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Awrad Mohammed Ali

dblp:160/9940 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Knowledge, reasoning and agents › Multi-agent systems
agent architecture
0.212015
Cognitive Social Learners: An Architecture for Modeling Normative Behavior · AAAI 2015
Knowledge, reasoning and agents › Multi-agent systems › agent architecture
BDI architecture
0.212015
Cognitive Social Learners: An Architecture for Modeling Normative Behavior · AAAI 2015
Knowledge, reasoning and agents › Multi-agent systems
normative multi-agent systems
0.212015
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
0.212015
Cognitive Social Learners: An Architecture for Modeling Normative Behavior · AAAI 2015
Computational social science and digital humanities
social simulation
0.112015
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
YearPublicationVenuePosition
2023 Machine learning from casual conversation
Awrad Mohammed Ali, Avelino J. Gonzalez
Mach. Learn.1
2015 Cognitive Social Learners: An Architecture for Modeling Normative Behavior
abstract
In 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
AAAI2