用类比复用旧经验中的结构，降低新问题建模与规划的计算成本。
Analogy making as amortised model construction
- 将类比视为马尔可夫决策过程间的部分同态，构建可复用的抽象模块。
- 通过模块化组合，在不同领域实现策略与表征的灵活迁移。
- 适合研究认知建模、强化学习中的高效推理与跨域适应者。
人类在面对新情境时能灵活构建内部模型以指导行动。这些模型需在有限资源下足够准确，同时又易于构造。我们提出，类比在这一过程中起核心作用：它使智能体能够复用过往经验中与解决方案相关的结构，从而分摊建模（表征）与规划的计算开销。我们将类比形式化为马尔可夫决策过程间的部分同态，构建一个框架，其中从先前表征中提取的抽象模块可作为新表征的可组合构件。这种模块化复用支持在具有共同结构本质的不同领域间灵活调整策略与表征。
原文摘要 · Abstract (English)
Humans flexibly construct internal models to navigate novel situations. To be useful, these internal models must be sufficiently faithful to the environment that resource-limited planning leads to adequate outcomes; equally, they must be tractable to construct in the first place. We argue that analogy plays a central role in these processes, enabling agents to reuse solution-relevant structure from past experiences and amortise the computational costs of both model construction (construal) and planning. Formalising analogies as partial homomorphisms between Markov decision processes, we sketch a framework in which abstract modules, derived from previous construals, serve as composable building blocks for new ones. This modular reuse allows for flexible adaptation of policies and representations across domains with shared structural essence.
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