Deterministic Techniques for Stochastic Planning No longer the Rodney Dangerfield of Stochastic Planning?

Solving stochastic planning problems via determinizations Quite an old idea (e.g. envelope extension methods) What is new is that there is increasing realization that determinizing approaches provide state-of-the-art performance –Even for probabilistically interesting domains Should be a happy occasion..

Ways of using deterministic planning To compute the conditional branches –Robinson et al. To seed/approximate the value function –ReTraSE,Peng Dai, McLUG/POND, FF-Hop Use single determinization –FF-replan –ReTrASE (use diverse plans for a single determinization) Use sampled determinizations –FF-hop [AAAI 2008; with Yoon et al] –Use Relaxed solutions (for sampled determinizations) Peng Dai’s paper McLug [AIJ 2008; with Bryce et al] Would be good to understand the tradeoffs… Determinization = Sampling evolution of the world

Comparing approaches.. ReTrASE and FF-Hop seem closely related –ReTrASE uses diverse deterministic plans for a single determinization; FF-HOP computes deterministic plans for sampled determinizations –Is there any guarantee that syntactic (action) diversity is actually related to likely sample worlds? Cost of generating deterministic plans isn’t exactly too cheap.. –Relaxed reachability style approaches can compute multiple plans (for samples of the worlds) Would relaxation of samples’ plans be better or worse in convergence terms..?

Science may never fully explain who killed JFK, but any explanation must pass the scientific judgement. MDPs may never fully generate policies efficiently but any approach that does must pass MDP judgement.