In this paper, scalable collaborative human-robot systems for information gathering applications are approached as a decentralized Bayesian sensor network problem. Humancomputer augmented nodes and autonomous mobile sensor platforms are collaborating on a peer-to-peer basis by sharing information via wireless communication network. For each node, a computer (onboard the platform or carried by the human) implements both a decentralized Bayesian data fusion algorithm and a decentralized Bayesian control negotiation algorithm. The individual node controllers iteratively negotiate anonymously with each other in the information space to find cooperative search plans based on both observed and predicted information that explicitly consider the platforms (humans and robots) motion models, their sensors detection functions, as well as the target arbitrary motion model. The results of a collaborative multi-target search experiment conducted with a team of four autonomous mobile sensor platforms and five humans carrying small portable computers with wireless communication are presented to demonstrate the efficiency of the approach. I. INTRODUCTIONThis paper proposes an innovative scalable Bayesian approach for coordinating a network of humans and robots involved in information gathering type missions. The concept of a meta-node, to represent mobile robotic sensors and human-computer augmented systems, is introduced as a fundamental building block of a decentralized Active Sensor Network (ASN) architecture which couples decentralized communication, estimation and control.In this approach the human-computer augmented node constitute a mobile sensor unit where the human provides both the sensors and their carrying "platform", while the portable computer runs both a decentralized Bayesian fusion node and a decentralized Bayesian control negotiation algorithm. The networked controller nodes iteratively negotiate anonymously in the information space to find cooperative search plans based on both observed and predicted information that explicitly consider the humans and robots motion models, their sensors detection functions, as well as the targets arbitrary motion model. This type of decentralized architecture offers increased efficiency, reactivity, robustness and scalability by avoiding the overheads, bottlenecks and single points of failure associated with centralized structures. The proposed methodology enables synergistic human-machine interactions, with applications search and rescue, planetary exploration, mapping,

The weaponization of digital communications and social media to conduct disinformation campaigns at immense scale, speed, and reach presents new challenges to identify and counter hostile influence operations (IOs). This paper presents an end-to-end framework to automate detection of disinformation narratives, networks, and influential actors. The framework integrates natural language processing, machine learning, graph analytics, and a network causal inference approach to quantify the impact of individual actors in spreading IO narratives. We demonstrate its capability on real-world hostile IO campaigns with Twitter datasets collected during the 2017 French presidential elections and known IO accounts disclosed by Twitter over a broad range of IO campaigns (May 2007 to February 2020), over 50,000 accounts, 17 countries, and different account types including both trolls and bots. Our system detects IO accounts with 96% precision, 79% recall, and 96% area-under-the precision-recall (P-R) curve; maps out salient network communities; and discovers high-impact accounts that escape the lens of traditional impact statistics based on activity counts and network centrality. Results are corroborated with independent sources of known IO accounts from US Congressional reports, investigative journalism, and IO datasets provided by Twitter.

Estimating influence on social media networks is an important practical and theoretical problem, especially because this new medium is widely exploited as a platform for disinformation and propaganda. This paper introduces a novel approach to influence estimation on social media networks and applies it to the real-world problem of characterizing active influence operations on Twitter during the 2017 French presidential elections. The new influence estimation approach attributes impact by accounting for narrative propagation over the network using a network causal inference framework applied to data arising from graph sampling and filtering. This causal framework infers the difference in outcome as a function of exposure, in contrast to existing approaches that attribute impact to activity volume or topological features, which do not explicitly measure nor necessarily indicate actual network influence. Cramér-Rao estimation bounds are derived for parameter estimation as a step in the causal analysis, and used to achieve geometrical insight on the causal inference problem. The ability to infer high causal influence is demonstrated on real-world social media accounts that are later independently confirmed to be either directly affiliated or correlated with foreign influence operations using evidence supplied by the U.S. Congress and journalistic reports.

A method for controlling a mobile robot using qualitative inputs in the context of an approximate map, such as one sketched by a human, is presented. By defining a desired trajectory with respect to observable landmarks, human operators can send semi-autonomous robots into areas for which a true map is not available. Waypoint planning is formulated as a quadratic optimization problem which takes advantage of the probabilistic representation of the observed environment and the uncertain human input, resulting in robot trajectories in the true environment that are qualitatively similar to those provided by the human. This paper formally presents a methodology in which waypoints are extracted from a hand-drawn sketch, and obstacle avoidance is naturally accommodated through the addition of constraints in the optimization problem. A sensitivity analysis is performed to study how map distortions, sensor constraints, and a priori knowledge of the map orientation affect the performance of the planner. Lastly, a set of user studies is presented to demonstrate the robustness of the planner to different users' sketched maps and to illustrate the efficacy of such a method for mobile robot control.

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The structure and behavior of human networks have been investigated and quantitatively modeled by modern social scientists for decades, however the scope of these efforts is often constrained by the labor-intensive curation processes that are required to collect, organize, and analyze network data. The surge in online social media in recent years provides a new source of dynamic, semi-structured data of digital human networks, many of which embody attributes of real-world networks. In this paper we leverage the Reddit social media platform to study social communities whose dynamics indicate they may have experienced a disturbance event. We describe an unsupervised approach to analyzing natural language content for quantifying community similarity, monitoring temporal changes, and detecting anomalies indicative of disturbance events. We demonstrate how this method is able to detect anomalies in a spectrum of Reddit communities and discuss its applicability to unsupervised event detection for a broader class of social media use cases. 1 This distinction is subtle. In the context of this work we assume a 1:1 mapping between the (observable) network and the corresponding (sub)community it represents, and use these terms interchangeably.

A framework using Bayesian Networks (BNs) to model interactions between humans and multiple semiautonomous vehicles is introduced. Discrete operator decisions are modeled as probabilistic BN blocks with conditional dependencies on individual system states. Human decision data was collected on the RoboFlag simulation testbed, which served as an experimental platform to observe human control of simulated semiautonomous teams of robot vehicles. The data is used to generate non-parametric decision models through a method based on Parzen density estimation. The identified probabilistic graph can be used in estimation form (update distributions based on new data) or prediction form (predict the probability of a decision based on new data). Actual decision modeling results are shown using data from the RoboFlag experiments.