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EDBT 2026 Demo / reviewers in the wild / expert

Alex B. Jose

dblp:135/8414 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2013
—ORCID · none

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

Artificial intelligence and machine learning · 1Systems, architecture and hardware · 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.

Theoretical computer science
1 paper
Automated reasoning and model checking · 100%
Artificial intelligence
1 paper
Robot navigation and mapping · 50% Video understanding and tracking · 50%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Automated reasoning and model checking › reactive synthesis
GR(1) synthesis		 0.212013
Patching task-level robot controllers based on a local μ-calculus formula · ICRA 2013
Automated reasoning and model checking
reactive synthesis		 0.212013
Patching task-level robot controllers based on a local μ-calculus formula · ICRA 2013
Automated reasoning and model checking › synthesis
temporal logic synthesis		 0.212013
Patching task-level robot controllers based on a local μ-calculus formula · ICRA 2013
Robotics › Robot navigation and mapping
mobile robot navigation		 0.012013
Patching task-level robot controllers based on a local μ-calculus formula · ICRA 2013
Computer vision › Video understanding and tracking
video surveillance		 0.012013
Patching task-level robot controllers based on a local μ-calculus formula · ICRA 2013

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

μ-calculus · 0.3local strategy computation · 0.3
YearPublicationVenuePosition
2013 Patching task-level robot controllers based on a local μ-calculus formula
abstract
We present a method for mending strategies for GR(1) specifications. Given the addition or removal of edges from the game graph describing a problem (essentially transition rules in a GR(1) specification), we apply a μ-calculus formula to a neighborhood of states to obtain a “local strategy” that navigates around the invalidated parts of an original synthesized strategy. Our method may thus avoid global resynthesis while recovering correctness with respect to the new specification. We illustrate the results both in simulation and on physical hardware for a planar robot surveillance task.
Scott C. Livingston, Pavithra Prabhakar, Alex B. Jose, Richard M. Murray
 ICRA3