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Jeremy Kong

dblp:199/6472 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 2017
—ORCID · none

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

Artificial intelligence and machine learning · 1 · 1 first-authorGraphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author

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 · 77% Logic in computer science · 23%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Automated reasoning and model checking › model checking
multi-agent system verification
0.312017
Model Checking Multi-Agent Systems against LDLK Specifications · IJCAI 2017
Logic in computer science
epistemic logic
0.112017
Model Checking Multi-Agent Systems against LDLK Specifications · IJCAI 2017

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

symbolic model checking · 0.3alternating automata · 0.3OBDD · 0.3
YearPublicationVenuePosition
2017 Model Checking Multi-Agent Systems against LDLK Specifications
abstract
We define the logic LDLK, a formalism for specifying multi-agent systems. LDLK extends LDL with epistemic modalities, including common knowledge, for reasoning about the evolution of knowledge states of the agents in the system. We study the complexity of verifying a multi-agent system against LDLK specifications and show this to be in PSPACE. We give an algorithm for the practical verification of multi-agent systems specified in LDLK. We show that the model checking algorithm, based on alternating-automata and nFA, is amenable to symbolic implementation on OBDDs. We introduce MCMAS LDLK , an extension of the open-source model checker MCMAS, implementing the algorithm and discuss the experimental results obtained.
Jeremy Kong, Alessio Lomuscio
IJCAI1