SSAKG 2.0: An Open-Source Package for Structural Associative Sequence Memory and Context-Based Retrieval
本文介绍SSAKG 2.0，通过优化算法和混合编程实现高效存储与基于上下文的序列检索，适用于多种数据类型。
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A Cellular Automata Approach to Donation Game
This study investigates the evolutionary mechanisms of cooperation in multi-agent systems under local neighborhood interactions, focusing on the effects of environmental noise and agent strategies (e.g., reputation, generosity, tolerance). We propose a donation game modeling framework based on one-dimensional binary cellular automata, incorporating novel components: perception/action noise models, strategy mutation matrices, and agent mobility mechanisms. Our experiments demonstrate that spatial proximity significantly enhances both the emergence and long-term stability of cooperation—outperforming fully connected random interaction topologies. Moderate levels of noise improve cooperative robustness, while strategic diversity synergizes with local network structure to drive cooperative evolution. This work establishes a computationally tractable paradigm for understanding the origins of cooperation under realistic constraints—namely, limited observation capabilities and bounded interaction ranges—thereby advancing theoretical and empirical research on decentralized cooperative dynamics.
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SSAKG 2.0: An Open-Source Package for Structural Associative Sequence Memory and Context-Based Retrieval
本文介绍SSAKG 2.0，通过优化算法和混合编程实现高效存储与基于上下文的序列检索，适用于多种数据类型。
0 citationsRead paper
A Cellular Automata Approach to Donation Game
This study investigates the evolutionary mechanisms of cooperation in multi-agent systems under local neighborhood interactions, focusing on the effects of environmental noise and agent strategies (e.g., reputation, generosity, tolerance). We propose a donation game modeling framework based on one-dimensional binary cellular automata, incorporating novel components: perception/action noise models, strategy mutation matrices, and agent mobility mechanisms. Our experiments demonstrate that spatial proximity significantly enhances both the emergence and long-term stability of cooperation—outperforming fully connected random interaction topologies. Moderate levels of noise improve cooperative robustness, while strategic diversity synergizes with local network structure to drive cooperative evolution. This work establishes a computationally tractable paradigm for understanding the origins of cooperation under realistic constraints—namely, limited observation capabilities and bounded interaction ranges—thereby advancing theoretical and empirical research on decentralized cooperative dynamics.
0 citationsRead paper