Table 1. Descriptions of Normative Algorithms and Heuristics.

Normative Algorithm	Description
RRTD-IDDFS	Resource-Rational Task Decomposition (RRTD) using Iterative-Deepening Depth-First Search (IDDFS) as a search algorithm
RRTD-BFS	RRTD using Breadth-First Search (BFS) as a search algorithm
RRTD-RW	RRTD using a Random Walk (RW) as a search algorithm
Solway et al. (2014) [8]	Identifies partitions of the task into subtasks that minimize the description length of optimal solutions, given that subtask solutions are reused across tasks.
Tomov et al. (2020) [7]	Performs inference over partitions of the task graph into regions based on a prior over hierarchical graphs. Incorporates a preference for tasks to start and end in the same region, and for states in the same region to have similar rewards.
Heuristic	Description
QCut [16, 33]	Partitions the task graph through spectral decomposition of the graph.
Degree Centrality	Chooses subgoals based on Degree Centrality, which is the number of transitions into or out of a state s. For tasks where all state transitions are reversible, Degree Centrality is the number of neighbors $\left|\mathcal{N}\right(s\left)\right|$.
Betweenness Centrality [22]	Chooses subgoals based on Betweenness Centrality, which is how often a state s appears on shortest paths, averaged over all possible start and goal states. Takes into account cases with multiple shortest paths.