Demo Data from VLDB 2026 / reviewers in the wild / expert
Esin Seref
dblp:99/40
· DBLP profile ↗
2ranked-venue papers
0as first author
0since 2021 · last 2001
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1Applied, interdisciplinary, general and emerging computing · 1
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 |
Multi-agent systems · 56% Reinforcement learning · 44% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Reinforcement learning
goal-conditioned reinforcement learning |
0.0 | 1 | 2001 | Simulation with learning agents · Proc. IEEE 2001 |
Knowledge, reasoning and agents › Multi-agent systems
learning agents |
0.0 | 1 | 2001 | Simulation with learning agents · Proc. IEEE 2001 |
Knowledge, reasoning and agents › Multi-agent systems
agent-based simulation |
0.0 | 1 | 2001 | Simulation with learning agents · Proc. IEEE 2001 |
Methods — techniques the papers use, named apart from their topics
reinforcement learning · 0.0neural network · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2001 | Simulation with learning agentsabstractWe propose that learning agents (LAs) be incorporated into simulation environments in order to model the adaptive behavior of humans. These LAs adapt to specific circumstances and events during the simulation run. They would select tasks to be accomplished among a given set of tasks as the simulation progresses, or synthesize tasks for themselves based on their observations of the environment and on information they may receive from other agents. We investigate an approach in which agents are assigned goals when the simulation starts and then pursue these goals autonomously and adaptively. During the simulation, agents progressively improve their ability to accomplish their goals effectively and safely. Agents learn from their own observations and from the experience of other agents with whom they exchange information. Each LA starts with a given representation of the simulation environment from which it progressively constructs its own internal representation and uses it to make decisions. The paper describes how learning neural networks can support this approach and shows that goal based learning may be used effectively used in this context. An example simulation is presented in which agents represent manned vehicles; they are assigned the goal of traversing a dangerous metropolitan grid safely and rapidly using goal based reinforcement learning with neural networks and compared to three other algorithms. Erol Gelenbe, Esin Seref, Zhiguang Xu |
Proc. IEEE | 2 |
| 1999 | Cognitive Packet NetworksabstractWe propose cognitive packet networks (CPN) in which intelligent capabilities for routing and flow control are concentrated in the packets, rather than in the nodes and protocols. Cognitive packets within a CPN route themselves. They are assigned goals before entering the network and pursue these goals adaptively. Cognitive packets learn from their own observations about the network and from the experience of other packets with whom they exchange information via mailboxes. Cognitive packets rely minimally on routers. This paper describes CPN and shows how learning can support intelligent behavior of cognitive packets. Erol Gelenbe, Zhiguang Xu, Esin Seref |
ICTAI | 3 |