Inspired by cognitive theories of creativity, this paper introduces a computational model (AIGenC) that lays down the necessary components to enable artificial agents to learn, use and generate transferable representations. Unlike machine representation learning, which relies exclusively on raw sensory data, biological representations incorporate relational and associative information that embeds rich and structured concept spaces. The AIGenC model poses a hierarchical graph architecture with various levels and types of representations procured by different components. The first component, Concept Processing, extracts objects and affordances from sensory input and encodes them into a concept space. The resulting representations are stored in a dual memory system and enriched with goal-directed and temporal information acquired through reinforcement learning, creating a higher-level of abstraction. Two additional components work in parallel to detect and recover relevant concepts and create new ones, respectively, in a process akin to cognitive Reflective Reasoning and Blending. The Reflective Reasoning unit detects and recovers from memory concepts relevant to the task by means of a matching process that calculates a similarity value between the current state and memory graph structures. Once the matching interaction ends, rewards and temporal information are added to the graph, building further abstractions. If the reflective reasoning processing fails to offer a suitable solution, a blending operation comes into place, creating new concepts by combining past information. We discuss the model's capability to yield better out-of-distribution generalisation in artificial agents, thus advancing toward Artificial General Intelligence.

翻译：摘要：受创造力认知理论的启发，本文提出了一种计算模型（AIGenC），该模型奠定了使人工智能体能够学习、使用和生成可迁移表示的必要组件。与仅依赖原始感官数据的机器表示学习不同，生物表示融入了关系性和关联性信息，从而嵌入丰富且结构化的概念空间。AIGenC模型采用分层图架构，包含由不同组件获取的各种层次和类型的表示。第一个组件——概念处理模块，从感官输入中提取对象和可供性，并将其编码至概念空间。由此产生的表示存储于双记忆系统中，并通过强化学习获取的目标导向和时间信息加以丰富，从而形成更高层次的抽象。另外两个组件并行运作，分别用于检测并恢复相关概念以及创造新概念，这一过程类似于认知中的反思推理与概念融合。反思推理单元通过匹配过程（计算当前状态与记忆图结构之间的相似度）检测并从记忆中恢复与任务相关的概念。匹配交互结束后，奖励信息和时间信息被添加至图中，构建更高层次的抽象。若反思推理处理未能提供合适解决方案，则启动融合操作，通过组合过往信息创造新概念。我们讨论了该模型在提升人工智能体分布外泛化能力方面的潜力，从而向通用人工智能迈进。