DeepAI AI Chat
Log In Sign Up

Planning in Stochastic Environments with Goal Uncertainty

10/18/2018
by   Sandhya Saisubramanian, et al.
University of Massachusetts Amherst
0

We present the Goal Uncertain Stochastic Shortest Path (GUSSP) problem --- a general framework to model stochastic environments with goal uncertainty. The model is an extension of the stochastic shortest path (SSP) framework to dynamic environments in which it is impossible to determine the exact goal states ahead of plan execution. GUSSPs introduce flexibility in goal specification by allowing a belief over possible goal configurations. The partial observability is restricted to goals, facilitating the reduction to an SSP. We formally define a GUSSP and discuss its theoretical properties. We then propose an admissible heuristic that reduces the planning time of FLARES --- a start-of-the-art probabilistic planner. We also propose a determinization approach for solving this class of problems. Finally, we present empirical results using a mobile robot and three other problem domains.

READ FULL TEXT
08/27/2018

On the convergence of optimistic policy iteration for stochastic shortest path problem

In this paper, we prove some convergence results of a special case of op...
05/21/2017

Generalizing the Role of Determinization in Probabilistic Planning

The stochastic shortest path problem (SSP) is a highly expressive model ...
07/10/2020

Learning to plan with uncertain topological maps

We train an agent to navigate in 3D environments using a hierarchical st...
04/08/2022

Preliminary Results on Using Abstract AND-OR Graphs for Generalized Solving of Stochastic Shortest Path Problems

Several goal-oriented problems in the real-world can be naturally expres...
02/16/2019

Neuromodulated Goal-Driven Perception in Uncertain Domains

In uncertain domains, the goals are often unknown and need to be predict...
03/20/2016

An Approximation Approach for Solving the Subpath Planning Problem

The subpath planning problem is a branch of the path planning problem, w...