Learning How to Search for Plans with Exponentially Less Space

Key points

• We instead learn search control, one specification per domain, written as an indexical policy: a generalized policy with registers that hold objects and modes that sequence its rules.1
• We add the choose rule, which loads an object into a register and marks a backtracking point, where one candidate suffices; every other rule must work for all of its outcomes and needs no search.1
• Our main result is that structural termination, which rules out infinite executions, also bounds every execution by a polynomial in the number of objects.1
• A depth-first procedure then finds a plan in polynomial space, however large the state space, with no list of visited states.1

Sources (1)

• [1]Learning How to Search for Plans with Exponentially Less Space
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 7, 10:14 PM

  Heuristic search for a plan can store exponentially many states, even when its heuristic is almost perfect.

  We instead learn search control, one specification per domain, written as an indexical policy: a generalized policy with registers that hold objects and modes that sequence its rules.

Extractive summary: sentences quoted from the sources.