Symbolic Pattern Planning

Abstract

Prof. Enrico Giunchiglia will present a novel approach for solving numeric planning problems, called Symbolic Pattern Planning. Given a planning problem Π, a bound n and a pattern≺ –defined as an arbitrary sequence of actions - we encode the problem of finding a plan for Π with bound n as an SMT formula Π≺n with fewer variables and clauses than all the other state-of-the-art encodings. 

Moreover, we prove that, for anybound n, the other encodings can never find a valid plan while ours does not. We argue that our approach provides a new starting point for symbolic planning,allowing to bridge the gap with search-based planning (this is joint work with Matteo Cardellini and Marco Maratea).

To sustain such claim, we present some recent results (under submission) which show the benefits of integrating search-based techniques in Symbolic Pattern Planning. Further, we present how Symbolic Pattern Planning can be effectively extended to the temporal setting (under submission). All the claims are supported by extensive experimental comparisons involving the various available planning systems  (this is joint work with Matteo Cardellini).

About the Speaker

Enrico Giunchiglia is full professor in Computer Engineering at the Università degli Studi di Genova. His main research interests are in Automated Reasoning, Formal Verification and Automated AI Planning, in which fields he published more than 100 papers, collecting more than 8000 citations on Google Scholar.