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

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

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

Toward Efficient Computation of the Dempster-Shafer Belief Theoretic Conditionals

Dempster-Shafer (DS) belief theory provides a convenient framework for the development of powerful data fusion engines by allowing for a convenient representation of a wide variety of data imperfections. The recent work on the DS theoretic (DST) conditional approach, which is based on the Fagin-Halpern (FH) DST conditionals, appears to demonstrate the suitability of DS theory for incorporating both soft (generated by human-based sensors) and hard (generated by physics-based sources) evidence into the fusion process. However, the computation of the FH conditionals imposes a significant computational burden. One reason for this is the difficulty in identifying the FH conditional core, i.e., the set of propositions receiving nonzero support after conditioning. The conditional core theorem (CCT) in this paper redresses this shortcoming by explicitly identifying the conditional focal elements with no recourse to numerical computations, thereby providing a complete characterization of the conditional core. In addition, we derive explicit results to identify those conditioning propositions that may have generated a given conditional core. This "converse" to the CCT is of significant practical value for studying the sensitivity of the updated knowledge base with respect to the evidence received. Based on the CCT, we also develop an algorithm to efficiently compute the conditional masses (generated by FH conditionals), provide bounds on its computational complexity, and employ extensive simulations to analyze its behavior