Factored Planning Using Decomposition Trees

Factored planning using decomposition trees

Factored Planning using Decomposition Trees

Improving AI planning algorithms relies on the ability to exploit the structure of the problem at hand. A promising direction is that of factored planning, where the domain is partitioned into subdomains with as little interaction as possible. Recent work in this field has led to an detailed theoretical analysis of such approaches and to a couple of high level planning algorithms, but with no p...

Factored Planning: How, When, and When Not

Automated domain factoring, and planning methods that utilize them, have long been of interest to planning researchers. Recent work in this area yielded new theoretical insight and algorithms, but left many questions open: How to decompose a domain into factors? How to work with these factors? And whether and when decomposition-based methods are useful? This paper provides theoretical analysis ...

Improving Decision Diagrams for Decision Theoretic Planning

In the domain of decision theoretic planning, the factored framework (FMDP) has produced optimized algorithms using Decision Trees (SVI, SPI) and Algebraic Decision Diagrams (SPUDD). However, the state-of-the-art SPUDD algorithm requires i) the problem to be specified with binary variables and ii) the data structures to share a common order on variables. In this article, we propose a new algori...

Sampling-Based Real-Time Motion Planning under State Uncertainty for Autonomous Micro-Aerial Vehicles in GPS-Denied Environments

This paper presents a real-time motion planning approach for autonomous vehicles with complex dynamics and state uncertainty. The approach is motivated by the motion planning problem for autonomous vehicles navigating in GPS-denied dynamic environments, which involves non-linear and/or non-holonomic vehicle dynamics, incomplete state estimates, and constraints imposed by uncertain and cluttered...