Evolving Real-Time Heuristics Search Algorithms with Building Blocks
This work is incremental, addressing performance variability in real-time heuristic search for applications like robotics or game AI.
The paper tackles the problem of improving real-time heuristic search algorithms by extending an evolutionary framework with new building blocks, including a published method and a novel strategy, and finds that deeper lookahead reduces suboptimality and scrubbing complexity in the best-evolved algorithms.
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algorithm, namely lookahead based A-star shaped local search space generation method from LSSLRTA-star, plus an unpublished novel strategy to generate local search space with Greedy Best First Search.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.