From drone swarms to AI border guards: How futuristic technology could be used to police Britain's borders


Whether it is the Irish backstop or English Channel, the issue of how the UK and Europe are controlling their borders has been thrust into the public consciousness. And as with many of the globe's conundrums, countries and private companies are turning to ever more futuristic, and often controversial, technologies in order to protect their borders. There are, of course, immediate issues for Britain's borders with quandaries such as the potential hard border in Northern Ireland following Brexit, with the nebulous'technology' promised by some politicians either still being developed or put under question. One such future proposal is a satellite system that registered mobile phones as they pass the border, while sensors buried in the ground or radars on flying drones could detect possible unlawful breaches of the boundaries. But that would still leave the question of invasive, even if largely invisible, checks that run against the Good Friday Agreement.

Solving Multiagent Planning Problems with Concurrent Conditional Effects Artificial Intelligence

In this work we present a novel approach to solving concurrent multiagent planning problems in which several agents act in parallel. Our approach relies on a compilation from concurrent multiagent planning to classical planning, allowing us to use an off-the-shelf classical planner to solve the original multiagent problem. The solution can be directly interpreted as a concurrent plan that satisfies a given set of concurrency constraints, while avoiding the exponential blowup associated with concurrent actions. Our planner is the first to handle action effects that are conditional on what other agents are doing. Theoretically, we show that the compilation is sound and complete. Empirically, we show that our compilation can solve challenging multiagent planning problems that require concurrent actions.

Novelty Messages Filtering for Multi Agent Privacy-preserving Planning Artificial Intelligence

In multi-agent planning, agents jointly compute a plan that achieves mutual goals, keeping certain information private to the individual agents. Agents' coordination is achieved through the transmission of messages. These messages can be a source of privacy leakage as they can permit a malicious agent to collect information about other agents' actions and search states. In this paper, we investigate the usage of novelty techniques in the context of (decentralised) multi-agent privacy-preserving planning, addressing the challenges related to the agents' privacy and performance. In particular, we show that the use of novelty based techniques can significantly reduce the number of messages transmitted among agents, better preserving their privacy and improving their performance. An experimental study analyses the effectiveness of our techniques and compares them with the state-of-the-art. Finally, we evaluate the robustness of our approach, considering different delays in the transmission of messages as they would occur in overloaded networks, due for example to massive attacks or critical situations.

Neural Replicator Dynamics Artificial Intelligence

In multiagent learning, agents interact in inherently nonstationary environments due to their concurrent policy updates. It is, therefore, paramount to develop and analyze algorithms that learn effectively despite these nonstationarities. A number of works have successfully conducted this analysis under the lens of evolutionary game theory (EGT), wherein a population of individuals interact and evolve based on biologically-inspired operators. These studies have mainly focused on establishing connections to value-iteration based approaches in stateless or tabular games. We extend this line of inquiry to formally establish links between EGT and policy gradient (PG) methods, which have been extensively applied in single and multiagent learning. We pinpoint weaknesses of the commonly-used softmax PG algorithm in adversarial and nonstationary settings and contrast PG's behavior to that predicted by replicator dynamics (RD), a central model in EGT. We consequently provide theoretical results that establish links between EGT and PG methods, then derive Neural Replicator Dynamics (NeuRD), a parameterized version of RD that constitutes a novel method with several advantages. First, as NeuRD reduces to the well-studied no-regret Hedge algorithm in the tabular setting, it inherits no-regret guarantees that enable convergence to equilibria in games. Second, NeuRD is shown to be more adaptive to nonstationarity, in comparison to PG, when learning in canonical games and imperfect information benchmarks including Poker. Thirdly, modifying any PG-based algorithm to use the NeuRD update rule is straightforward and incurs no added computational costs. Finally, while single-agent learning is not the main focus of the paper, we verify empirically that NeuRD is competitive in these settings with a recent baseline algorithm.

Multi-user Resource Control with Deep Reinforcement Learning in IoT Edge Computing Machine Learning

By leveraging the concept of mobile edge computing (MEC), massive amount of data generated by a large number of Internet of Things (IoT) devices could be offloaded to MEC server at the edge of wireless network for further computational intensive processing. However, due to the resource constraint of IoT devices and wireless network, both the communications and computation resources need to be allocated and scheduled efficiently for better system performance. In this paper, we propose a joint computation offloading and multi-user scheduling algorithm for IoT edge computing system to minimize the long-term average weighted sum of delay and power consumption under stochastic traffic arrival. We formulate the dynamic optimization problem as an infinite-horizon average-reward continuous-time Markov decision process (CTMDP) model. One critical challenge in solving this MDP problem for the multi-user resource control is the curse-of-dimensionality problem, where the state space of the MDP model and the computation complexity increase exponentially with the growing number of users or IoT devices. In order to overcome this challenge, we use the deep reinforcement learning (RL) techniques and propose a neural network architecture to approximate the value functions for the post-decision system states. The designed algorithm to solve the CTMDP problem supports semi-distributed auction-based implementation, where the IoT devices submit bids to the BS to make the resource control decisions centrally. Simulation results show that the proposed algorithm provides significant performance improvement over the baseline algorithms, and also outperforms the RL algorithms based on other neural network architectures.

RLH Corporation Launches Artificial Intelligence Virtual Agent For Central Reservations Across Entire Brand Portfolio Red Lion Hotels Corporation


DENVER, June 17, 2019 (GLOBE NEWSWIRE) -- RLH Corporation (NYSE:RLH) announced today that they have successfully deployed an industry-first Artificial Intelligence Virtual Agent for Central Reservations to support all 1,400 system hotels. This announcement follows the successful completion of a three-month program pilot originally announced on April 24, 2019. "The results of our pilot have been very positive," said RLH Corporation Director of Telephony Optimization & Analytics, April Weatherly. "We're seeing travelers engage with the technology at a rate higher than we anticipated and are very pleased with the Virtual Agent's ability to resolve traveler questions autonomously, reducing the cost of non-revenue related calls." In concert with the systemwide rollout of the Virtual Agent, new features and capabilities have been deployed to further support travelers.

Evolutionary Reinforcement Learning for Sample-Efficient Multiagent Coordination Artificial Intelligence

A key challenge for Multiagent RL (Reinforcement Learning) is the design of agent-specific, local rewards that are aligned with sparse global objectives. In this paper, we introduce MERL (Multiagent Evolutionary RL), a hybrid algorithm that does not require an explicit alignment between local and global objectives. MERL uses fast, policy-gradient based learning for each agent by utilizing their dense local rewards. Concurrently, an evolutionary algorithm is used to recruit agents into a team by directly optimizing the sparser global objective. We explore problems that require coupling (a minimum number of agents required to coordinate for success), where the degree of coupling is not known to the agents. We demonstrate that MERL's integrated approach is more sample-efficient and retains performance better with increasing coupling orders compared to MADDPG, the state-of-the-art policy-gradient algorithm for multiagent coordination.

PACMAN: A Planner-Actor-Critic Architecture for Human-Centered Planning and Learning Artificial Intelligence

Conventional reinforcement learning (RL) allows an agent to learn policies via environmental rewards only, with a long and slow learning curve at the beginning stage. On the contrary, human learning is usually much faster because prior and general knowledge and multiple information resources are utilized. In this paper, we propose a \textbf{P}lanner-\textbf{A}ctor-\textbf{C}ritic architecture for hu\textbf{MAN}-centered planning and learning (\textbf{PACMAN}), where an agent uses its prior, high-level, deterministic symbolic knowledge to plan for goal-directed actions, while integrates Actor-Critic algorithm of RL to fine-tune its behaviors towards both environmental rewards and human feedback. This is the first unified framework where knowledge-based planning, RL, and human teaching jointly contribute to the policy learning of an agent. Our experiments demonstrate that PACMAN leads to a significant jump start at the early stage of learning, converges rapidly and with small variance, and is robust to inconsistent, infrequent and misleading feedback.

Interactive Differentiable Simulation Machine Learning

Intelligent agents need a physical understanding of the world to predict the impact of their actions in the future. While learning-based models of the environment dynamics have contributed to significant improvements in sample efficiency compared to model-free reinforcement learning algorithms, they typically fail to generalize to system states beyond the training data, while often grounding their predictions on non-interpretable latent variables. We introduce Interactive Differentiable Simulation (IDS), a differentiable physics engine, that allows for efficient, accurate inference of physical properties of rigid-body systems. Integrated into deep learning architectures, our model is able to accomplish system identification using visual input, leading to an interpretable model of the world whose parameters have physical meaning. We present experiments showing automatic task-based robot design and parameter estimation for nonlinear dynamical systems by automatically calculating gradients in IDS. When integrated into an adaptive model-predictive control algorithm, our approach exhibits orders of magnitude improvements in sample efficiency over model-free reinforcement learning algorithms on challenging nonlinear control domains.