A novel model developed by MIT and Microsoft researchers identifies instances in which autonomous systems have "learned" from training examples that don't match what's actually happening in the real world. Engineers could use this model to improve the safety of artificial intelligence systems, such as driverless vehicles and autonomous robots. The AI systems powering driverless cars, for example, are trained extensively in virtual simulations to prepare the vehicle for nearly every event on the road. But sometimes the car makes an unexpected error in the real world because an event occurs that should, but doesn't, alter the car's behavior. Consider a driverless car that wasn't trained, and more importantly doesn't have the sensors necessary, to differentiate between distinctly different scenarios, such as large, white cars and ambulances with red, flashing lights on the road.

In scenarios where a robot generates and executes a plan, there may be instances where this generated plan is less costly for the robot to execute but incomprehensible to the human. When the human acts as a supervisor and is held accountable for the robot's plan, the human may be at a higher risk if the incomprehensible behavior is deemed to be unsafe. In such cases, the robot, who may be unaware of the human's exact expectations, may choose to do (1) the most constrained plan (i.e. one preferred by all possible supervisors) incurring the added cost of executing highly sub-optimal behavior when the human is observing it and (2) deviate to a more optimal plan when the human looks away. These problems amplify in situations where the robot has to fulfill multiple goals and cater to the needs of different human supervisors. In such settings, the robot, being a rational agent, should take any chance it gets to deviate to a lower cost plan. On the other hand, continuous monitoring of the robot's behavior is often difficult for the human because it costs them valuable resources (eg. time, effort, cognitive overload etc.). To optimize the cost for constant monitoring while ensuring the robots follow the {\em safe} behavior, we model this problem in the game-theoretic framework of trust where the human is the agent that trusts the robot. We show that the notion of human's trust, which is well-defined when there is a pure strategy equilibrium, is inversely proportional to the probability it assigns for observing the robot's behavior. We then show that with high probability, our game lacks a pure strategy Nash equilibrium, forcing us to define a notion of trust boundary over mixed strategies of the human in order to guarantee safe behavior by the robot.

In recent years, there has been a great deal of coverage about the dearth of PhD qualified AI data-scientists and the level of salaries qualified candidates can gain. One such piece can be found here: NYTimes article. Then you have universities complaining how their PhD qualified AI scientists are being poached by the industry thus demonstrating the demand for PhD qualified AI scientists: Guardian Article. Also, you have many universities opening numerous funded AI PhD positions such as this university: Leeds University Isn't it then obvious, a PhD in AI technology should be on all data scientists to do list. Well, as one who contemplated briefly to do a second PhD (focusing on swarm intelligence and multi-agent system in Healthcare) and who spent some time researching the necessity of completing a PhD to be across AI, I found it detrimental to undertake a PhD focusing on a specific AI algorithmic approach. This article is not meant to signal an obituary for PhD's focusing AI, but a cautionary note for someone contemplating a PhD focusing on a specific AI technique.

Methods for learning and planning in sequential decision problems often assume the learner is aware of all possible states and actions in advance. This assumption is sometimes untenable. In this paper, we give a method to learn factored markov decision problems from both domain exploration and expert assistance, which guarantees convergence to near-optimal behaviour, even when the agent begins unaware of factors critical to success. Our experiments show our agent learns optimal behaviour on small and large problems, and that conserving information on discovering new possibilities results in faster convergence.

Autonomous agents, such as self-driving cars and drones, need to make decisions in real time, which is particularly important but difficult in critical situations for example to avoid collisions. Such decisions often need to be made in a sequential manner to achieve the eventual goal (e.g., avoiding collisions and recovering to safe conditions), under partially observable environment, and by taking into account how other agents behave. Towards this far-reaching goal of realizing such autonomous agents, we propose practical techniques of sequential decision making in real time and demonstrate their effectiveness in Pommerman, a multi-agent environment that has been used in one of the competitions held at the Thirty-second Conference on Neural Information Processing Systems (NeurIPS 2018) on Dec. 8, 2018 Resnick et al. [2018a]. The techniques that we propose in this paper have been used in the Pommerman agents (HakozakiJunctions and dypm-final) who have won the first and third places in the competition. In Pommerman, a team of two agents competes against another team of two agents on a board of 11 11 grids (see Figure 1 (a) for an initial configuration of the board). Each agent can observe only a limited area of the board, and the agents cannot communicate with each other. The goal of a team is to knock down all of the opponents. Towards this goal, the agents place bombs to destroy wooden walls and collect power-up items that might appear from those wooden walls, while avoiding flames and attacking opponents. See Figure 1 (b) for an example of the board in the middle of the game.

Numerous, artificially intelligent, networked things will populate the battlefield of the future, operating in close collaboration with human warfighters, and fighting as teams in highly adversarial environments. This chapter explores the characteristics, capabilities and intelli-gence required of such a network of intelligent things and humans - Internet of Battle Things (IOBT). The IOBT will experience unique challenges that are not yet well addressed by the current generation of AI and machine learning.

This study provides a systematic review of the recent advances in designing the intelligent tutoring robot (ITR), and summarises the status quo of applying artificial intelligence (AI) techniques. We first analyse the environment of the ITR and propose a relationship model for describing interactions of ITR with the students, the social milieu and the curriculum. Then, we transform the relationship model into the perception-planning-action model for exploring what AI techniques are suitable to be applied in the ITR. This article provides insights on promoting human-robot teaching-learning process and AI-assisted educational techniques, illustrating the design guidelines and future research perspectives in intelligent tutoring robots.

Connected and automated vehicle (CAV) technology is one of the promising solutions to addressing the safety, mobility and sustainability issues of our current transportation systems. Specifically, the control algorithm plays an important role in a CAV system, since it executes the commands generated by former steps, such as communication, perception, and planning. In this study, we propose a consensus algorithm to control the longitudinal motion of CAVs in real time. Different from previous studies in this field where control gains of the consensus algorithm are pre-determined and fixed, we develop algorithms to build up a lookup table, searching for the ideal control gains with respect to different initial conditions of CAVs in real time. Numerical simulation shows that, the proposed lookup table-based consensus algorithm outperforms the authors' previous work, as well as van Arem's linear feedback-based longitudinal motion control algorithm in all four different scenarios with various initial conditions of CAVs, in terms of convergence time and maximum jerk of the simulation run.

In response to changes in industry, there are changes to this year's AnyLogic Conference. In a way, this mirrors the relationship developing between simulation and machine learning – each benefitting from the input and feedback of each other. This April's AnyLogic Conference, in addition to presentations from leading simulation practitioners, will feature an expert-led panel discussion on how agent-based simulation is helping AI develop beyond deep learning. The panel will be led by Anand Rao, Global Artificial Intelligence Lead and Partner at PwC. With 33 years industry experience and an AI background, he helps senior executives manage critical issues.

We present a method that learns to integrate temporal information, from a learned dynamics model, with ambiguous visual information, from a learned vision model, in the context of interacting agents. Our method is based on a graph-structured variational recurrent neural network (Graph-VRNN), which is trained end-to-end to infer the current state of the (partially observed) world, as well as to forecast future states. We show that our method outperforms various baselines on two sports datasets, one based on real basketball trajectories, and one generated by a soccer game engine. At any given time, you can only see a subset of the players, and you may or may not be able to see the ball, yet you probably have some reasonable idea about where all the players currently are, even if they are not in the field of view. Similarly, you cannot see the future, but you may still be able to predict where the "agents" (players and ball) will be, at least approximately. Crucially, these problems are intertwined: we are able to predict future states by using a state dynamics model, but we can also use the same dynamics model to infer the current state of the world by extrapolating from the last time we saw each agent. In this paper, we present a unified approach to state estimation and future forecasting for problems of this kind. More precisely, we assume the observed data consists of a sequence of video frames, v, obtained from a stationary or moving camera.