The ability to invent new tools has been identified as an important facet of our ability as a species to problem solve in dynamic and novel environments. While the use of tools by artificial agents presents a challenging task and has been widely identified as a key goal in the field of autonomous robotics, far less research has tackled the invention of new tools by agents. In this paper, (1) we articulate the distinction between tool discovery and tool innovation by providing a minimal description of the two concepts under the formalism of active inference. We then (2) apply this description to construct a toy model of tool innovation by introducing the notion of tool affordances into the hidden states of the agent's probabilistic generative model. This particular state factorisation facilitates the ability to not just discover tools but invent them through the offline induction of an appropriate tool property. We discuss the implications of these preliminary results and outline future directions of research.

翻译：发明新工具的能力已被视为人类在动态且新颖环境中解决问题能力的重要方面。尽管人工智能体使用工具是一项具有挑战性的任务，并且已被广泛认为是自主机器人领域的关键目标，但关于智能体发明新工具的研究却少得多。在本文中，（1）我们通过提供主动推理形式化下这两个概念的最小化描述，阐明了工具发现与工具创新之间的区别。然后，（2）我们应用这一描述，通过将工具可供性概念引入智能体概率生成模型的隐藏状态，构建了一个工具创新的简化模型。这种特定的状态因子分解不仅促进了工具发现的能力，还通过离线归纳适当的工具属性实现了工具的发明。我们讨论了这些初步结果的意义，并概述了未来的研究方向。