Demo Data from EDBT 2026 / reviewers in the wild / expert
Awrad Mohammed Ali
dblp:160/9940
· DBLP profile ↗
2ranked-venue papers
1as first author
1since 2021 · last 2023
0000-0002-2971-5363ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 2 · 1 first-author · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 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 · 100% | |
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Computational social science and digital humanities · 100% |
Topics — the 5 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Knowledge, reasoning and agents › Multi-agent systems
agent architecture |
0.2 | 1 | 2015 | Cognitive Social Learners: An Architecture for Modeling Normative Behavior · AAAI 2015 |
Knowledge, reasoning and agents › Multi-agent systems › agent architecture
BDI architecture |
0.2 | 1 | 2015 | Cognitive Social Learners: An Architecture for Modeling Normative Behavior · AAAI 2015 |
Knowledge, reasoning and agents › Multi-agent systems
normative multi-agent systems |
0.2 | 1 | 2015 | Cognitive Social Learners: An Architecture for Modeling Normative Behavior · AAAI 2015 |
Knowledge, reasoning and agents › Multi-agent systems › normative multi-agent systems › social norms
norm emergence |
0.2 | 1 | 2015 | Cognitive Social Learners: An Architecture for Modeling Normative Behavior · AAAI 2015 |
Computational social science and digital humanities
social simulation |
0.1 | 1 | 2015 | Cognitive Social Learners: An Architecture for Modeling Normative Behavior · AAAI 2015 |
Methods — techniques the papers use, named apart from their topics
social learning · 0.4agent-based simulation · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | Machine learning from casual conversation
Awrad Mohammed Ali, Avelino J. Gonzalez |
Mach. Learn. | 1 |
| 2015 | Cognitive Social Learners: An Architecture for Modeling Normative BehaviorabstractIn many cases, creating long-term solutions to sustainability issues requires not only innovative technology, but also large-scale public adoption of the proposed solutions. Social simulations are a valuable but underutilized tool that can help public policy researchers understand when sustainable practices are likely to make the delicate transition from being an individual choice to becoming a social norm. In this paper, we introduce a new normative multi-agent architecture, Cognitive Social Learners (CSL), that models bottom-up norm emergence through a social learning mechanism, while using BDI (Belief/Desire/Intention) reasoning to handle adoption and compliance. CSL preserves a greater sense of cognitive realism than influence propagation or infectious transmission approaches, enabling the modeling of complex beliefs and contradictory objectives within an agent-based simulation. In this paper, we demonstrate the use of CSL for modeling norm emergence of recycling practices and public participation in a smoke-free campus initiative. Rahmatollah Beheshti, Awrad Mohammed Ali, Gita Reese Sukthankar |
AAAI | 2 |