Becoming Team Members: Identifying Interaction Patterns of Mutual Adaptation for Human-Robot Co-Learning

Becoming a well-functioning team requires continuous collaborative learning by all team members. This is called co-learning, conceptualized in this paper as comprising two alternating iterative stages: partners adapting their behavior to the task and to each other (co-adaptation), and partners sustaining successful behavior through communication. This paper focuses on the first stage in human-robot teams, aiming at a method for the identification of recurring behaviors that indicate co-learning. Studying this requires a task context that allows for behavioral adaptation to emerge from the interactions between human and robot. We address the requirements for conducting research into co-adaptation by a human-robot team, and designed a simplified computer simulation of an urban search and rescue task accordingly. A human participant and a virtual robot were instructed to discover how to collaboratively free victims from the rubbles of an earthquake. The virtual robot was designed to be able to real-time learn which actions best contributed to good team performance. The interactions between human participants and robots were recorded. The observations revealed patterns of interaction used by human and robot in order to adapt their behavior to the task and to one another. Results therefore show that our task environment enables us to study co-learning, and suggest that more participant adaptation improved robot learning and thus team level learning. The identified interaction patterns can emerge in similar task contexts, forming a first description and analysis method for co-learning. Moreover, the identification of interaction patterns support awareness among team members, providing the foundation for human-robot communication about the co-adaptation (i.e., the second stage of co-learning). Future research will focus on these human-robot communication processes for co-learning.Interactive Intelligenc

Ontology-Based Reflective Communication for Shared Human-AI Recognition of Emergent Collaboration Patterns

When humans and AI-agents collaborate, they need to continuously learn about each other and the task. We propose a Team Design Pattern that utilizes adaptivity in the behavior of human and agent team partners, causing new Collaboration Patterns to emerge. Human-AI Co-Learning takes place when partners can formalize recognized patterns of collaboration in a commonly shared language, and can communicate with each other about these patterns. For this, we developed an ontology of Collaboration Patterns. An accompanying Graphical User Interface (GUI) enables partners to formalize and refine Collaboration Patterns, which can then be communicated to the partner. The ontology was evaluated empirically with human participants who viewed video recordings of joint human-agent activities. Participants were requested to identify Collaboration Patterns in the footage, and to formalize patterns by using the ontology’s GUI. Results show that the ontology supports humans to recognize and define Collaboration Patterns successfully. To improve the ontology, it is suggested to include pre- and post-conditions of tasks, as well as parallel actions of team members.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.BUS/TNO STAFFInteractive IntelligenceHuman-Robot Interactio

Myeloid-related protein-14 contributes to protective immunity in gram-negative pneumonia derived sepsis

Contains fulltext : 108788.pdf (publisher's version ) (Open Access)Klebsiella (K.) pneumoniae is a common cause of pneumonia-derived sepsis. Myeloid related protein 8 (MRP8, S100A8) and MRP14 (S100A9) are the most abundant cytoplasmic proteins in neutrophils. They can form MRP8/14 heterodimers that are released upon cell stress stimuli. MRP8/14 reportedly exerts antimicrobial activity, but in acute fulminant sepsis models MRP8/14 has been found to contribute to organ damage and death. We here determined the role of MRP8/14 in K. pneumoniae sepsis originating from the lungs, using an established model characterized by gradual growth of bacteria with subsequent dissemination. Infection resulted in gradually increasing MRP8/14 levels in lungs and plasma. Mrp14 deficient (mrp14(-/-)) mice, unable to form MRP8/14 heterodimers, showed enhanced bacterial dissemination accompanied by increased organ damage and a reduced survival. Mrp14(-/-) macrophages were reduced in their capacity to phagocytose Klebsiella. In addition, recombinant MRP8/14 heterodimers, but not MRP8 or MRP14 alone, prevented growth of Klebsiella in vitro through chelation of divalent cations. Neutrophil extracellular traps (NETs) prepared from wildtype but not from mrp14(-/-) neutrophils inhibited Klebsiella growth; in accordance, the capacity of human NETs to kill Klebsiella was strongly impaired by an anti-MRP14 antibody or the addition of zinc. These results identify MRP8/14 as key player in protective innate immunity during Klebsiella pneumonia

Design patterns for human-AI co-learning: A wizard-of-Oz evaluation in an urban-search-and-rescue task

The rapid advancement of technology empowered by artificial intelligence is believed to intensify the collaboration between humans and AI as team partners. Successful collaboration requires partners to learn about each other and about the task. This human-AI co-learning can be achieved by presenting situations that enable partners to share knowledge and experiences. In this paper we describe the development and implementation of a task context and procedures for studying co-learning. More specifically, we designed specific sequences of interactions that aim to initiate and facilitate the co-learning process. The effects of these interventions on learning were evaluated in an experiment, using a simplified virtual urban-search-and-rescue task for a human-robot team. The human participants performed a victim rescue- and evacuation mission in collaboration with a wizard-of-Oz (i.e., a confederate of the experimenter who executed the robot-behavior consistent with an ontology-based AI-model). The designed interaction sequences, formulated as Learning Design Patterns (LDPs), were intended to bring about co-learning. Results show that LDPs support the humans understanding and awareness of their robot partner and of the teamwork. No effects were found on collaboration fluency, nor on team performance. Results are used to discuss the importance of co-learning, the challenges of designing human-AI team tasks for research into this phenomenon, and the conditions under which co-learning is likely to be successful. The study contributes to our understanding of how humans learn with and from AI-partners, and our propositions for designing intentional learning (LDPs) provide directions for applications in future human-AI teams.Interactive Intelligenc

Receptor for advanced glycation end products is protective during murine tuberculosis.

Item does not contain fulltextThe development of active tuberculosis after infection with Mycobacterium tuberculosis is almost invariably associated with a persistent or transient state of relative immunodeficiency. The receptor for advanced glycation end products (RAGE) is a promiscuous receptor that is involved in pulmonary inflammation and infection. To investigate the role of RAGE in tuberculosis, we intranasally infected wild-type (Wt) and RAGE deficient (RAGE(-/-)) mice with live virulent M. tuberculosis. While lungs of uninfected Wt mice expressed RAGE, in particular on endothelium, M. tuberculosis pneumonia was associated with an enhanced pulmonary expression of RAGE. Lung inflammation was increased in RAGE(-/-) mice, as indicated by histopathology, percentage of inflamed area, lung weight and cytokine and chemokine levels. In addition, lung lymphocyte and neutrophil numbers were increased in the RAGE(-/-) mice. RAGE(-/-) mice had modestly higher mycobacterial loads in the lungs after 3 weeks but not after 6 weeks of infection. Moreover, RAGE(-/-) mice displayed more body weight loss and enhanced mortality. In summary, pulmonary RAGE expression is increased during tuberculosis. In addition, these data suggest that RAGE plays a beneficial role in the host response to pulmonary tuberculosis.1 oktober 201

Identifying Interaction Patterns of Tangible Co-Adaptations in Human-Robot Team Behaviors

As robots become more ubiquitous, they will increasingly need to behave as our team partners and smoothly adapt to the (adaptive) human team behaviors to establish successful patterns of collaboration over time. A substantial amount of adaptations present themselves through subtle and unconscious interactions, which are difficult to observe. Our research aims to bring about awareness of co-adaptation that enables team learning. This paper presents an experimental paradigm that uses a physical human-robot collaborative task environment to explore emergent human-robot co-adaptions and derive the interaction patterns (i.e., the targeted awareness of co-adaptation). The paradigm provides a tangible human-robot interaction (i.e., a leash) that facilitates the expression of unconscious adaptations, such as “leading” (e.g., pulling the leash) and “following” (e.g., letting go of the leash) in a search-and-navigation task. The task was executed by 18 participants, after which we systematically annotated videos of their behavior. We discovered that their interactions could be described by four types of adaptive interactions: stable situations, sudden adaptations, gradual adaptations and active negotiations. From these types of interactions we have created a language of interaction patterns that can be used to describe tacit co-adaptation in human-robot collaborative contexts. This language can be used to enable communication between collaborating humans and robots in future studies, to let them share what they learned and support them in becoming aware of their implicit adaptations.Interactive Intelligenc