Hardness measures for gridworld benchmarks and performance analysis of real-time heuristic search algorithms

Gridworlds are one of the most popular settings used in benchmark problems for real-time heuristic search algorithms. However, no comprehensive studies have existed so far on how the difference in the density of randomly positioned obstacles affects the hardness of the problems. This paper presents two measures for characterizing the hardness of gridworld problems parameterized by obstacle ratio, and relates them to the performance of the algorithms. We empirically show that the peak locations of those measures and actual performance degradation of the basic algorithms (RTA* and LRTA*) almost coincide with each other for a wide variety of problem settings. Thus the measures uncover some interesting aspects of the gridworlds

Hardness measures for gridworld benchmarks and performance analysis of real-time heuristic search algorithms

Gridworlds are one of the most popular settings used in benchmark problems for real-time heuristic search algorithms. However, no comprehensive studies have existed so far on how the difference in the density of randomly positioned obstacles affects the hardness of the problems. This paper presents two measures for characterizing the hardness of gridworld problems parameterized by obstacle ratio, and relates them to the performance of the algorithms. We empirically show that the peak locations of those measures and actual performance degradation of the basic algorithms (RTA* and LRTA*) almost coincide with each other for a wide variety of problem settings. Thus the measures uncover some interesting aspects of the gridworlds

Automated planning for pathfinding in real-time strategy games

This thesis is focused on the design of a new path planning algorithm to solve path planning problems in dynamic, partially observable and real-time environments such as Real-Time Strategy(RTS) games. The emphasis is put on fast action selection motivating the use of Monte-Carlo planning techniques. Three main contributions are presented in this thesis. The first contribution is a Monte-Carlo planning technique, called MCRT, that performs selective action sampling and limits how many times a particular state-action pair is explored to balance the trade-off between exploration of new actions and exploitation of the current best action. The thesis also presents two variations of MCRT as the second contribution. The first variation of MCRT randomly selects an action as a sample at each state seen during the look-ahead search. The second variation, called MCRT-CAS, performs the selective action sampling using corridors. The third contribution is the design of four real-time path planners that exploit MCRT and its variations to solve path planning problems in real-time. Three of these planners are empirically evaluated using four standard pathfinding benchmarks (and over 1000 instances). Performance of these three planners is compared against two recent rival algorithms (Real-time D*-Lite (RTD) and Local Search Space-Learning Real-Time A* (LSS-LRTA)). These rival algorithms are based on real-time heuristic search. The results show that a variation of MOCART, called MOCART-CAS, performs action selection significantly faster than the rival planners. The fourth planner, called the MG-MOCART planner, is evaluated using a typical Real-Time Strategy game. The MG-MOCART planner can solve the path planning problems with multiple goals. This planner is compared against four rivals: Upper Confidence bounds applied to Trees (UCT), LSS-LRTA, Real-Time Dynamic Programming (RTDP) and a rapidly-exploring random tree (RRT) planner. The performance is measured using score and planning cost. The results show that the MG-MOCART planner performs better than its rival techniques with respect to score and planning cost.EThOS - Electronic Theses Online ServiceGBUnited Kingdo