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Kamal Mammadov

dblp:286/1212 · DBLP profile ↗
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1ranked-venue papers
1as first author
1since 2021 · last 2026
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author · 1 since 2021

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
Motion planning and robot control · 67% Multi-agent systems · 33%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Motion planning and robot control › robot control › optimization-based control
gradient-based control
1.012026
Area-Optimal Control Strategies for Heterogeneous Multi-Agent Pursuit · AAAI 2026
Robotics › Motion planning and robot control › robot control
optimal control
1.012026
Area-Optimal Control Strategies for Heterogeneous Multi-Agent Pursuit · AAAI 2026
Knowledge, reasoning and agents › Multi-agent systems
pursuit-evasion
1.012026
Area-Optimal Control Strategies for Heterogeneous Multi-Agent Pursuit · AAAI 2026

Methods — techniques the papers use, named apart from their topics

zero-sum game formulation · 1.0apollonius circles · 1.0analytical gradient · 1.0
YearPublicationVenuePosition
2026 Area-Optimal Control Strategies for Heterogeneous Multi-Agent Pursuit
abstract
This paper presents a novel strategy for a multi-agent pursuit-evasion game involving multiple faster pursuers with heterogenous speeds and a single slower evader. We define a geometric region, the evader's safe-reachable set, as the intersection of Apollonius circles derived from each pursuer-evader pair. The capture strategy is formulated as a zero-sum game where the pursuers cooperatively minimize the area of this set, while the evader seeks to maximize it, effectively playing a game of spatial containment. By deriving the analytical gradients of the safe-reachable set's area with respect to agent positions, we obtain closed-form, instantaneous optimal control laws for the heading of each agent. These strategies are computationally efficient, allowing for real-time implementation. Simulations demonstrate that the gradient-based controls effectively steer the pursuers to systematically shrink the evader’s safe region, leading to guaranteed capture. This area-minimization approach provides a clear geometric objective for cooperative capture.
Kamal Mammadov, Damith Chinthana Ranasinghe
AAAI1