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Anas El Kouaiti

dblp:376/8221 · DBLP profile ↗
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2ranked-venue papers
2as first author
2since 2021 · last 2024
—ORCID · none

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

Artificial intelligence and machine learning · 2 · 2 first-author · 2 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
Planning, search and constraint satisfaction · 100%
Interdisciplinary, comprehensive, and emerging computing
1 paper
Smart cities and intelligent transportation · 100%

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

TopicWeightPapersLastEvidence papers
Knowledge, reasoning and agents › Planning, search and constraint satisfaction › plan representation › planning languages
PDDL+
0.812024
PDDL+ Models for Deployable yet Effective Traffic Signal Optimisation · ICAPS 2024
Knowledge, reasoning and agents › Planning, search and constraint satisfaction
plan representation
0.812024
PDDL+ Models for Deployable yet Effective Traffic Signal Optimisation · ICAPS 2024
Smart cities and intelligent transportation › traffic control
traffic signal optimization
0.212024
PDDL+ Models for Deployable yet Effective Traffic Signal Optimisation · ICAPS 2024

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

heuristic search · 1.5
YearPublicationVenuePosition
2024 PDDL+ Models for Deployable yet Effective Traffic Signal Optimisation
abstract
The use of planning techniques in traffic signal optimisation has proven effective in managing unexpected traffic conditions as well as typical traffic patterns. However, significant challenges concerning the deployability of generated signal strategies remain, as existing approaches tend not to consider constraints and features of the actual real-world infrastructure on which they will be implemented. To address this challenge, we introduce a range of PDDL+ models embodying technological requirements as well as insights from domain experts. The proposed models have been extensively tested on historical data using a range of well-known search strategies and heuristics, as well as alternative encodings. Results demonstrate their competitiveness with the state of the art.
Anas El Kouaiti, Francesco Percassi, Alessandro Saetti, Thomas Leo McCluskey, Mauro Vallati
ICAPS1
2024 Deployable Yet Effective Traffic Signal Optimisation via Automated Planning (Extended Abstract)
abstract
The use of planning techniques in traffic signal optimisation has proven effective in managing unexpected traffic conditions as well as typical traffic patterns. However, significant challenges concerning the deployability of generated signal plans remain, as planning systems need to consider constraints and features of the actual real-world infrastructure on which they will be implemented. To address this challenge, we introduce a range of PDDL+ models embodying technological requirements as well as insights from domain experts. The proposed models have been extensively tested on historical data using a range of well-known search strategies and heuristics, as well as alternative encodings. Results demonstrate their competitiveness with the state of the art.
Anas El Kouaiti, Francesco Percassi, Alessandro Saetti, Thomas Leo McCluskey, Mauro Vallati
SOCS1