Abstract Cognitive architectures (CA) are an IA approach to implement computer systems with human-like behavior. Fundamental exhibited human capabilities include planning and decision-making. In that regard, numerous AI systems successfully exhibit human-like behavior but are limited to either achieving specific objectives or are restrained to too heavily constrained environments, which makes them unsuitable in the presence of unforeseen situations where autonomy is required. To try to alleviate the problem, we present a bio-inspired computational model to solve the autonomous navigation problem of a computational entity in a controlled context. This proposal is the result of the interaction between planning and decision-making, spatial attention and the motor cognitive functions. The proposed model is based on neuroscientific evidence concerning the involved cognitive functions and is part of a more general cognitive architecture. In the case study developed to validate our idea, we can see that the processes previously identified play an important role to accomplish spatial navigation. In the case study presented, an agent achieves the navigation over an unexplored maze from an initial to a final position successfully. The reunited results motivate us to continue improving our model considering attentional information to influence the agent’s motor behavior.

中文翻译：

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考虑决策和规划、空间注意力和基本运动命令过程的仿生行为模型

摘要 认知架构 (CA) 是一种实现具有类人行为的计算机系统的 IA 方法。基本的人类能力包括计划和决策。在这方面，许多人工智能系统成功地表现出类似人类的行为，但仅限于实现特定目标或受限于过于严格的环境，这使得它们在需要自主的不可预见情况下不适合。为了试图缓解这个问题，我们提出了一个仿生计算模型来解决受控上下文中计算实体的自主导航问题。该提议是规划和决策、空间注意力和运动认知功能之间相互作用的结果。所提出的模型基于有关所涉及的认知功能的神经科学证据，并且是更一般的认知架构的一部分。在为验证我们的想法而开发的案例研究中，我们可以看到先前确定的过程在完成空间导航方面发挥着重要作用。在介绍的案例研究中，代理成功地实现了从初始位置到最终位置的未探索迷宫的导航。重新统一的结果激励我们继续改进我们的模型，考虑注意信息以影响代理的运动行为。在介绍的案例研究中，代理成功地实现了从初始位置到最终位置的未探索迷宫的导航。重新统一的结果激励我们继续改进我们的模型，考虑注意信息以影响代理的运动行为。在介绍的案例研究中，代理成功地实现了从初始位置到最终位置的未探索迷宫的导航。重新统一的结果激励我们继续改进我们的模型，考虑注意信息以影响代理的运动行为。