Smart self-propelled particles: a framework to investigate the cognitive bases of movement.
Smart self-propelled particles: a framework to investigate the cognitive bases of movement.
复制标题
DOI：
10.1098/rsif.2023.0127
复制
发表时间：
2023-07
期刊：
影响因子：
--
通讯作者：
中科院分区：
文献类型：
--
作者：
单个动物或动物群的决策和运动通常被视为独立的过程进行研究。然而，许多决定是在特定空间中移动时做出的。换句话说，这两个过程是同时优化的，只有根据运动约束才能理解最佳决策过程。为了充分理解嵌入在环境中的决策的基本原理（以及潜在的进化过程），发展空间决策理论是很有帮助的。在这里，我们提出了一个框架，专门开发来解决这个问题的人工神经网络和遗传算法的手段。具体来说，我们调查一个简单的任务，其中单个代理需要学习探索他们的广场竞技场，而不离开其边界。我们表明，代理人发展越来越优化的策略来解决空间嵌入式学习任务，而没有一个初始的任意模型的运动。该过程允许智能体学习如何移动（即，通过避开竞技场墙），以便做出越来越优化的决策（改善它们对竞技场的探索）。最终，该框架预测了学习和运动相结合的任务可能的最佳行为策略。
Decision-making and movement of single animals or group of animals are often treated and investigated as separate processes. However, many decisions are taken while moving in a given space. In other words, both processes are optimized at the same time, and optimal decision-making processes are only understood in the light of movement constraints. To fully understand the rationale of decisions embedded in an environment (and therefore the underlying evolutionary processes), it is instrumental to develop theories of spatial decision-making. Here, we present a framework specifically developed to address this issue by the means of artificial neural networks and genetic algorithms. Specifically, we investigate a simple task in which single agents need to learn to explore their square arena without leaving its boundaries. We show that agents evolve by developing increasingly optimal strategies to solve a spatially embedded learning task while not having an initial arbitrary model of movements. The process allows the agents to learn how to move (i.e. by avoiding the arena walls) in order to make increasingly optimal decisions (improving their exploration of the arena). Ultimately, this framework makes predictions of possibly optimal behavioural strategies for tasks combining learning and movement.
登录
查看更多内容
影响因子：
4.6
作者：
Rodríguez-Morales D;Tapia-McClung H;Robledo-Ospina LE;Rao D
通讯作者：
Rao D
影响因子：
1.6
作者：
Mueller, Thomas;Fagan, William F.;Grimm, Volker
通讯作者：
Grimm, Volker
DOI：
10.1073/pnas.2102157118
发表时间：
2021-12-14
影响因子：
11.1
作者：
Sridhar VH;Li L;Gorbonos D;Nagy M;Schell BR;Sorochkin T;Gov NS;Couzin ID
通讯作者：
Couzin ID
影响因子：
--
作者：
Leung, FHF;Lam, HK;Tam, PKS
通讯作者：
Tam, PKS
影响因子：
4.8
作者：
Bouhlel, Mohamed Amine;Hwang, John T.;Martins, Joaquim R. R. A.
通讯作者：
Martins, Joaquim R. R. A.