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ADABA: improving the balancing between runtime and accuracy in a new distributed version of the alpha–beta algorithm

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Abstract

The satisfactory performance of the intelligent agents conceived to solve several real-life problems requires efficient decision-making algorithms. To address issues with high state-spaces within reasonable runtime limits, these algorithms are distributed according to some approaches that can be either synchronous or asynchronous, where the former guarantee the same results as their corresponding serial versions through synchronization points, which causes the undesirable effects of communication overhead and idle processors. To mitigate this, the asynchronous approaches reduce the message exchanges in such a way as to accelerate the runtime without too much compromising the response accuracy. The challenge of enhancing the minimax technique through pruning makes Alpha–Beta a relevant case study in parallelism research. Young Brothers Wait Concept (YBWC) and Asynchronous Parallel Hierarchical Iterative Deepening (APHID) are highlighted among the existing Alpha–Beta distributions. Knowing that APHID proved to be more suitable than YBWC to operate in distributed memory and that shared memory architectures are scarcely available due to their high costs, the primary motivation here is to implement the Asynchronous Distributed Alpha–Beta Algorithm (ADABA), which increases the accuracy and performance of APHID through the enhancement of the slaves’ task ordering policies, the communication process between the processors and the window’s updating strategy. Experiments fulfilled through tournaments involving ADABA-based and APHID-based Checkers agents proved that the player based on the best ADABA version reached, approximately, a victory rate 95% superior and a runtime two times faster than the APHID-based player, keeping the same response accuracy level of its opponent.

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Data availability

The dataset used in the experiments of this work is available in the public Git Repository, which link is: https://github.com/ldbononi/adaba.

Code availability

The source code is available in the public Git Repository, which link is: https://github.com/ldbononi/adaba.

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Not applicable.

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Authors and Affiliations

  1. Federal Institute of Triângulo Mineiro, Uberaba, Brazil

    Lidia Bononi Paiva Tomaz

  2. Federal University of Uberlândia, Uberlândia, Brazil

    Rita Maria Silva Julia & Matheus Prado Prandini Faria

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  1. Lidia Bononi Paiva Tomaz
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  2. Rita Maria Silva Julia
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  3. Matheus Prado Prandini Faria
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Correspondence to Lidia Bononi Paiva Tomaz.

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The authors have non-financial interests. In this sense, author Lídia B. P. Tomaz declares to be a Professor at the Federal Institute of Triangulo Mineiro, Brazil. Author Rita M. S. Julia declares to be a Full Professor at the Federal University of Uberlândia, Brazil. Finally, author Matheus P. P. Faria is a PhD Student at Federal University of Uberlândia, Brazil. Thus, such work contributes to improving the research in the domain of Artificial Intelligence in both institutions at which the authors develop their academic activities.

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Tomaz, L.B.P., Julia, R.M.S. & Faria, M.P.P. ADABA: improving the balancing between runtime and accuracy in a new distributed version of the alpha–beta algorithm. Artif Intell Rev 56, 4255–4293 (2023). https://doi.org/10.1007/s10462-022-10269-3

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  • DOI: https://doi.org/10.1007/s10462-022-10269-3

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