戈芬凤头鹦鹉的机械解题--走向复杂行为建模

IF 1.2 4区 计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Manuel Baum, Theresa Rössler, Antonio J. Osuna-Mascaró, Alice Auersperg, Oliver Brock
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引用次数: 0

摘要

戈芬凤头鹦鹉(Cacatua goffiniana)可以解决各种各样的机械问题,如使用工具、制造工具和机械谜题。然而,这种适应行为的基本机制在很大程度上是未知的。同样,在机器人学等领域,设计出能够灵活解决此类机械难题的人工代理仍然是一项巨大的挑战。本文采用跨学科方法来研究机械问题的解决,我们希望这种方法与这两个领域都有关联。我们所研究的行为是复杂环境(锁箱)与支配动物行为的不同过程之间相互作用的结果。因此,我们对鹦鹉的(1)参与、(2)感觉运动技能学习和(3)行动选择进行了联合分析。我们发现,这些方面都不能单独解释动物的行为适应性,一个合理的近似机制模型必须共同解决这些方面的问题。在分析的同时,我们还讨论了确定这种机制的方法。同时,我们认为,仅凭几项研究的有限行为数据来确定一个详细的模型是难以置信的。相反,我们主张采用渐进的方法来建立模型,即首先建立近似机制的约束条件,然后再制定具体、详细的模型。为了说明这一观点,我们将其应用于本文提供的数据。我们认为,随着该领域试图为日益复杂的行为找到机理解释,这种替代建模方法将是必要的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Mechanical Problem Solving in Goffin’s Cockatoos—Towards Modeling Complex Behavior
Goffin’s cockatoos ( Cacatua goffiniana) can solve a diverse set of mechanical problems, such as tool use, tool manufacture, and mechanical puzzles. However, the proximate mechanisms underlying this adaptive behavior are largely unknown. Similarly, engineering artificial agents that can as flexibly solve such mechanical puzzles is still a substantial challenge in areas such as robotics. This article is an interdisciplinary approach to study mechanical problem solving which we hope is relevant to both fields. The behavior we are studying results from the interaction between a complex environment (the lockbox) and different processes that govern the animals’ behavior. We therefore jointly analyze the parrots’ (1) engagement, (2) sensorimotor skill learning, and (3) action selection. We find that none of these aspects could solely explain the animals’ behavioral adaptation and that a plausible model of proximate mechanisms must jointly address these aspects. We accompany this analysis with a discussion of methods to identify such mechanisms. At the same time, we argue, it is implausible to identify a detailed model from the limited behavioral data of just a few studies. Instead, we advocate for an incremental approach to model building in which one first establishes constraints on proximate mechanisms before specific, detailed models are formulated. To illustrate this idea, we apply it to the data presented here. We argue that as the field attempts to find mechanistic explanations for increasingly complex behaviors, such alternative modeling approaches will be necessary.
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来源期刊
Adaptive Behavior
Adaptive Behavior 工程技术-计算机:人工智能
CiteScore
4.30
自引率
18.80%
发文量
34
审稿时长
>12 weeks
期刊介绍: _Adaptive Behavior_ publishes articles on adaptive behaviour in living organisms and autonomous artificial systems. The official journal of the _International Society of Adaptive Behavior_, _Adaptive Behavior_, addresses topics such as perception and motor control, embodied cognition, learning and evolution, neural mechanisms, artificial intelligence, behavioral sequences, motivation and emotion, characterization of environments, decision making, collective and social behavior, navigation, foraging, communication and signalling. Print ISSN: 1059-7123
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