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Rick Evertsz

dblp:77/4210 · DBLP profile ↗
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7ranked-venue papers
6as first author
0since 2021 · last 2020
0000-0001-7849-2947ORCID · corroborated

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

Artificial intelligence and machine learning · 5 · 4 first-authorGraphics, computer vision, multimedia, augmented reality and games · 4 · 3 first-authorSoftware engineering, systems software and programming languages · 1 · 1 first-authorDatabases, data management, data science and information retrieval · 1 · 1 first-authorApplied, interdisciplinary, general and emerging computing · 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.

Software engineering, system software, and programming languages
2 papers
Requirements engineering and software design · 100% Program analysis · 0%
Human-computer interaction and pervasive computing
1 paper
Human-AI interaction · 100%
Artificial intelligence
3 papers
Multi-agent systems · 90% Knowledge representation and reasoning · 10%

Topics — the 4 heaviest of 7, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Human-AI interaction
human-AI collaboration
0.412020
A Framework for Engineering Human/Agent Teaming Systems · AAAI 2020
Knowledge, reasoning and agents › Multi-agent systems › multi-agent coordination
team coordination
0.112020
A Framework for Engineering Human/Agent Teaming Systems · AAAI 2020
Knowledge, reasoning and agents › Knowledge representation and reasoning › knowledge-based systems
rule-based systems
0.011991
The Automated Analysis of Rule-based Systems, Based on their Procedural Semantics · IJCAI 1991
Computing education
student modeling
0.011989
The Generation of 'Critical Problems' by Abstract Interpretations of Student Models · IJCAI 1989

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

user study · 1.3TDF-T diagrams · 1.3abstract interpretation · 0.0
YearPublicationVenuePosition
2020 A Framework for Engineering Human/Agent Teaming Systems
abstract
The increasing capabilities of autonomous systems offer the potential for more effective teaming with humans. Effective human/agent teaming is facilitated by a mutual understanding of the team objective and how that objective is decomposed into team roles. This paper presents a framework for engineering human/agent teams that delineates the key human/agent teaming components, using TDF-T diagrams to design the agents/teams and then present contextualised team cognition to the human team members at runtime. Our hypothesis is that this facilitates effective human/agent teaming by enhancing the human's understanding of their role in the team and their coordination requirements. To evaluate this hypothesis we conducted a study with human participants using our user interface for the StarCraft strategy game, which presents pertinent, instantiated TDF-T diagrams to the human at runtime. The performance of human participants in the study indicates that their ability to work in concert with the non-player characters in the game is significantly enhanced by the timely presentation of a diagrammatic representation of team cognition.
Rick Evertsz, John Thangarajah
AAAI1
2017 The Conceptual Modelling of Dynamic Teams for Autonomous Systems
Rick Evertsz, John Thangarajah, Michael Papasimeon
ER1
2015 A framework for modelling tactical decision-making in autonomous systems
Rick Evertsz, John Thangarajah, Nitin Yadav, Thanh Ly
J. Syst. Softw.1
1991 The Development of SYLLABUS--An Interactive, Constraint-Based Scheduler for Schools and Colleges
Rick Evertsz
IAAI1
1991 The Automated Analysis of Rule-based Systems, Based on their Procedural Semantics
Rick Evertsz
IJCAI1
1989 The Generation of 'Critical Problems' by Abstract Interpretations of Student Models
Rick Evertsz
IJCAI1
1984 Novice Physics Problem Solving Behaviour
Eileen Scanlon, C. Hawkridge, Rick Evertsz
ECAI3