A Polynomial All Outcome Determinization for Probabilistic Planning

نویسندگان

  • Thomas Keller
  • Patrick Eyerich
چکیده

Most predominant approaches in probabilistic planning utilize techniques from the more thoroughly investigated field of classical planning by determinizing the problem at hand. In this paper, we present a method to map probabilistic operators to an equivalent set of probabilistic operators in a novel normal form, requiring polynomial time and space. From this, we directly derive a determinization which can be used for, e. g., replanning strategies incorporating a classical planning system. Unlike previously described all outcome determinizations, the number of deterministic operators is not exponentially but polynomially bounded in the number of parallel probabilistic effects, enabling the use of more sophisticated determinization-based techniques in the future.

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Generalizing the Role of Determinization in Probabilistic Planning

The stochastic shortest path problem (SSP) is a highly expressive model for probabilistic planning. The computational hardness of SSPs has sparked interest in determinization-based planners that can quickly solve large problems. However, existing methods employ a simplistic approach to determinization. In particular, they ignore the possibility of tailoring the determinization to the specific c...

متن کامل

Probabilistic Planning with Reduced Models

Markov decision processes (MDP) (Puterman 1994) offer a rich model that has been extensively used by the AI community for planning and learning under uncertainty. Some applications include planning for mobile robots, network management, optimizing software on mobile phones, and managing water levels of river reservoirs. MDPs have polynomial complexity in the size of the state space, but the sta...

متن کامل

Extending Classical Planning Heuristics to Probabilistic Planning with Dead-Ends

Recent domain-determinization techniques have been very successful in many probabilistic planning problems. We claim that traditional heuristic MDP algorithms have been unsuccessful due mostly to the lack of efficient heuristics in structured domains. Previous attempts like mGPT used classical planning heuristics to an all-outcome determinization of MDPs without discount factor ; yet, discounte...

متن کامل

A Compilation Based Approach to Conformant Probabilistic Planning with Stochastic Actions

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 sele...

متن کامل

Improving Determinization in Hindsight for On-line Probabilistic Planning

Recently, ‘determinization in hindsight’ has enjoyed surprising success in on-line probabilistic planning. This technique evaluates the actions available in the current state by using non-probabilistic planning in deterministic approximations of the original domain. Although the approach has proven itself effective in many challenging domains, it is computationally very expensive. In this paper...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2011