The three of us were in a 1957 de Havilland Beaver, floating in the middle of a crater lake in the southwest quadrant of Alaska. The pilot was recounting the toll that the Vietnam War had taken on him, while, over in the right seat, my boyfriend, Karl, listened. Thanks to proximity, I was listening as well, though chances are they'd forgotten I was there. Outside, water sloshed against the pontoons, rocking the plane gently from side to side. No one had asked this man to tell his story in a long time, but Karl had asked, and so the pilot put the plane down on the lake, turned off the ignition, and began.

Making travel plans is tricky even in the best of times, but the pandemic and its lingering effects have made things even more complicated. Whether you're going halfway around the world or planning a trip much closer to home, your smartphone can be a huge help. You probably already use your phone to get you from A to B as quickly as possible, but Google Maps, Apple Maps, and other apps can do much more than this--they can put together a proper itinerary for your next excursion, storing longer lists of places that you want to tick off on your travels. Planning ahead means that time away from home--whether a vacation, business trip, or anything else--is much better organized and less stressful. You can make sure you visit everywhere you want to visit in the time you have available.

Drone racing is an increasingly popular sport with big money prizes for skilled professionals. New control algorithms developed at the University of Zurich (UZH) have beaten experienced human pilots for the first time – but they still have significant limitations. In the past, attempts to develop automated algorithms to beat humans have run into problems with accurately simulating the limitations of the quadcopter and the flight path it takes. Traditional flight paths around a complex drone racing course are calculated using polynomial methods which produce a series of smooth curves, and these are not necessarily as fast as the sharper and more jagged paths flown by human pilots. A team from the Robotics and Perception Group at UZH has developed a trajectory planning algorithm to calculates the optimal route at every point in the flight, rather than doing it section by section.

Deep Q Network (DQN) has several limitations when applied in planning a path in environment with a number of dilemmas according to our experiment. The reward function may be hard to model, and successful experience transitions are difficult to find in experience replay. In this context, this paper proposes an improved Double DQN (DDQN) to solve the problem by reference to A* and Rapidly-Exploring Random Tree (RRT). In order to achieve the rich experiments in experience replay, the initialization of robot in each training round is redefined based on RRT strategy. In addition, reward for the free positions is specially designed to accelerate the learning process according to the definition of position cost in A*. The simulation experimental results validate the efficiency of the improved DDQN, and robot could successfully learn the ability of obstacle avoidance and optimal path planning in which DQN or DDQN has no effect.

Indoor navigation systems leverage shortest path algorithms to calculate routes. In order to define the "shortest path", a cost function has to be specified based on theories and heuristics in the application domain. For the domain of indoor routing, we survey theories and criteria identified in the literature as essential for human path planning. We drive quantitative definitions and integrate them into a cost function that weights each of the criteria separately. We then apply an exhaustive grid search to find weights that lead to an ideal cost function. "Ideal" here is defined as guiding the algorithm to plan routes that are most similar to those chosen by humans. To explore which criteria should be taken into account in an improved pathfinding algorithm, eleven different factors whose favorable impact on route selection has been established in past research were considered. Each factor was included separately in the Dijkstra algorithm and the similarity of thus calculated routes to the actual routes chosen by students at the University of Regensburg was determined. This allows for a quantitative assessment of the factors' impact and further constitutes a way to directly compare them. A reduction of the number of turns, streets, revolving doors, entryways, elevators as well as the combination of the aforementioned factors was found to have a positive effect and generate paths that were favored over the shortest path. Turns and the combination of criteria turned out to be most impactful.

Epistemic Planning (EP) refers to an automated planning setting where the agent reasons in the space of knowledge states and tries to find a plan to reach a desirable state from the current state. Its general form, the Multi-agent Epistemic Planning (MEP) problem involves multiple agents who need to reason about both the state of the world and the information flow between agents. In a MEP problem, multiple approaches have been developed recently with varying restrictions, such as considering only the concept of knowledge while not allowing the idea of belief, or not allowing for ``complex" modal operators such as those needed to handle dynamic common knowledge. While the diversity of approaches has led to a deeper understanding of the problem space, the lack of a standardized way to specify MEP problems independently of solution approaches has created difficulties in comparing performance of planners, identifying promising techniques, exploring new strategies like ensemble methods, and making it easy for new researchers to contribute to this research area. To address the situation, we propose a unified way of specifying EP problems - the Epistemic Planning Domain Definition Language, E-PDDL. We show that E-PPDL can be supported by leading MEP planners and provide corresponding parser code that translates EP problems specified in E-PDDL into (M)EP problems that can be handled by several planners. This work is also useful in building more general epistemic planning environments where we envision a meta-cognitive module that takes a planning problem in E-PDDL, identifies and assesses some of its features, and autonomously decides which planner is the best one to solve it.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Elizabeth Marks just wanted to prove she was fit for duty. She started swimming as a way to get back in shape and qualify for active duty again when a friend suggested she compete in the Warrior Games, a sport event for wounded, injured or ill service members and veterans. "I was just trying to be found fit for duty, and I couldn't run, so I took up swimming as a second form of cardio, and there was a gentleman there who encouraged me to try out for Warrior Games," Marks told Fox News.

Brussels – The European Union is using its strength as a wealthy trade bloc of half a billion consumers to set the global pace of climate change action, challenging others to match the ambitions of its latest carbon cutting plans. In its most ambitious bid yet to hit a goal of cutting net greenhouse gas emissions by 55% from 1990 levels by 2030, the EU on Wednesday laid out proposals that would consign the internal combustion engine to history and raise the cost of emitting carbon for heating, transport and factories. The question now is whether the EU gambit becomes an established benchmark upon which investors and sectors like the auto industry set transition strategies, and how big emitters like the United States and China respond ahead of U.N. climate talks later this year. "Amongst G7 and G20 nations, the EU position is now the explicit global benchmark," said Julian Poulter, Head of Investor Relations at Inevitable Policy Response, a consultancy on environmental economics. "It will exert a new influence on that basis, in other industrialized nations and their financial sectors, and increase pressure on those nations that remain as climate outliers and spoilers," he added.

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Classical AI planners provide solutions to planning problems in the form of long and opaque text outputs. To aid in the understanding transferability of planning solutions, it is necessary to have a rich and comprehensible representation for both human and computers beyond the current line-by-line text notation. In particular, it is desirable to encode the trace of literals throughout the plan to capture the dependencies between actions selected. The approach of this paper is to view the actions as maps between literals and the selected plan as a composition of those maps. The mathematical theory, called category theory, provides the relevant structures for capturing maps, their compositions, and maps between compositions. We employ this theory to propose an algorithm agnostic, model-based representation for domains, problems, and plans expressed in the commonly used planning description language, PDDL. This category theoretic representation is accompanied by a graphical syntax in addition to a linear notation, similar to algebraic expressions, that can be used to infer literals used at every step of the plan. This provides the appropriate constructive abstraction and facilitates comprehension for human operators. In this paper, we demonstrate this on a plan within the Blocksworld domain.