Collaborating Authors

Multi-Select Faceted Navigation Based on Minimum Description Length Principle

AAAI Conferences

Faceted navigation can effectively reduce user efforts of reaching targeted resources in databases, by suggesting dynamic facet values for iterative query refinement. A key issue is minimizing the navigation cost in a user query session. Conventional navigation scheme assumes that at each step, users select only one suggested value to figure out resources containing it. To make faceted navigation more flexible and effective, this paper introduces a multi-select scheme where multiple suggested values can be selected at one step, and a selected value can be used to either retain or exclude the resources containing it. Previous algorithms for cost-driven value suggestion can hardly work well under our navigation scheme. Therefore, we propose to optimize the navigation cost using the Minimum Description Length principle, which can well balance the number of navigation steps and the number of suggested values per step under our new scheme. An emperical study demonstrates that our approach is more cost-saving and efficient than state-of-the-art approaches.

Google Maps AR navigation is rolling out to a handful of users


Google Maps' augmented reality navigation is finally rolling out several months after its debut, although you might still have to wait a while. The company told the Wall Street Journal the walking-focused feature will be available shortly, but only to Local Guides (community reviewers) at first. The feature will need "more testing" before it's available to everyone else, Google said. Still, this suggests AR route-finding is much closer to becoming a practical reality. The core functionality remains the same.

Learning Efficient Navigation in Vortical Flow Fields Artificial Intelligence

Efficient point-to-point navigation in the presence of a background flow field is important for robotic applications such as ocean surveying. In such applications, robots may only have knowledge of their immediate surroundings or be faced with time-varying currents, which limits the use of optimal control techniques for planning trajectories. Here, we apply a novel Reinforcement Learning algorithm to discover time-efficient navigation policies to steer a fixed-speed swimmer through an unsteady two-dimensional flow field. The algorithm entails inputting environmental cues into a deep neural network that determines the swimmer's actions, and deploying Remember and Forget Experience replay. We find that the resulting swimmers successfully exploit the background flow to reach the target, but that this success depends on the type of sensed environmental cue. Surprisingly, a velocity sensing approach outperformed a bio-mimetic vorticity sensing approach by nearly two-fold in success rate. Equipped with local velocity measurements, the reinforcement learning algorithm achieved near 100% success in reaching the target locations while approaching the time-efficiency of paths found by a global optimal control planner.

Artificial Intelligence: Taking driverless navigation up a gear


AI is not only vital in ensuring successful and efficient navigation, but it's a crucial element in ensuring the journey from A to B is as safe and comfortable as possible. The biggest benefit of AI is its ability to boost efficiency and complete complex tasks that cannot be easily managed by humans. When it comes to navigation, this translates to evaluating real-time conditions with optimum route guidance that helps the driver avoid traffic, amongst other road hazards. The implementation of AI into cars, however, is no easy task. When the control over navigation is taken out of the driver's hands, there's a need to ensure that the data the AI is working with is up to code.

Translating Navigation Instructions in Natural Language to a High-Level Plan for Behavioral Robot Navigation Artificial Intelligence

We propose an end-to-end deep learning model for translating free-form natural language instructions to a high-level plan for behavioral robot navigation. The proposed model uses attention mechanisms to connect information from user instructions with a topological representation of the environment. To evaluate this model, we collected a new dataset for the translation problem containing 11,051 pairs of user instructions and navigation plans. Our results show that the proposed model outperforms baseline approaches on the new dataset. Overall, our work suggests that a topological map of the environment can serve as a relevant knowledge base for translating natural language instructions into a sequence of navigation behaviors.