Approximate Novelty Search

TL;DR

This paper introduces polynomial approximation methods for width-based search algorithms, significantly improving their efficiency and scalability in planning tasks by using random sampling, Bloom filters, and adaptive policies.

Contribution

It presents novel polynomial approximation techniques for novelty computation and search strategies, enhancing the performance of width-based planners.

Findings

Significantly better performance than state-of-the-art planners on benchmarks.

Reduced runtime and memory usage through approximation methods.

Effective integration of sampling and adaptive policies into existing algorithms.

Abstract

Width-based search algorithms seek plans by prioritizing states according to a suitably defined measure of novelty, that maps states into a set of novelty categories. Space and time complexity to evaluate state novelty is known to be exponential on the cardinality of the set. We present novel methods to obtain polynomial approximations of novelty and width-based search. First, we approximate novelty computation via random sampling and Bloom filters, reducing the runtime and memory footprint. Second, we approximate the best-first search using an adaptive policy that decides whether to forgo the expansion of nodes in the open list. These two techniques are integrated into existing width-based algorithms, resulting in new planners that perform significantly better than other state-of-the-art planners over benchmarks from the International Planning Competitions.