Ran Taig

ICAPS Conference 2015 Conference Paper

A Compilation Based Approach to Conformant Probabilistic Planning with Stochastic Actions

• Ran Taig
• Ronen I. Brafman

We extend RBPP, the state-of-the-art, translation-based planner for conformant probabilistic planning (CPP) with deterministic actions, to handle a wide set of CPPs with stochastic actions. Our planner uses relevance analysis to divide a probabilistic "failure-allowance" between the initial state and the stochastic actions. Using its "initial-state allowance, " it uses relevance analysis to select a subset of the set of initial states on which planning efforts will focus. Then, it generates a deterministic planning problem using all-outcome determinization in which action cost reflects the probability of the modeledoutcome. Finally, a cost-bounded classical planner generates a plan with failure probability lower than the"stochastic-effect allowance. " Our compilation method is sound, but incomplete, as it may underestimates the success probability of a plan. Yet, it scales up much better than the state-of-the-art PFF planner, solving larger problems and handling tighter probabilistic bounds on existing benchmarks.

AAAI Conference 2014 Conference Paper

A Relevance-Based Compilation Method for Conformant Probabilistic Planning

• Ran Taig
• Ronen Brafman

Conformant probabilistic planning (CPP) differs from conformant planning (CP) by two key elements: the initial belief state is probabilistic, and the conformant plan must achieve the goal with probability ≥ θ, for some 0 < θ ≤ 1. In earlier work we observed that one can reduce CPP to CP by finding a set of initial states whose probability ≥ θ, for which a conformant plan exists. In previous solvers we used the underlying planner to select this set of states and to plan for them simultaneously. Here we suggest an alternative approach: start with relevance analysis to determine a promising set of initial states on which to focus. Then, call an off-the-shelf conformant planner to solve the resulting problem. This approach has a number of advantages. First, instead of depending on the heuristic function to select the set of initial states, we can introduce specific, efficient relevance reasoning techniques. Second, we can benefit from optimizations used by conformant planners that are unsound when applied to the original CPP. Finally, we are free to use any existing (or new) CP solver. Consequently, the new planner dominates previous solvers on almost all domains and scales to instances that were not solved before.

AAAI Conference 2014 Conference Paper

Compilation Based Approaches to Probabilistic Planning — Thesis Summary

• Ran Taig

ICAPS Conference 2013 Conference Paper

Compiling Conformant Probabilistic Planning Problems into Classical Planning

• Ran Taig
• Ronen I. Brafman

In CPP, we are given a set of actions (assumed deterministic in this paper), a distribution over initial states, a goal condition, and a real value 0 < θ ≤1. We seek a plan π such that following its execution, the goal probability is at least θ. Motivated by the success of the translation-based approach for conformant planning, introduced by Palacios and Geffner, we suggest a new compilation scheme from CPP to classical planning. Our compilation scheme maps CPP into cost-bounded classical planning, where the cost-bound represents the maximum allowed probability of failure. Empirically, this technique shows mixed, but promising results, performing very well on some domains, and less so on others when compared to the state of the art PFF planner. It is also very flexible due to its generic nature, allowing us to experiment with diverse search strategies developed for classical planning. Our results show that compilation-based technique offer a new viable approach to CPP and, possibly, more general probabilistic planning problems.