The Planner Optimization Problem: Formulations and Frameworks

03/12/2023
by   Yiyuan Lee, et al.
0

Identifying internal parameters for planning is crucial to maximizing the performance of a planner. However, automatically tuning internal parameters which are conditioned on the problem instance is especially challenging. A recent line of work focuses on learning planning parameter generators, but lack a consistent problem definition and software framework. This work proposes the unified planner optimization problem (POP) formulation, along with the Open Planner Optimization Framework (OPOF), a highly extensible software framework to specify and to solve these problems in a reusable manner.

READ FULL TEXT

page 3

page 7

page 8

page 9

research
07/26/2016

Focused Model-Learning and Planning for Non-Gaussian Continuous State-Action Systems

We introduce a framework for model learning and planning in stochastic d...
research
11/30/2011

Task Interaction in an HTN Planner

Hierarchical Task Network (HTN) planning uses task decomposition to plan...
research
07/04/2013

Towards Combining HTN Planning and Geometric Task Planning

In this paper we present an interface between a symbolic planner and a g...
research
04/02/2020

Human-Guided Planner for Non-Prehensile Manipulation

We present a human-guided planner for non-prehensile manipulation in clu...
research
05/27/2015

Qsmodels: ASP Planning in Interactive Gaming Environment

Qsmodels is a novel application of Answer Set Programming to interactive...
research
06/09/2011

A Critical Assessment of Benchmark Comparison in Planning

Recent trends in planning research have led to empirical comparison beco...
research
10/30/2018

Preparing for the Unexpected: Diversity Improves Planning Resilience in Evolutionary Algorithms

As automatic optimization techniques find their way into industrial appl...

Please sign up or login with your details

Forgot password? Click here to reset