A Framework for Engineering Human/Agent Teaming Systems

Evertsz, Ricardo; Thangarajah, John

doi:10.1609/aaai.v34i03.5629

Cited by 5 publications

(3 citation statements)

References 6 publications

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“…TDF [15,16] is the successor to Prometheus [25], a mature and popular AOSE methodology. A pilot study has shown that TDF significantly improves comprehension of behaviour models, compared to UML [18].…”

Section: Tdf and Sarlmentioning

confidence: 99%

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User and System Stories: an agile approach for managing requirements in AOSE

Rodríguez¹,

Thangarajah²,

Winikoff³

2021

Preprint

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Section: Tdf and Sarlmentioning

confidence: 99%

“…Most agent design methodologies support the translation from design to implementation. The detailed agent diagrams in TDF are readily translated to implementation constructs in agent implementation languages such as JACK [17] and SARL [15].…”

Section: System Story Mappings and Traceabilitymentioning

confidence: 99%

User and System Stories: an agile approach for managing requirements in AOSE

Rodríguez¹,

Thangarajah²,

Winikoff³

2021

Preprint

Self Cite

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“…For instance, BO-MUSE employs Bayesian methods to optimize the experimental design involving human-AI teams (Gupta et al 2023). Evertsz and Thangarajah employ TDF-T diagrams to engineer humanagent teams and provide context-specific team cognition to human team members during the runtime of the StarCraft strategy game (Evertsz and Thangarajah 2020).…”

Section: Introductionmentioning

confidence: 99%

RL-HAT: A New Framework for Understanding Human-Agent Teaming

Jafari Meimandi,

Bolton,

Beling

2023

AAAI-SS

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This paper presents a novel framework for human-agent teaming grounded in the principles of Reinforcement Learning (RL). Recognizing the need for a unified language across various disciplines, we utilize RL concepts to provide a standard for the understanding and evaluation of diverse teaming strategies. Our framework extends beyond traditional RL constructs, integrating aspects such as belief states, prior knowledge, social considerations, situational awareness, and mental models. A particular focus is placed on the role of ethics and trust in effective teaming. Additionally, we discuss how sensor data, perception models, and actuator modules can be incorporated, emphasizing the adaptability of our framework to a broad range of tasks and environments. We believe this work forms a substantial contribution to the field of human-agent teaming, establishing a solid foundation for future research and application.

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Dynamic role discovery and assignment in multi-agent task decomposition

Xia

Zhu

2023

Complex Intell. Syst.

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Effective multi-agent teamwork can be facilitated by using personas to decompose goals into lower-level team subtasks through a shared understanding of multi-agent tasks. However, traditional methods for role discovery and assignment are not scalable and fail to adapt to dynamic changes in the environment. To solve this problem, we propose a new framework for learning dynamic role discovery and assignment. We first introduce an action encoder to construct a vector representation for each action based on its characteristics, and define and classify roles from a more comprehensive perspective based on both action differences and action contributions. To rationally assign roles to agents, we propose a representation-based role selection policy based on consideration of role differences and reward horizons, which enables agents to play roles dynamically by dynamically assigning agents with similar abilities to play the same role. Agents playing the same role share their learning of the role, and different roles correspond to different action spaces. We also introduce regularizers to increase the differences between roles and stabilize training by preventing agents from changing roles frequently. Role selection and role policy integrate action representations and role differences in a restricted action space, improving learning efficiency. We conducted experiments in the SMAC benchmark and showed that our method enables effective role discovery and assignment, outperforming the baseline on four of the six scenarios, with an average improvement in win rate of 20$$\%$$ % , and is effective in hard and super hard maps. We also conduct ablation experiments to demonstrate the importance of each component in our approach.

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A Framework for Engineering Human/Agent Teaming Systems

Cited by 5 publications

References 6 publications

User and System Stories: an agile approach for managing requirements in AOSE

User and System Stories: an agile approach for managing requirements in AOSE

RL-HAT: A New Framework for Understanding Human-Agent Teaming

Dynamic role discovery and assignment in multi-agent task decomposition

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