The research area of real-time heuristics search has produced quite many algorithms. In the landscape of real-time heuristics search research, it is not rare to find that an algorithm X that appears to perform better than algorithm Y on a group of problems, performed worse than Y for another group of problems. If these published algorithms are combined to generate a more powerful space of algorithms, then that novel space of algorithms may solve a distribution of problems more efficiently. Based on this intuition, a recent work Bulitko 2016 has defined the task of finding a combination of heuristics search algorithms as a survival task. In this evolutionary approach, a space of algorithms is defined over a set of building blocks published algorithms and a simulated evolution is used to recombine these building blocks to find out the best algorithm from that space of algorithms. In this paper, we extend the set of building blocks by adding one published algorithm, namely lookahead based A-star shaped local search space generation method from LSSLRTA-star, plus an unpublished novel strategy to generate local search space with Greedy Best First Search. Then we perform experiments in the new space of algorithms, which show that the best algorithms selected by the evolutionary process have the following property: the deeper is the lookahead depth of an algorithm, the lower is its suboptimality and scrubbing complexity.

Real-time heuristic search algorithms follow the agent-centered search paradigm wherein the agent has access only to information local to the agent’s current position in the environment. This allows agents with constant-bounded computational faculties (e.g., memory) to take on search problems of progressively increasing sizes. As the agent’s memory does not scale with the size of the search problem, the heuristic must necessarily be stored externally, in the environment. Storing the heuristic in the environment brings the extra challenge of read/write errors. In video games, introducing error artificially to the heuristics can make the non-player characters (NPC) behave more naturally. In this paper, we evaluate effects of such errors on real-time heuristic search algorithms. In particular, we empirically study the effects of heuristic read redundancy on algorithm performance and compare its effects to the existing technique of using weights in heuristic learning. Finally, we evaluate a recently proposed technique of correcting the heuristic with a one-step error term in the presence of read/write error.

Heuristic search is a core area of Artificial Intelligence with applications to planning, scheduling and game playing. Real-time heuristic search applies to search problems where plan execution needs to start before a complete solution can be computed. Since the inception of real-time heuristic search in the early 1990s a great number of algorithms have been proposed and evaluated. In this paper we break them down into building blocks and conduct a search in the space of such building blocks. Even simple tabulated and iterative searches find new real-time heuristic search algorithms outperforming manually crafted contemporary algorithms.

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