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Sweewarman Balachandran

dblp:178/8717 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Robotics › Autonomous driving › vehicle control
autonomous vehicle control
0.212016
An Autonomous Override System to Prevent Airborne Loss of Control · AAAI 2016
Machine learning › Reinforcement learning
markov decision process
0.212016
An Autonomous Override System to Prevent Airborne Loss of Control · AAAI 2016
Knowledge, reasoning and agents › Planning, search and constraint satisfaction › planning
online planning
0.212016
An Autonomous Override System to Prevent Airborne Loss of Control · AAAI 2016
Knowledge, reasoning and agents › Planning, search and constraint satisfaction
sparse sampling
0.212016
An Autonomous Override System to Prevent Airborne Loss of Control · AAAI 2016
Embedded and real-time systems
cyber-physical system platforms
0.212016
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
YearPublicationVenuePosition
2016 An Autonomous Override System to Prevent Airborne Loss of Control
abstract
Loss 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
AAAI1