Smart factories are on the verge of becoming the new industrial paradigm, wherein optimization permeates all aspects of production, from concept generation to sales. To fully pursue this paradigm, flexibility in the production means as well as in their timely organization is of paramount importance. AI is planning a major role in this transition, but the scenarios encountered in practice might be challenging for current tools. Task planning is one example where AI enables more efficient and flexible operation through an online automated adaptation and rescheduling of the activities to cope with new operational constraints and demands. In this paper we present SMarTplan, a task planner specifically conceived to deal with real-world scenarios in the emerging smart factory paradigm. Including both special-purpose and general-purpose algorithms, SMarTplan is based on current automated reasoning technology and it is designed to tackle complex application domains. In particular, we show its effectiveness on a logistic scenario, by comparing its specialized version with the general purpose one, and extending the comparison to other state-of-the-art task planners.

Value Iteration Networks (VINs) are effective differentiable path planning modules that can be used by agents to perform navigation while still maintaining end-to-end differentiability of the entire architecture. Despite their effectiveness, they suffer from several disadvantages including training instability, random seed sensitivity, and other optimization problems. In this work, we reframe VINs as recurrent-convolutional networks which demonstrates that VINs couple recurrent convolutions with an unconventional max-pooling activation. From this perspective, we argue that standard gated recurrent update equations could potentially alleviate the optimization issues plaguing VIN. The resulting architecture, which we call the Gated Path Planning Network, is shown to empirically outperform VIN on a variety of metrics such as learning speed, hyperparameter sensitivity, iteration count, and even generalization. Furthermore, we show that this performance gap is consistent across different maze transition types, maze sizes and even show success on a challenging 3D environment, where the planner is only provided with first-person RGB images.

In a report on Thursday, Ofcom said the progress toward the legal separation of BT and Openreach has been "broadly satisfactory", but some steps were yet to be completed such as the transfer of Openreach employees to the new Openreach Ltd due to complexities with BT's pension scheme.

Fast and efficient motion planning algorithms are crucial for many state-of-the-art robotics applications such as self-driving cars. Existing motion planning methods such as RRT*, A*, and D*, become ineffective as their computational complexity increases exponentially with the dimensionality of the motion planning problem. To address this issue, we present a neural network-based novel planning algorithm which generates end-to-end collision-free paths irrespective of the obstacles' geometry. The proposed method, called MPNet (Motion Planning Network), comprises of a Contractive Autoencoder which encodes the given workspaces directly from a point cloud measurement, and a deep feedforward neural network which takes the workspace encoding, start and goal configuration, and generates end-to-end feasible motion trajectories for the robot to follow. We evaluate MPNet on multiple planning problems such as planning of a point-mass robot, rigid-body, and 7 DOF Baxter robot manipulators in various 2D and 3D environments. The results show that MPNet is not only consistently computationally efficient in all 2D and 3D environments but also show remarkable generalization to completely unseen environments. The results also show that computation time of MPNet consistently remains less than 1 second which is significantly lower than existing state-of-the-art motion planning algorithms. Furthermore, through transfer learning, the MPNet trained in one scenario (e.g., indoor living places) can also quickly adapt to new scenarios (e.g., factory floors) with a little amount of data.

As autonomous service robots become more affordable and thus available also for the general public, there is a growing need for user friendly interfaces to control the robotic system. Currently available control modalities typically expect users to be able to express their desire through either touch, speech or gesture commands. While this requirement is fulfilled for the majority of users, paralyzed users may not be able to use such systems. In this paper, we present a novel framework, that allows these users to interact with a robotic service assistant in a closed-loop fashion, using only thoughts. The brain-computer interface (BCI) system is composed of several interacting components, i.e., non-invasive neuronal signal recording and decoding, high-level task planning, motion and manipulation planning as well as environment perception. In various experiments, we demonstrate its applicability and robustness in real world scenarios, considering fetch-and-carry tasks and tasks involving human-robot interaction. As our results demonstrate, our system is capable of adapting to frequent changes in the environment and reliably completing given tasks within a reasonable amount of time. Combined with high-level planning and autonomous robotic systems, interesting new perspectives open up for non-invasive BCI-based human-robot interactions.

Fans of "Days of Our Lives" and other NBC shows will have to take a day off from their favorite programs on Thursday, June 7, as the programming schedule has been altered due to coverage of the French Open. According to NBC's programming schedule for the day, "The Today Show" will still air until 11 a.m. EDT with Megyn Kelly's block of the broadcast beginning at 9 a.m., followed by Kathie Lee Gifford and Hoda Kotb's block at 10 a.m. However, at 11 a.m. EDT, the programming will switch to the network's coverage of the French Open. This will continue until 2 p.m. EDT, preempting the 11:00 News, "New York Live," "Days of Our Lives," and "Access Hollywood Live." A normal Thursday schedule resumes at 2:00 with "Steve." These scheduling changes will also be in effect on Friday, June 8.

During the 60s and 70s, AI researchers explored intuitions about intelligence by writing programs that displayed intelligent behavior. Many good ideas came out from this work but programs written by hand were not robust or general. After the 80s, research increasingly shifted to the development of learners capable of inferring behavior and functions from experience and data, and solvers capable of tackling well-defined but intractable models like SAT, classical planning, Bayesian networks, and POMDPs. The learning approach has achieved considerable success but results in black boxes that do not have the flexibility, transparency, and generality of their model-based counterparts. Model-based approaches, on the other hand, require models and scalable algorithms. Model-free learners and model-based solvers have close parallels with Systems 1 and 2 in current theories of the human mind: the first, a fast, opaque, and inflexible intuitive mind; the second, a slow, transparent, and flexible analytical mind. In this paper, I review developments in AI and draw on these theories to discuss the gap between model-free learners and model-based solvers, a gap that needs to be bridged in order to have intelligent systems that are robust and general.

We define an admissibility condition for abstractions expressed using angelic semantics and show that these conditions allow us to accelerate planning while preserving the ability to find the optimal motion plan. We then derive admissible abstractions for two motion planning domains with continuous state. We extract upper and lower bounds on the cost of concrete motion plans using local metric and topological properties of the problem domain. These bounds guide the search for a plan while maintaining performance guarantees. We show that abstraction can dramatically reduce the complexity of search relative to a direct motion planner. Using our abstractions, we find near-optimal motion plans in planning problems involving $10^{13}$ states without using a separate task planner.

Early next year, the Springer Nature publishing group will launch a new subscription journal devoted to artificial intelligence. Like its other journals, Nature will impose a pay wall and restrict access to paying customers--a move that isn't going over well with AI researchers, who say a for-profit subscription journal is not what the field needs right now. Scheduled for launch in January 2019, the new journal will be called Nature Machine Intelligence, and it'll be the 53rd journal to bear the illustrious Nature name. The new online-only journal, headed by editor-in-chief Liesbeth Venema (previously a physics editor at Nature), will cover the "best research from across the field of artificial intelligence," and will include research and perspectives from the "fast-moving" fields of AI, machine learning, and robotics. But if a petition organized by Tom Dietterich from the International Machine Learning Society and a computer scientist at Oregon University is any indication, the new journal won't include content from a sizable portion of the AI research community.

To avoid the paper being thrown in the bin we provide this with a large, negative reward, say -1, and because the teacher is please with it being placed in the bin this nets a large positive reward, 1. To avoid the outcome where it continually gets passed around the room, we set the reward for all other actions to be a small, negative value, say -0.04. If we set this as a positive or null number then the model may let the paper go round and round as it would be better to gain small positives than risk getting close to the negative outcome. This number is also very small as it will only collect a single terminal reward but it could take many steps to end the episode and we need to ensure that, if the paper is place in the bin, the positive outcome is not cancelled out. Please note, the rewards are always relative to one another and I have chosen arbitrary figures but these can be changed if the results are not as desired.