Demo Data from EDBT 2026 / reviewers in the wild / expert
Kamal Mammadov
dblp:286/1212
· DBLP profile ↗
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
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Robotics › Motion planning and robot control › robot control › optimization-based control
gradient-based control |
1.0 | 1 | 2026 | Area-Optimal Control Strategies for Heterogeneous Multi-Agent Pursuit · AAAI 2026 |
Robotics › Motion planning and robot control › robot control
optimal control |
1.0 | 1 | 2026 | Area-Optimal Control Strategies for Heterogeneous Multi-Agent Pursuit · AAAI 2026 |
Knowledge, reasoning and agents › Multi-agent systems
pursuit-evasion |
1.0 | 1 | 2026 | 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
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | Area-Optimal Control Strategies for Heterogeneous Multi-Agent PursuitabstractThis 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 |
AAAI | 1 |