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The study considers a virtual agent-based game environment “Stones”, designed to model and analyze interactions between agents of various types, including real people. The basic concept is very simple. The game features a game board with stones and agents that can move among them. A stone requires a specific number of agents to be removed; too few agents lack strength, while too many create confusion. The game continues until all stones are removed. On the other hand, it presents a wide scope for its complication through various modifications like movement modes, communication types, and win conditions. Therefore, it can be used as a benchmark to investigate the effectiveness of different agent architectures and training approaches, as well as in studies of the psychology of human behavior and interaction of humans with virtual agents. This study considers the simplest version of the gameplay with synchronous movement mode, no distance considerations, equivalence of all stones, no agent signals, and a cooperative game objective. Testing was conducted for algorithmic agents and emotional biologically inspired cognitive architecture (eBICA) agents. For eBICA agents, optimization of parameters was also performed using genetic algorithms.

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Evolution of eBICA Agent Learning in “Stones” Virtual Environment

  • 会议论文

  • 发表日期：

    2026年

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  • Igor Isaev,
  • Kirill Chernov,
  • Artem Guskov,
  • Gavriil Kupriyanov,
  • Alexander Makarov,
  • Anastasiia Mushchina,
  • Alexei Samsonovich,
  • Sergey Dolenko

摘要

The study considers a virtual agent-based game environment “Stones”, designed to model and analyze interactions between agents of various types, including real people. The basic concept is very simple. The game features a game board with stones and agents that can move among them. A stone requires a specific number of agents to be removed; too few agents lack strength, while too many create confusion. The game continues until all stones are removed. On the other hand, it presents a wide scope for its complication through various modifications like movement modes, communication types, and win conditions. Therefore, it can be used as a benchmark to investigate the effectiveness of different agent architectures and training approaches, as well as in studies of the psychology of human behavior and interaction of humans with virtual agents. This study considers the simplest version of the gameplay with synchronous movement mode, no distance considerations, equivalence of all stones, no agent signals, and a cooperative game objective. Testing was conducted for algorithmic agents and emotional biologically inspired cognitive architecture (eBICA) agents. For eBICA agents, optimization of parameters was also performed using genetic algorithms.