Navigating in Uncertain Environments with Heterogeneous Visibility

2026-03-03

AI Summary

This work addresses navigation in environments with uncertain connectivity, where agents must balance path cost against the value of information gained through observation. The authors propose a heuristic algorithm that efficiently selects paths by optimizing the sum of traversal cost and a customizable observation reward across nodes with heterogeneous visibility. The method introduces an edge utility evaluation mechanism based on multi-realization environmental sampling and employs a single hyperparameter to flexibly modulate the importance of observations. By integrating shortest-path sampling, observation-reward modeling, and heterogeneous visibility mapping, the algorithm consistently achieves lower average path costs than a shortest-path baseline that ignores observations, while reducing computational overhead by orders of magnitude compared to existing approaches—demonstrating strong performance across diverse uncertain navigation tasks, including those involving real-world terrain.

Technology Category

• Search and Optimization: Heuristic Search
• Planning, Routing, and Scheduling: Planning under Uncertainty
• Reasoning under Uncertainty: Sequential Decision Making

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Navigating an environment with uncertain connectivity requires a strategic balance between minimizing the cost of traversal and seeking information to resolve map ambiguities. Unlike previous approaches that rely on local sensing, we utilize a framework where nodes possess varying visibility levels, allowing for observation of distant edges from certain vantage points. We propose a novel heuristic algorithm that balances the cost of detouring to high-visibility locations against the gain in information by optimizing the sum of a custom observation reward and the cost of traversal. We introduce a technique to sample the shortest path on numerous realizations of the environment, which we use to define an edge's utility for observation and to quickly estimate the path with the highest reward. Our approach can be easily adapted to a variety of scenarios by tuning a single hyperparameter that determines the importance of observation. We test our method on a variety of uncertain navigation tasks, including a map based on real-world topographical data. The method demonstrates lower mean cost of traversal compared to a shortest path baseline that does not consider observation and has exponentially lower computational overhead compared to an existing method for balancing observation with path cost minimization.

Problem

• uncertain navigation
• heterogeneous visibility
• map ambiguity
• path cost minimization
• information gathering

Innovation

• heterogeneous visibility
• uncertain navigation
• observation-reward heuristic
• path cost minimization
• environment sampling