We present a new preprocessing algorithm for embedding the nodes of a given edge-weighted undirected graph into a Euclidean space. The Euclidean distance between any two nodes in this space approximates the length of the shortest path between them in the given graph. Later, at runtime, a shortest path between any two nodes can be computed with A* search using the Euclidean distances as heuristic. Our preprocessing algorithm, called FastMap, is inspired by the data mining algorithm of the same name and runs in near-linear time. Hence, FastMap is orders of magnitude faster than competing approaches that produce a Euclidean embedding using Semidefinite Programming. FastMap also produces admissible and consistent heuristics and therefore guarantees the generation of shortest paths. Moreover, FastMap applies to general undirected graphs for which many traditional heuristics, such as the Manhattan Distance heuristic, are not well defined. Empirically, we demonstrate that A* search using the FastMap heuristic is competitive with A* search using other state-of-the-art heuristics, such as the Differential heuristic.

To run quantum algorithms on emerging gate-model quantum hardware, quantum circuits must be compiled to take into account constraints on the hardware. For near-term hardware, with only limited means to mitigate decoherence, it is critical to minimize the duration of the circuit. We investigate the application of temporal planners to the problem of compiling quantum circuits to newly emerging quantum hardware. While our approach is general, we focus on compiling to superconducting hardware architectures with nearest neighbor constraints. Our initial experiments focus on compiling Quantum Alternating Operator Ansatz (QAOA) circuits whose high number of commuting gates allow great flexibility in the order in which the gates can be applied. That freedom makes it more challenging to find optimal compilations but also means there is a greater potential win from more optimized compilation than for less flexible circuits. We map this quantum circuit compilation problem to a temporal planning problem, and generated a test suite of compilation problems for QAOA circuits of various sizes to a realistic hardware architecture. We report compilation results from several state-of-the-art temporal planners on this test set. This early empirical evaluation demonstrates that temporal planning is a viable approach to quantum circuit compilation.

Fuelled by successes in Computer Go, Monte Carlo tree search (MCTS) has achieved widespread adoption within the games community. Its links to traditional reinforcement learning (RL) methods have been outlined in the past; however, the use of RL techniques within tree search has not been thoroughly studied yet. In this paper we re-examine in depth this close relation between the two fields; our goal is to improve the cross-awareness between the two communities. We show that a straightforward adaptation of RL semantics within tree search can lead to a wealth of new algorithms, for which the traditional MCTS is only one of the variants. We confirm that planning methods inspired by RL in conjunction with online search demonstrate encouraging results on several classic board games and in arcade video game competitions, where our algorithm recently ranked first. Our study promotes a unified view of learning, planning, and search.

AEye Inc has introduced iDAR, a new form of intelligent data collection that enables rapid, dynamic perception and path planning. AEye's iDAR is designed to intelligently prioritize and interrogate co-located pixels (2D) and voxels (3D) within a frame, enabling the system to target and identify objects within a scene 10-20x more effectively than LiDAR-only products. Additionally, iDAR is capable of overlaying 2D images on 3D point clouds for the creation of True Color LiDAR. Its embedded AI capabilities enable iDAR to utilize thousands of existing and custom computer vision algorithms, which add intelligence that can be leveraged by path planning software. The introduction of iDAR follows AEye's September demonstration of the first 360 degree, vehicle-mounted, solid-state LiDAR system with ranges up to 300 meters at high resolution.

The question to running back Melvin Gordon seemed straightforward. Did the Chargers under first-year coach Anthony Lynn, try to force the run a little too much earlier this season? "Uh, probably … um … not really," Gordon said. "Actually, I don't know, maybe." Lynn, a running backs coach for 14 of his 17 years as an NFL assistant, came to the Chargers preaching the importance of the run.

In order to have effective human AI collaboration, it is not simply enough to address the question of autonomy; an equally important question is, how the AI's behavior is being perceived by their human counterparts. When AI agent's task plans are generated without such considerations, they may often demonstrate inexplicable behavior from the human's point of view. This problem arises due to the human's partial or inaccurate understanding of the agent's planning process and/or the model. This may have serious implications on human-AI collaboration, from increased cognitive load and reduced trust in the agent, to more serious concerns of safety in interactions with physical agent. In this paper, we address this issue by modeling the notion of plan explicability as a function of the distance between a plan that agent makes and the plan that human expects it to make. To this end, we learn a distance function based on different plan distance measures that can accurately model this notion of plan explicability, and develop an anytime search algorithm that can use this distance as a heuristic to come up with progressively explicable plans. We evaluate the effectiveness of our approach in a simulated autonomous car domain and a physical service robot domain. We provide empirical evaluations that demonstrate the usefulness of our approach in making the planning process of an autonomous agent conform to human expectations.

American reached a deal late last week to pay pilots double wages for working some flights that were short-staffed after an error in the crew-scheduling system let pilots drop assignments even if no backups were available.

If you were looking forward to a new episode of "Arrow" tonight, then this news might be a bit disappointing. The hit CW series will not be airing tonight because the network switched around its broadcast schedule this week, and its latest new episode aired this past Monday night instead. The reason for this change was to accommodate the four-show crossover event, "Crisis on Earth-X," with "Supergirl," "Arrow," "The Flash" and "DC's Legends of Tomorrow." With the "Arrow" part of the crossover airing on Monday night after "Supergirl," it allowed for the event to take place over two nights instead of three, and also prevented the flow from breaking up due to the usual Wednesday block of superhero-less programming. The scheduling for "Arrow" will go back to normal next week, just in time for its Season 6 midseason finale.

There is an extensive literature on the complexity of planning, but explicit bounds on time and space complexity are very rare. On the other hand, problems like the constraint satisfaction problem (CSP) have been thoroughly analysed in this respect. We provide a number of upper- and lower-bound results (the latter based on various complexity-theoretic assumptions such as the Exponential Time Hypothesis) for both satisficing and optimal planning. We show that many classes of planning instances exhibit a dichotomy: either they can be solved in polynomial time or they cannot be solved in subexponential time. In many cases, we can even prove closely matching upper and lower bounds. Our results also indicate, analogously to CSPs, the existence of sharp phase transitions. We finally study and discuss the trade-off between time and space. In particular, we show that depth-first search may sometimes be a viable option for planning under severe space constraints.

Trump opens Asia trip with Japan's Abe against backdrop of tensions with North Korea Just one in three Americans trust Trump to handle North Korean tensions well Japan's Abe treats Trump to a day of personal diplomacy, including golf and trucker hats Brazile says Democratic primaries weren't'rigged' though some see evidence in her new book Trump is silent on Saudi king's purge though he and Salman spoke by phone Japan's Abe treats Trump to a day of personal diplomacy, including golf and trucker hats Brazile says Democratic primaries weren't'rigged' though some see evidence in her new book Trump is silent on Saudi king's purge though he and Salman spoke by phone The greatest benefit from the House Republican tax bill would go to upper-income households, according to an analysis released Monday by the nonpartisan Tax Policy Center. Middle-income taxpayers -- those earning between $48,600 and $86,100 annually -- would receive an average tax cut of $700 next year, or about 1% of their after-tax income, the analysis said. The top 20% of the nation's earners -- those making more than $149,400 a year -- would receive an average tax cut of $4,850, or about 1.4% of after-tax income. Those top earners would also receive 60% of the total tax benefits under the plan. Of that, the top 1% of earners, defined as those making more than $730,000 a year, receive about 22% of the total amount of tax cuts in 2018, the Tax Policy Center said.