Search Results for author:
Found 18 papers, 5 papers with code
Synergising Human-like Responses and Machine Intelligence for Planning in Disaster Response
In the rapidly changing environments of disaster response, planning and decision-making for autonomous agents involve complex and interdependent choices.
Hierarchical Fault-Tolerant Coverage Control for an Autonomous Aerial Agent
The second-stage fault-tolerant controller then aims to follow this reference plan, even in the presence of erroneous control inputs caused by non-Gaussian disturbances.
Incremental Learning with Concept Drift Detection and Prototype-based Embeddings for Graph Stream Classification
Data stream mining aims at extracting meaningful knowledge from continually evolving data streams, addressing the challenges posed by nonstationary environments, particularly, concept drift which refers to a change in the underlying data distribution over time.
Joint Estimation and Control for Multi-Target Passive Monitoring with an Autonomous UAV Agent
This work considers the problem of passively monitoring multiple moving targets with a single unmanned aerial vehicle (UAV) agent equipped with a direction-finding radar.
Integrated Guidance and Gimbal Control for Coverage Planning With Visibility Constraints
This work proposes an integrated guidance and gimbal control coverage path planning (CPP) approach, in which the mobility and gimbal inputs of an autonomous UAV agent are jointly controlled and optimized to achieve full coverage of a given object of interest, according to a specified set of optimality criteria.
Distributed Search Planning in 3-D Environments With a Dynamically Varying Number of Agents
In this work, a novel distributed search-planning framework is proposed, where a dynamically varying team of autonomous agents cooperate in order to search multiple objects of interest in three-dimension (3-D).
A Cooperative Multi-Agent Probabilistic Framework for Search and Track Missions
In this work a robust and scalable cooperative multi-agent searching and tracking framework is proposed.
Jointly-Optimized Searching and Tracking with Random Finite Sets
In this paper, we investigate the problem of joint searching and tracking of multiple mobile targets by a group of mobile agents.
Probabilistic Search and Track with Multiple Mobile Agents
In this paper we are interested in the task of searching and tracking multiple moving targets in a bounded surveillance area with a group of autonomous mobile agents.
Decentralized Search and Track with Multiple Autonomous Agents
Based on this, we develop decentralized cooperative look-ahead strategies for efficient searching and tracking of an unknown number of targets inside a bounded surveillance area.
Data augmentation on-the-fly and active learning in data stream classification
Second, learning models have access to more labelled data without the need to increase the active learning budget and / or the original memory size.
A Hybrid Active-Passive Approach to Imbalanced Nonstationary Data Stream Classification
In real-world applications, the process generating the data might suffer from nonstationary effects (e. g., due to seasonality, faults affecting sensors or actuators, and changes in the users' behaviour).
Nonstationary data stream classification with online active learning and siamese neural networks
We conduct an extensive study that compares the role of different active learning budgets and strategies, the performance with/without memory, the performance with/without ensembling, in both synthetic and real-world datasets, under different data nonstationarity characteristics and class imbalance levels.
Performance Analysis of Event-Triggered Consensus Control for Multi-agent Systems under Cyber-Physical Attacks
This work presents a rigorous analysis of the adverse effects of cyber-physical attacks on the performance of multi-agent consensus with event-triggered control protocols.
Data-efficient Online Classification with Siamese Networks and Active Learning
In this paper we investigate learning from limited labelled, nonstationary and imbalanced data in online classification.
Online Learning With Adaptive Rebalancing in Nonstationary Environments
An enormous and ever-growing volume of data is nowadays becoming available in a sequential fashion in various real-world applications.
Extracting the fundamental diagram from aerial footage
At large, this system behavior is characterized through the fundamental diagram of a road segment, a region or the network.
Queue-based Resampling for Online Class Imbalance Learning
Online class imbalance learning constitutes a new problem and an emerging research topic that focusses on the challenges of online learning under class imbalance and concept drift.
