412 research outputs found
Symmetry-Based Search Space Reduction For Grid Maps
In this paper we explore a symmetry-based search space reduction technique
which can speed up optimal pathfinding on undirected uniform-cost grid maps by
up to 38 times. Our technique decomposes grid maps into a set of empty
rectangles, removing from each rectangle all interior nodes and possibly some
from along the perimeter. We then add a series of macro-edges between selected
pairs of remaining perimeter nodes to facilitate provably optimal traversal
through each rectangle. We also develop a novel online pruning technique to
further speed up search. Our algorithm is fast, memory efficient and retains
the same optimality and completeness guarantees as searching on an unmodified
grid map
Pathfinding in Games
Commercial games can be an excellent testbed to artificial intelligence (AI) research, being a middle ground between synthetic, highly abstracted academic benchmarks, and more intricate problems from real life. Among the many AI techniques and problems relevant to games, such as learning, planning, and natural language processing, pathfinding stands out as one of the most common applications of AI research to games. In this document we survey recent work in pathfinding in games. Then we identify some challenges and potential directions for future work. This chapter summarizes the discussions held in the pathfinding workgroup
Improving Lookahead search for grid-based pathfinding
Pathfinding is an essential part of navigation systems, often used in video games, route planning and robotic navigation. A* search has been one of the most well-known and frequently used algorithms for pathfinding. A* uses an open list and a closed list to keep track of all nodes generated and expanded. The size and performance of these data structures are major drawbacks of A*. Lookahead is used to investigate future outcomes and improve the quality of available choices. Lookaheads are done on a DFS manner from the frontier of A* search. This combination of A* and DFS lookahead has been shown to save space when working with puzzles. We leverage this concept with grid-based pathfinding in video games to save the amount of space consumed. However, because grids contain redundant paths, the DFS lookaheads end up being an overhead as they do not maintain a list of nodes visited or expanded. By using a domain-specific pruning technique, we significantly improve the time taken by the algorithm and further improve upon the space consumed. A combination of lookahead and A* search with this pruning technique is, therefore, able to achieve improvement in both space-consumed and time-taken over the standard A* search algorithm for grid-based pathfinding
QED driven QAOA for network-flow optimization
We present a general framework for modifying quantum approximate optimization
algorithms (QAOA) to solve constrained network flow problems. By exploiting an
analogy between flow constraints and Gauss's law for electromagnetism, we
design lattice quantum electrodynamics (QED) inspired mixing Hamiltonians that
preserve flow constraints throughout the QAOA process. This results in an
exponential reduction in the size of the configuration space that needs to be
explored, which we show through numerical simulations, yields higher quality
approximate solutions compared to the original QAOA routine. We outline a
specific implementation for edge-disjoint path (EDP) problems related to
traffic congestion minimization, numerically analyze the effect of initial
state choice, and explore trade-offs between circuit complexity and qubit
resources via a particle-vortex duality mapping. Comparing the effect of
initial states reveals that starting with an ergodic (unbiased) superposition
of solutions yields better performance than beginning with the mixer
ground-state, suggesting a departure from the "short-cut to adiabaticity"
mechanism often used to motivate QAOA.Comment: 14 pages, 10 figure
Risk-aware navigation for UAV digital data collection
This thesis studies the navigation task for autonomous UAVs to collect digital data in a risky environment. Three problem formulations are proposed according to different real-world situations. First, we focus on uniform probabilistic risk and assume UAV has unlimited amount of energy. With these assumptions, we provide the graph-based Data-collecting Robot Problem (DRP) model, and propose heuristic planning solutions that consist of a clustering step and a tour building step. Experiments show our methods provide high-quality solutions with high expected reward. Second, we investigate non-uniform probabilistic risk and limited energy capacity of UAV. We present the Data-collection Problem (DCP) to model the task. DCP is a grid-based Markov decision process, and we utilize reinforcement learning with a deep Ensemble Navigation Network (ENN) to tackle the problem. Given four simple navigation algorithms and some additional heuristic information, ENN is able to find improved solutions. Finally, we consider the risk in the form of an opponent and limited energy capacity of UAV, for which we resort to the Data-collection Game (DCG) model. DCG is a grid-based two-player stochastic game where the opponent may have different strategies. We propose opponent modeling to improve data-collection efficiency, design four deep neural networks that model the opponent\u27s behavior at different levels, and empirically prove that explicit opponent modeling with a dedicated network provides superior performance
Quantum walks: a comprehensive review
Quantum walks, the quantum mechanical counterpart of classical random walks,
is an advanced tool for building quantum algorithms that has been recently
shown to constitute a universal model of quantum computation. Quantum walks is
now a solid field of research of quantum computation full of exciting open
problems for physicists, computer scientists, mathematicians and engineers.
In this paper we review theoretical advances on the foundations of both
discrete- and continuous-time quantum walks, together with the role that
randomness plays in quantum walks, the connections between the mathematical
models of coined discrete quantum walks and continuous quantum walks, the
quantumness of quantum walks, a summary of papers published on discrete quantum
walks and entanglement as well as a succinct review of experimental proposals
and realizations of discrete-time quantum walks. Furthermore, we have reviewed
several algorithms based on both discrete- and continuous-time quantum walks as
well as a most important result: the computational universality of both
continuous- and discrete- time quantum walks.Comment: Paper accepted for publication in Quantum Information Processing
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