RTAA* is probably the best-performing real-time heuristic search algorithm at path-finding tasks in which the environ- ment is not known in advance or in which the environment is known and there is no time for pre-processing. As most real- time search algorithms do, RTAA performs poorly in presence of heuristic depressions, which are bounded areas of the search space in which the heuristic is too low with respect to their border. Recently, it has been shown that LSS-LRTA, a well-known real-time search algorithm, can be improved when search is actively guided away of depressions. In this paper we investigate whether or not RTAA can be improved in the same manner. We propose aRTAA and daRTAA, two algorithms based on RTAA that avoid heuristic depressions. Both algorithms outperform RTAA on standard path-finding tasks, obtaining better-quality solutions when the same time deadline is imposed on the duration of the planning episode.

Heuristics used for solving hard real-time search problems have regions with depressions. Such regions are bounded areas of the search space in which the heuristic function is inaccurate compared to the actual cost to reach a solution. Early real-time search algorithms, like LRTA*, easily become trapped in those regions since the heuristic values of their states may need to be updated multiple times, which results in costly solutions. State-of-the-art real-time search algorithms, like LSS-LRTA* or LRTA*(k), improve LRTA*'s mechanism to update the heuristic, resulting in improved performance. Those algorithms, however, do not guide search towards avoiding depressed regions. This paper presents depression avoidance, a simple real-time search principle to guide search towards avoiding states that have been marked as part of a heuristic depression. We propose two ways in which depression avoidance can be implemented: mark-and-avoid and move-to-border. We implement these strategies on top of LSS-LRTA* and RTAA*, producing 4 new real-time heuristic search algorithms: aLSS-LRTA*, daLSS-LRTA*, aRTAA*, and daRTAA*. When the objective is to find a single solution by running the real-time search algorithm once, we show that daLSS-LRTA* and daRTAA* outperform their predecessors sometimes by one order of magnitude. Of the four new algorithms, daRTAA* produces the best solutions given a fixed deadline on the average time allowed per planning episode. We prove all our algorithms have good theoretical properties: in finite search spaces, they find a solution if one exists, and converge to an optimal after a number of trials.

RTAA* is probably the best-performing real-time heuristic search algorithm at path-finding tasks in which the environ- ment is not known in advance or in which the environment is known and there is no time for pre-processing. As most real- time search algorithms do, RTAA∗ performs poorly in presence of heuristic depressions, which are bounded areas of the search space in which the heuristic is too low with respect to their border. Recently, it has been shown that LSS-LRTA∗, a well-known real-time search algorithm, can be improved when search is actively guided away of depressions. In this paper we investigate whether or not RTAA∗ can be improved in the same manner. We propose aRTAA∗ and daRTAA∗, two algorithms based on RTAA∗ that avoid heuristic depressions. Both algorithms outperform RTAA∗ on standard path-finding tasks, obtaining better-quality solutions when the same time deadline is imposed on the duration of the planning episode. We prove, in addition, that both algorithms have good theoretical properties

Real-time agent-centric algorithms have been used for learning and solving problems since the introduction of the LRTA* algorithm in 1990. In this time period, numerous variants have been produced, however, they have generally followed the same approach in varying parameters to learn a heuristic which estimates the remaining cost to arrive at a goal state. Recently, a different approach, RIBS, was suggested which, instead of learning costs to the goal, learns costs from the start state. RIBS can solve some problems faster, but in other problems has poor performance. We present a new algorithm, f-cost Learning Real-Time A* (f-LRTA*), which combines both approaches, simultaneously learning distances from the start and heuristics to the goal. An empirical evaluation demonstrates that f-LRTA* outperforms both RIBS and LRTA*-style approaches in a range of scenarios.

Heuristics used for solving hard real-time search problems have regions with depressions. Such regions are bounded areas of the search space in which the heuristic function is exceedingly low compared to the actual cost to reach a solution. Real-time search algorithms easily become trapped in those regions since the heuristic values of states in them may need to be updated multiple times, which results in costly solutions. State-of-the-art real-time search algorithms like LSS-LRTA*, LRTA*(k), etc., improve LRTA*'s mechanism to update the heuristic, resulting in improved performance. Those algorithms, however, do not guide search towards avoiding or escaping depressed regions. This paper presents depression avoidance, a simple real-time search principle to guide search towards avoiding states that have been marked as part of a heuristic depression. We apply the principle to LSS-LRTA* producing aLSS-LRTA*, a new real-time search algorithm whose search is guided towards exiting regions with heuristic depressions. We show our algorithm outperforms LSS-LRTA* in standard real-time benchmarks. In addition we prove aLSS-LRTA* has most of the good theoretical properties of LSS-LRTA*.

Real-time search is a well known approach to solving search problems under tight time constraints. Recently, it has been shown that LSS-LRTA∗ , a well-known real-time search algorithm, can be improved when search is actively guided away of depressions. In this paper we investigate whether or not RTAA∗ can be improved in the same manner. We propose aRTAA∗ and daRTAA∗ , two algorithms based on RTAA∗ that avoid heuristic depressions. Both algorithms outperform RTAA∗ on standard path-finding tasks, obtaining better-quality solutions when the same time deadline is imposed on the duration of the planning episode. We prove, in addition, that both algorithms have good theoretical properties.

Real-time heuristic search is a standard approach to pathfind- ing when agents are required to make decisions in a bounded, very short period of time. An assumption usually made in the development and evaluation of real-time algorithms is that the environment is unknown. Nevertheless, in many interesting applications such as pathfinding for automnomous characters in video games, the environment is known in advance. Recent real-time search algorithms such as D LRTA* and kNN LRTA* exploit knowledge about the environment while pathfinding under real-time constraints. Key to those algorithms is the computation of subgoals in a preprocessing step. Subgoals are subsequently used in the online planning phase to obtain high-quality solutions. Preprocessing in those algorithms, however, requires significant computation. In this paper we propose a novel preprocessing algorithm that generates subgoals using a series of backward search episodes carried out from potential goals. The result of a single backward search episode is a tree of subgoals that we then use while planning online. We show the advantages of our approach over state-of-the-art algorithms by carrying out experiments on standard real-time search benchmarks.