רונן ברפמן

Stochastic local search (SLS) techniques are very effective in solving hard prepositional satisfiability problems. This has lead to the popularity of the encode & solve paradigm in which different problems are encoded as propositional satisfiability problems to which SLS techniques are applied. In AI, planning is the main area in which this methodology is used. Yet, it seems plausible that SLS methods should perform better when applied to the original problem space whose structure they can exploit. As part of our attempts to validate this thesis, we experimented with LPSP, a planner that applies SLS techniques to the space of linear plans. LPSP outperforms SLS applied to encoded planning problems that enforce a similar linearity assumption because of its ability to exploit the special structure of planning problems. Additional experiments (reported in a longer version of this paper) conducted on the Hamiltonian circuit problem lend farther support to our thesis.