Hierarchical, Symbolic, Language, and Multi-Agent Planning › Key Parameters

The key design parameters across the abstractions in this chapter are:

• Option horizon, termination, and interruption: duration affects high-level decision frequency and continuation discounting. A closed-loop option still reacts internally; termination or an explicit interruption rule determines when the high-level policy can reselect.
• Option library size: a larger library can improve coverage while increasing high-level branching. Under the toy full-tree model, the depth benefit exceeds the branching cost when $|\Omega| < |\mathcal{A}|^{\bar{\tau}}$; real selection also depends on option quality and overlap.
• Predicate granularity: the number and coarseness of symbolic predicates, which trades grounding reliability against planner tractability. Coarse predicates are easier to detect reliably but blur distinctions the planner may need; fine predicates capture more structure but are harder to ground correctly.
• Recursion depth $d$: how many levels of nested belief the multi-agent model captures, trading strategic depth against computational cost and sensitivity to opponent assumptions. Higher depth can represent deeper strategic reasoning but is more expensive and more fragile when the assumptions are violated.
• Grounding vocabulary and training coverage: broader coverage can handle more concepts, but reliability depends on representation, data, compositional generalization, and calibrated abstention—not vocabulary size alone.