions
This chapter explores dynamics of consensus formation among a group of adaptive agents whose states are modeled as Dempster–Shafer theoretic (DST) body of evidence (BoE). In the consensus analyses, notions from graph theory and Dempster–Shafer (DS) belief theory are utilized for modeling agent interactions and complex agent opinions that consist of numerous uncertainties, respectively. Convergence properties of the DST belief revision process under these conditions are then established utilizing the properties of paracontracting operators. A consensus scenario of a group of adaptive agents is usually characterized by individual agents having access only to imperfect information, absence of global control, decentralized evidence and communication impairments. When the communication among agents is asynchronous, spatial coupling alone is insufficient for convergence analysis. The graph of an asynchronous iteration can be used to analyze temporal coupling among fusion operators. The chapter illustrates the spatial and temporal coupling of agents