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Found 7 papers, 2 papers with code
Program-Based Strategy Induction for Reinforcement Learning
Typical models of learning assume incremental estimation of continuously-varying decision variables like expected rewards.
Exploring the hierarchical structure of human plans via program generation
We find that humans are sensitive to both metrics, but that both accounts fail to predict a qualitative feature of human-created programs, namely that people prefer programs with reuse over and above the predictions of MDL.
Structurally guided task decomposition in spatial navigation tasks
How are people able to plan so efficiently despite limited cognitive resources?
Humans decompose tasks by trading off utility and computational cost
Human behavior emerges from planning over elaborate decompositions of tasks into goals, subgoals, and low-level actions.
Using Natural Language and Program Abstractions to Instill Human Inductive Biases in Machines
Co-training on these representations result in more human-like behavior in downstream meta-reinforcement learning agents than less abstract controls (synthetic language descriptions, program induction without learned primitives), suggesting that the abstraction supported by these representations is key.
People construct simplified mental representations to plan
We propose a computational account of this simplification process and, in a series of pre-registered behavioral experiments, show that it is subject to online cognitive control and that people optimally balance the complexity of a task representation and its utility for planning and acting.
Resource-rational Task Decomposition to Minimize Planning Costs
That is, rather than planning over a monolithic representation of a task, they decompose the task into simpler subtasks and then plan to accomplish those.
