In this paper we solve fundamental graph optimization problems like Maximum Clique and Minimum Coloring with recent advances of Monte-Carlo Search. The optimization problems are implemented as single-agent games in a generic state-space search framework, roughly comparable to what is encoded in PDDL for an action planner.

We propose an approach to solving Markov Decision Processes with non-Markovian rewards specified in Linear Temporal Logic interpreted over finite traces (LTL-f). Our approach integrates automata representations of LTL-f formulae into compiled MDPs that can be solved by off-the-shelf MDP planners, exploiting reward shaping to help guide search. Experiments with state-of-the-art UCT-based MDP planner PROST show automata-based reward shaping to be an effective method to guide search, producing solutions of superior quality, while maintaining policy optimality guarantees.

We adapt the relaxation heuristics h max , h add and h FF to interval based numeric relaxation frameworks, combining them with two different relaxation techniques and with two different search techniques. In contrast to previous approaches, the heuristics presented here are not limited to a subset of numeric planning and support action costs.

Any-angle path finding on grids is an important problem with applications in autonomous robot navigation. In this paper, we show that a well-known pre-processing technique, namely subgoal graphs, originally proposed for (non any-angle) 8-connected grids, can be straightforwardly adapted to the 2 k neighborhoods, a family of neighborhoods that allow an increasing number of movements (and angles) as k is increased. This observation yields a pathfinder that computes 2 k -optimal paths very quickly. Compared to ANYA, an optimal true any-angle planner, over a variety of benchmarks, our planner is one order of magnitude faster while being less than 0.0005% suboptimal. Important to our planner's performance was the development of an iterative 2 k heuristic, linear in k, which is also a contribution of this paper.

Star-Topology Decoupled Search has recently been introduced in classical planning. It splits the planning task into a set of components whose dependencies take a star structure, where one center component interacts with possibly many leaf components. Here we address a weakness of decoupled search, namely large leaf components, whose state space is enumerated explicitly. We propose a symbolic representation of the leaf state spaces via decision diagrams, which can be dramatically smaller, and also more runtime efficient. We further introduce a symbolic version of the LM-cut heuristic, that nicely connects to our new leaf representation. We show empirically that the symbolic representation indeed pays off when the leaf components are large.

Search using subgoal graphs is a recent preprocessing-based path-planning algorithm that can find shortest paths on 8-neighbor grids several orders of magnitude faster than A*, while requiring little preprocessing time and memory overhead. In this paper, we first generalize the ideas behind subgoal graphs to a framework that can be specialized to different types of environments (represented as weighted directed graphs) through the choice of a reachability relation. Intuitively, a reachability relation identifies pairs of vertices for which a shortest path can be found quickly. A subgoal graph can then be constructed as an overlay graph that is guaranteed to have edges only between vertices that satisfy the reachability relation, which allows one to find shortest paths on the original graph quickly. In the context of this general framework, subgoal graphs on grids use freespace-reachability (originally called h-reachability) as the reachability relation, which holds for pairs of vertices if and only if their distance on the grid with blocked cells is equal to their distance on the grid without blocked cells (freespace assumption). We apply this framework to state lattices by using variants of freespace-reachability as the reachability relation. We provide preliminary results on (x,y,theta)-state lattices, which shows that subgoal graphs can be used to speed up path planning on state lattices as well, although the speed-up is not as significant as it is on grids.

In the Any-Angle Pathfinding problem, the goal is to find the shortest path between a pair of vertices on a uniform square grid, that is not constrained to any fixed number of possible directions over the grid. Visibility Graphs are a known optimal algorithm for solving the problem with the use of pre-processing. However, Visibility Graphs are known to perform poorly in terms of running time, especially on large, complex maps. In this paper, we introduce two improvements over the Visibility Graph Algorithm to compute optimal paths. Sparse Visibility Graphs (SVGs) are constructed by pruning unnecessary edges from the original Visibility Graph. Edge N-Level Sparse Visibility Graphs (ENLSVGs) is a hierarchical SVG built by iteratively pruning non-taut paths. We also introduce Line-of-Sight Scans, a faster algorithm for building Visibility Graphs over a grid. SVGs run much faster than Visibility Graphs by reducing the average vertex degree. ENLSVGs, a hierarchical algorithm, improves this further, especially on larger maps, with millisecond runtimes even on 6000 x 6000 maps. On large maps, with the use of pre-processing, these algorithms are at least an order of magnitude faster than existing algorithms like Visibility Graphs, Anya and Theta*.

Agent planning programs are finite-state programs, possibly containing loops, whose atomic instructions consist of a guard, a maintenance goal, and an achievement goal, which act as precondition-invariance-postcondition assertions in program specification. The execution of such programs requires generating plans that meet the goals specified in the atomic instructions, while respecting the program control flow. Recently, De Giacomo et al. (2016) presented a technique, based on iteratively solving classical planning problems with action costs, for realizing planning programs in deterministic domains. Such a technique works generally well for domains with no or very few dead-end states. In this paper, we propose an enhancement of this technique to handle deterministic domains that have potentially many dead-end states, and we study the effectiveness of our technique through an experimental analysis.

With a changing workforce, the enterprise workspace is also undergoing a distinct change, aided by the HR technology available today. Trends such as mobility, flexibility, and cognitive computing are forcing organizations to rethink the ways of workplace management. Not only are HRMS changing talent management practices, but they are also helping revolutionize organizational designs. New office ergonomics and new policies and practices are sweeping across the modern organization. The boundaries between HR and technology are blurring, and innovative means for workforce management (WFM) are taking to the mainstream.

Aircraft fuel is without a doubt the biggest cost for any airline and often receives widespread attention, especially when airlines hedge their bets the wrong way. Cathay Pacific reported a HK$4.49 billion fuel-hedging loss in the first half of 2016, which has hurt the airline's profitability. The second biggest expense for an airline is human capital, and researchers from Hong Kong Polytechnic University and University of Nottingham Ningbo China Business School may have found a solution to ease some of Cathays financial woes through an unlikely source – Machine Learning and Data Science. The researchers say that a "poorly designed airline crew schedule can result in unreliable flight schedules, significantly jeopardizing airline operations and profitability if insufficient crew members are available or other glitches occur. For that reason, managing airline crew scheduling and costs are one of the most crucial topics for airlines because it yields enormous economic benefits and ranks as the second highest expenditure after fuel costs."