Beam Search: Faster and Monotonic

by   Sofia Lemons, et al.

Beam search is a popular satisficing approach to heuristic search problems that allows one to trade increased computation time for lower solution cost by increasing the beam width parameter. We make two contributions to the study of beam search. First, we show how to make beam search monotonic; that is, we provide a new variant that guarantees non-increasing solution cost as the beam width is increased. This makes setting the beam parameter much easier. Second, we show how using distance-to-go estimates can allow beam search to find better solutions more quickly in domains with non-uniform costs. Together, these results improve the practical effectiveness of beam search.



page 1

page 2

page 3

page 4


Setting Up the Beam for Human-Centered Service Tasks

We introduce the Beam, a collaborative autonomous mobile service robot, ...

gBeam-ACO: a greedy and faster variant of Beam-ACO

Beam-ACO, a modification of the traditional Ant Colony Optimization (ACO...

A Quantum Search Decoder for Natural Language Processing

Probabilistic language models, e.g. those based on an LSTM, often face t...

Pointing Error Analysis of Optically Pre-Amplified Pulse Position Modulation Receivers

We present analytical results on the effect of pointing errors on the av...

Best-First Beam Search

Decoding for many NLP tasks requires a heuristic algorithm for approxima...

Multi-rendezvous Spacecraft Trajectory Optimization with Beam P-ACO

The design of spacecraft trajectories for missions visiting multiple cel...

A simulation study to distinguish prompt photon from π^0 and beam halo in a granular calorimeter using deep networks

In a hadron collider environment identification of prompt photons origin...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.