S$^*$: A Heuristic Information-Based Approximation Framework for
  Multi-Goal Path Finding
  We combine ideas from uni-directional and bi-directional heuristic search,
and approximation algorithms for the Traveling Salesman Problem, to develop a
novel framework for a Multi-Goal Path Finding (MGPF) problem that provides a
2-approximation guarantee. MGPF aims to find a least-cost path from an origin
to a destination such that each node in a given set of goals is visited at
least once along the path. We present numerical results to illustrate the
advantages of our framework over conventional alternates in terms of the number
of expanded nodes and run time.
