The maturity of sensor network technologies has facilitated the emergence of an Industrial Internet of Things (IIoT), which has collected an increasing volume of data. Converting these data into actionable intelligence for fault diagnosis is key to, e.g., reducing unscheduled downtime and performance degradation. This paper formalizes a problem called semantic fault diagnosis–to construct the formal specifications of faults directly from data collected from IIoT-enabled systems. The specifications are written as signal temporal logic formulas, which can be easily interpreted by humans. To tackle the issue of the combinatorial explosion which arises, we propose an algorithm that combines ideas from agenda-based searching and imitation learning to train a policy that searches formulas in a strategic order. Specifically, we formulate the problem as a Markov decision process, which is further solved with a reinforcement learning algorithm. Our algorithm is applied to time series data collected from an IIoT-enabled iron-making factory. The results show empirically that our proposed algorithm is both scalable to the size of the data set and interpretable, therefore allowing human users to take actions for, e.g., predictive maintenance.

Multiple-player games involving cooperative and adversarial agents are a type of problems of great practical significance. In this paper, we consider an attack-defense game with a single attacker and multiple defenders. The attacker attempts to enter a protected region, while the defenders attempt to defend the same region and capture the attacker outside the region. We propose a distributed pursuit-defense strategy for the defenders' cooperative defense against the attacker. Inside a bounded, convex, two-dimensional space, the defenders choose among an area-decreasing, a distance-decreasing, or a pursuing strategy. We prove that our strategy guarantees the attacker to be captured before entering the protected region in a finite time. We also demonstrate with simulations that a human-controlled attacker is unable to enter a protected region when multiple defenders are using our pursuit-defense strategy.

This paper presents the foundations for the analysis and modeling of human guidance behavior that is based on the emergent patterns in the closed-loop agent-environment dynamics. The central hypothesis is that these patterns, which can be explained in terms of invariants inherent to the closed-loop dynamics, provide the building blocks for the organization of human guidance behavior. The concept of interaction patterns is first introduced using a toy example and then detailed formally using dynamical system and control principles. This paper then demonstrates the existence and significance of interaction patterns in human guidance behavior that is based on data collected using guidance experiments with a miniature helicopter. The results confirm that human guidance behavior indeed exhibits invariances as defined by interaction patterns. The trajectories that are associated with each interaction pattern are then further decomposed by applying piecewise linear identification. The resulting elements are then combined under a hierarchical model that provides a natural and formal description of human guidance behavior.

There have been many researchers studying how to enable unmanned aerial vehicles (UAVs) to navigate in complex and natural environments autonomously. In this paper, we develop an imitation learning framework and use it to train navigation policies for the UAV flying inside complex and GPS-denied riverine environments. The UAV relies on a forward-pointing camera to perform reactive maneuvers and navigate itself in 2D space by adapting the heading. We compare the performance of a linear regression-based controller, an end-to-end neural network controller and a variational autoencoder (VAE)-based controller trained with data aggregation method in the simulation environments. The results show that the VAE-based controller outperforms the other two controllers in both training and testing environments and is able to navigate the UAV with a longer traveling distance and a lower intervention rate from the pilots.