  LLMs have recently made impressive inroads on tasks whose output is
structured, such as coding, robotic planning and querying databases. The vision
of creating AI-powered personal assistants also involves creating structured
outputs, such as a plan for one's day, or for an overseas trip. Here, since the
plan is executed by a human, the output doesn't have to satisfy strict
syntactic constraints. A useful assistant should also be able to incorporate
vague constraints specified by the user in natural language. This makes LLMs an
attractive option for planning.
  We consider the problem of planning one's day. We develop an LLM-based
planner (LLMPlan) extended with the ability to self-reflect on its output and a
symbolic planner (SymPlan) with the ability to translate text constraints into
a symbolic representation. Despite no formal specification of constraints, we
find that LLMPlan performs explicit constraint satisfaction akin to the
traditional symbolic planners on average (2% performance difference), while
retaining the reasoning of implicit requirements. Consequently, LLM-based
planners outperform their symbolic counterparts in user satisfaction (70.5% vs.
40.4%) during interactive evaluation with 40 users.
