Cognitive agents are typically utilized in autonomous systems for automated decision making. With the widespread use of autonomous systems in complex environments, the need for real-time cognitive agents is essential. Cognitive agents are more capable when they are able to process larger amounts of information to make more informed and intelligent decisions. The solution search space for cognitive agents increases exponentially with large volumes of varied data. In this paper, we present the parallelization of the knowledge-mining component of a cognitive agent that can be programmed to reason like humans. This study examined a novel high-performance path-based forward checking algorithm on 128 compute nodes at the Ohio Supercomputing Center (768 cores) to achieve a speedup of over 200 times compared to a serial implementation of our algorithm. The serial implementation is around 10–25 times faster than a conventional Java-based constraint solver at generating the first solution.

中文翻译：

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自主认知代理中约束满足的并行路径搜索

认知代理通常在自主系统中用于自动决策。随着自治系统在复杂环境中的广泛使用，对实时认知代理的需求至关重要。当认知代理能够处理大量信息以做出更明智和更明智的决策时，他们就更有能力。认知代理的解决方案搜索空间随着大量不同的数据呈指数增长。在本文中，我们介绍了认知代理的知识挖掘组件的并行化，该组件可以编程为像人类一样推理。本研究在俄亥俄州超级计算中心（768 个内核）的 128 个计算节点上检验了一种新型的高性能基于路径的前向检查算法，与我们算法的串行实现相比，其速度提高了 200 多倍。