MCTS-minimax hybrids with state evaluations

Monte-Carlo Tree Search (MCTS) has been found to show weaker play than minimax-based search in some tactical game domains. In order to combine the tactical strength of minimax and the strategic strength of MCTS, MCTS-minimax hybrids have been proposed in prior work. This arti

Monte Carlo Tree Search as an intelligent search tool in structural design problems

Monte Carlo Tree Search (MCTS) is a search technique that in the last decade emerged as a major breakthrough for Artificial Intelligence applications regarding board- and video-games. In 2016, AlphaGo, an MCTS-based software agent, outperformed the human world champion of the board game Go. This game was for long considered almost infeasible for machines, due to its immense search space and the need for a long-term strategy. Since this historical success, MCTS is considered as an effective new approach for many other scientific and technical problems. Interestingly, civil structural engineering, as a discipline, offers many tasks whose solution may benefit from intelligent search and in particular from adopting MCTS as a search tool. In this work, we show how MCTS can be adapted to search for suitable solutions of a structural engineering design problem. The problem consists of choosing the load-bearing elements in a reference reinforced concrete structure, so to achieve a set of specific dynamic characteristics. In the paper, we report the results obtained by applying both a plain and a hybrid version of single-agent MCTS. The hybrid approach consists of an integration of both MCTS and classic Genetic Algorithm (GA), the latter also serving as a term of comparison for the results. The study's outcomes may open new perspectives for the adoption of MCTS as a design tool for civil engineers

PDS-PN: A New Proof-Number Search Algorithm: Application to Lines of Action

The paper introduces a new proof-number (pn) search algorithm, called pds-pn. It is a two-level search, which performs at the first level a depth-first proof-number and disproof-number search (pds), and at the second level a best-first pn search. First, we thoroughly investigate four established algorithms in the domain of lines of action endgame positions: pn, pn2, pds and aß search. It turns out that pn2 and pds are best in solving hard problems when measured by the number of solutions and the solution time. However, each of those two has a practical disadvantage: pn2 is restricted by the working memory, and pds is relatively slow in searching. Then we formulate our new algorithm by selectively using the power of each one: the two-level nature and the depth-first traversal, respectively. Experiments reveal that pds-pn is competitive with pds in terms of speed and with pn2 since it is not restricted in working memory