  We study the problem of generating plans for given natural language planning
task requests. On one hand, LLMs excel at natural language processing but do
not perform well on planning. On the other hand, classical planning tools excel
at planning tasks but require input in a structured language such as the
Planning Domain Definition Language (PDDL). We leverage the strengths of both
the techniques by using an LLM for generating the PDDL representation (task
PDDL) of planning task requests followed by using a classical planner for
computing a plan. Unlike previous approaches that use LLMs for generating task
PDDLs directly, our approach comprises of (a) translate: using an LLM only for
generating a logically interpretable intermediate representation of natural
language task description, (b) infer: deriving additional logically dependent
information from the intermediate representation using a logic reasoner
(currently, Answer Set Programming solver), and (c) compile: generating the
target task PDDL from the base and inferred information. We observe that using
an LLM to only output the intermediate representation significantly reduces LLM
errors. Consequently, TIC approach achieves, for at least one LLM, high
accuracy on task PDDL generation for all seven domains of our evaluation
dataset.
