Motion-planning problems, such as manipulation in cluttered environments, often require a collision-free shortest path to be computed quickly given a roadmap graph. Typically, the computational cost of evaluating whether an edge of the roadmap graph is collision-free dominates the running time of search algorithms. Algorithms such as Lazy Weighted A* (LWA*) and LazySP have been proposed to reduce the number of edge evaluations by employing a lazy lookahead (one-step lookahead and infinite-step lookahead, respectively). However, this comes at the expense of additional graph operations: the larger the lookahead, the more the graph operations that are typically required. We propose Lazy Receding-Horizon A* (LRA*) to minimize the total planning time by balancing edge evaluations and graph operations. Endowed with a lazy lookahead, LRA* represents a family of lazy shortest-path graph-search algorithms that generalizes LWA* and LazySP. We analyze the theoretic properties of LRA* and demonstrate empirically that, in many cases, to minimize the total planning time, the algorithm requires an intermediate lazy lookahead. Namely, using an intermediate lazy lookahead, our algorithm outperforms both LWA* and LazySP. These experiments span simulated random worlds in R^2 and R^4, and manipulation problems using a 7-DOF manipulator.

Planning the motion for humanoid robots is a computationally-complex task due to the high dimensionality of the system. Thus, a common approach is to first plan in the low-dimensional space induced by the robot’s feet—a task referred to as footstep planning. This low-dimensional plan is then used to guide the full motion of the robot. One approach that has proven successful in footstep planning is using search-based planners such as A* and its many variants. To do so, these search-based planners have to be endowed with effective heuristics to efficiently guide them through the search space. However, designing effective heuristics is a time-consuming task that requires the user to have good domain knowledge. Thus, our goal is to be able to effectively plan the footstep motions taken by a humanoid robot while obviating the burden on the user to carefully design local-minima free heuristics. To this end, we propose to use user-defined homotopy classes in the workspace that are intuitive to define. These homotopy classes are used to automatically generate heuristic functions that efficiently guide the footstep planner. We compare our approach for footstep planning with a standard approach that uses a heuristic common to footstep planning. In simple scenarios, the performance of both algorithms is comparable. However, in more complex scenarios our approach allows for a speedup in planning of several orders of magnitude when compared to the standard approach.

Observation planning for Unmanned Aerial Vehicles (UAVs) is a challenging task as it requires planning trajectories over a large continuous space and with motion models that can not be directly encoded into current planners. Furthermore, realistic problems often require complex objective functions that complicate problem decomposition. In this paper, we propose a local search approach to plan the trajectories of a fleet of UAVs on an observation mission. The strength of the approach lies in its loose coupling with domain specific requirements such as the UAV model or the objective function that are both used as black boxes. Furthermore, the Variable Neighborhood Search (VNS) procedure considered facilitates the adaptation of the algorithm to specific requirements through the addition of new neighborhoods. We demonstrate the feasibility and convenience of the method on a large joint observation task in which a fleet of fixed-wing UAVs maps wildfires over areas of a hundred square kilometers. The approach allows generating plans over tens of minutes for a handful of UAVs in matter of seconds, even when considering very short primitive maneuvers.

This paper presents two prototypical epistemic forward planners, called EFP and PG-EFP, for generating plans in multi-agent environments. These planners differ from recently developed epistemic planners in that they can deal with unlimited nested beliefs, common knowledge, and capable of generating plans with both knowledge and belief goals. EFP is simply a breadth first search planner while PG-EFP is a heuristic search based system. To generate heuristics in PG-EFP, the paper introduces the notion of an epistemic planning graph. The paper includes an evaluation of the planners using benchmarks collected from the literature and discusses the issues that affect their scalability and efficiency, thus identifies potentially directions for future work. It also includes experimental evaluation that proves the usefulness of epistemic planning graphs.

In this paper, we present a novel model for entity disambiguation that combines both local contextual information and global evidences through Limited Discrepancy Search (LDS). Given an input document, we start from a complete solution constructed by a local model and conduct a search in the space of possible corrections to improve the local solution from a global view point. Our search utilizes a heuristic function to focus more on the least confident local decisions and a pruning function to score the global solutions based on their local fitness and the global coherences among the predicted entities. Experimental results on CoNLL 2003 and T AC 2010 benchmarks verify the effectiveness of our model.

Pathfinding is a very popular area in computer game development. While two-dimensional (2D) pathfinding is widely applied in most of the popular game engines, little implementation of real three-dimensional (3D) pathfinding can be found. This research presents a dynamic search space optimization algorithm which can be applied to tessellate 3D search space unevenly, significantly reducing the total number of resulting nodes. The algorithm can be used with popular pathfinding algorithms in 3D game engines. Furthermore, a simplified standalone 3D pathfinding algorithm is proposed in this paper. The proposed algorithm relies on ray-casting or line vision to generate a feasible path during runtime without requiring division of the search space into a 3D grid. Both of the proposed algorithms are simulated on Unreal Engine to show innerworkings and resultant path comparison with A*. The advantages and shortcomings of the proposed algorithms are also discussed along with future directions.

Incredibly, the system learned to dominate the classic 3D puzzle in just 44 hours and without any human intervention. "A generally intelligent agent must be able to teach itself how to solve problems in complex domains with minimal human supervision," write the authors of the new paper, published online at the arXiv preprint server. Indeed, if we're ever going to achieve a general, human-like machine intelligence, we'll have to develop systems that can learn and then apply those learnings to real-world applications. Recent breakthroughs in machine learning have produced systems that, without any prior knowledge, have learned to master games like chess and Go. But these approaches haven't translated very well to the Rubik's Cube.

OK, let's break this down. The Rubik's Cube is pretty difficult, right? But you'd imagine it might be pretty easy for an artificial intelligence to break down and solve consistently, right? Creating an algorithm that can solve the Rubik's Cube is relatively simple -- the kind of algorithms that allow AI to beat humans at chess or Go or even DOTA 2! But creating a machine that can solve the Rubik's Cube without algorithms hand-crafted by human beings? Stephen McAleer and his colleagues at the University of California think they have solved the problem, with a process called "autodidactic iteration". Autodidactic iteration: McAleer and his team call it a "novel reinforcement learning algorithm that is able to teach itself how to solve the Rubik's Cube with no human assistance."

Deep-learning machines have figured out how to master games like chess or Mortal Kombat. Now, computer scientists at the University of California, Irvine taken things to the third dimension by creating an algorithm that can figure out how to solve a Rubik's Cube, a surprisingly difficult change. "Our algorithm is able to solve 100 percent of randomly scrambled cubes while achieving a median solve length of 30 moves - less than or equal to solvers that employ human domain knowledge," say the scientists in the abstract to their paper, up on Arvix. The algorithm, called DeepCube, uses what's known as "autodidactic iteration," a form of machine learning developed by the authors of the paper. The big challenge of autodidactic iteration was to allow machines to find their own rewards in solving a puzzle, a goal they can reach.

The Rubik's Cube is a three-dimensional puzzle developed in 1974 by the Hungarian inventor Erno Rubik, the object being to align all squares of the same color on the same face of the cube. It became an international best-selling toy and sold over 350 million units. The puzzle has also attracted considerable interest from computer scientists and mathematicians. One question that has intrigued them is the smallest number of moves needed to solve it from any position. The answer, proved in 2014, turns out to be 26.