Demo Data from SIGIR 2026 / reviewers in the wild / expert
Sweewarman Balachandran
dblp:178/8717
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 2016
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 first-authorGraphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Artificial intelligence
1 paper |
Planning, search and constraint satisfaction · 50% Reinforcement learning · 25% Autonomous driving · 25% | |
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
Embedded and real-time systems · 100% |
Topics — the 5 heaviest of 6, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Robotics › Autonomous driving › vehicle control
autonomous vehicle control |
0.2 | 1 | 2016 | An Autonomous Override System to Prevent Airborne Loss of Control · AAAI 2016 |
Machine learning › Reinforcement learning
markov decision process |
0.2 | 1 | 2016 | An Autonomous Override System to Prevent Airborne Loss of Control · AAAI 2016 |
Knowledge, reasoning and agents › Planning, search and constraint satisfaction › planning
online planning |
0.2 | 1 | 2016 | An Autonomous Override System to Prevent Airborne Loss of Control · AAAI 2016 |
Knowledge, reasoning and agents › Planning, search and constraint satisfaction
sparse sampling |
0.2 | 1 | 2016 | An Autonomous Override System to Prevent Airborne Loss of Control · AAAI 2016 |
Embedded and real-time systems
cyber-physical system platforms |
0.2 | 1 | 2016 | An Autonomous Override System to Prevent Airborne Loss of Control · AAAI 2016 |
Methods — techniques the papers use, named apart from their topics
sparse sampling · 0.5markov decision process · 0.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2016 | An Autonomous Override System to Prevent Airborne Loss of ControlabstractLoss of Control (LOC) is the most common precursor to aircraft accidents. This paper presents a Flight Safety Assessment and Management (FSAM) decision system to reduce in-flight LOC risk. FSAM nominally serves as a monitor to detect conditions that pose LOC risk, automatically activating the appropriate control authority if necessary to prevent LOC and restore a safe operational state. This paper contributes an efficient Markov Decision Process (MDP) formulation for FSAM. The state features capture risk associated with aircraft dynamics, configuration, health, pilot behavior and weather. The reward function trades cost of inaction against the cost of overriding the current control authority. A sparse sampling algorithm obtains a near-optimal solution for the MDP online. This approach enables the FSAM MDP to incorporate dynamically changing flight envelope and environment constraints into decision-making. Case studies based on realworld aviation incidents are presented. Sweewarman Balachandran, Ella M. Atkins |
AAAI | 1 |