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	1	+Team Design Patterns (TDPs) are applied to the problem of developing Human-Agent/Robot-Teams and can be defined as a description of generic reusable behaviours of actors of effective and resilient teamwork.
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	3	+**Hybrid (human-AI) disease management**
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	5	+Van Stijn et al. (2021) present Team Design Patterns for mitigating harmful biases in machine learning algorithms, based on the research and development of hybrid intelligence (HI) system for diabetes management. It was shown that TDPs are a useful method to unambiguously describe solutions for diverse HI design problems with a moral component on varying abstraction levels, usable by a heterogeneous group of multidisciplinary researchers.
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	7	+* Van Stijn, J.J., Neerincx, M.A., ten Teije, A.T., and Vethman, S. (2021). Team Design Patterns for Moral Decisions in Hybrid Intelligent Systems: A Case Study of Bias Mitigation. AAAI 2021 Spring Symposium on Combining Machine Learning and Knowledge Engineering (AAAI-MAKE 2021)
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	9	+**Allocation of tasks and responsibilities in HART**
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	11	+Van der Waa et al. (2020) provide some high-level patterns for the allocation of moral decision-making in human-agent/robot teams (HART):
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	13	+* van der Waa, J., van Diggelen, J., Siebert, L. C., Neerincx, M., & Jonker, C. (2020). Allocation of Moral Decision-Making in Human-Agent Teams: A Pattern Approach. In //International Conference on Human-Computer Interaction// (pp. 203-220). Springer, Cham.
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	15	+Diggelen et al. (2018) provide a team design pattern language that can be used for the development of artificial team members (e.g. robots, avatars). These patterns can be constructive or destructive, applying to an individual team member or the overall team. The patterns are defined with reference to a team ontology. Examples from the Space and Railway domains provide some example patterns (e.g. for local and global problem solving via a huddle, and for after-action team reflection via a Resiliencer display).
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	17	+* van Diggelen, J., Neerincx, M., Peeters, M., & Schraagen, J. M. (2018). Developing effective and resilient human-agent teamwork using team design patterns. //IEEE intelligent systems//, //34//(2), 15-24.
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	19	+**Robot-assisted disaster response teams**
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	21	+The design patterns of Neerincx et al. (2016) for this domain, did not (yet) make a distinction between Team and Interaction Design Patterns, their descriptions provide a mix of them. The Design Patterns address the mutual and context-dependent behaviours of the human and agent/robot team members during disaster response missions. The pattern descriptions capture four key concepts: The**// Actor//** can be //Human//, //Agent// or //Robot//, the **//Relationship//** between actors can be //Supervisory// and/or //Collaborative//, actors can perform their work at the //Same// (co-location) or a //Distant// (distributed) **//Location//**, and the Pattern status can be //Proto// (i.e., in construction) or //Grounded// (e.g., empirically validated in an experiment). Three example patterns are provided. The first example centers on obtaining adjustable working agreements between humans and agents to establish flexible and adaptive teamwork, supporting dynamic and adaptive human-agent (sub)task allocation. For specific work contexts, the human can set agreements with the agent on how the tasks will be allocated. The second design pattern centers on the demand for an operator to stay in the vicinity of the workstation when an event or situation may appear that will ask for immediate action. The third pattern focuses on the management of multiple interactions between humans and agents. The patterns are presented in:
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	23	+* Neerincx, M. A., van Diggelen, J., & van Breda, L. (2016). Interaction design patterns for adaptive human-agent-robot teamwork in high-risk domains. In //International conference on engineering psychology and cognitive ergonomics// (pp. 211-220). Springer, Cham.
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	25	+**Co-Learning patterns**
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	27	+Van Zoelen et al. (2021) are developing a method for the generation of "human-robot co-learning patterns" out of successful recurring behaviors in joint human-robot performances:
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	29	+Van Zoelen, E. M., Van Den Bosch, K., & Neerincx, M. (2021). Becoming team members: identifying interaction patterns of mutual adaptation for human-robot co-learning. //Frontiers in Robotics and AI//, //8//.