7,316 research outputs found
Role and Discipline Relationships in a Transdisciplinary Biomedical Team: Structuration, Values Override and Context Scaffolding
Though accepted that "team science" is needed to tackle and conquer the
health problems that are plaguing our society significant empirical evidence of
team mechanisms and functional dynamics is still lacking in abundance. Through
grounded methods the relationship between scientific disciplines and team roles
was observed in a United States National Institutes of Health-funded (NIH)
research consortium. Interviews and the Organizational Culture Assessment
Instrument (OCAI) were employed.. Findings show strong role and discipline
idiosyncrasies that when viewed separately provide different insights into team
functioning and change receptivity. When considered simultaneously,
value-latent characteristics emerged showing self-perceived contributions to
the team. This micro/meso analysis suggests that individual participation in
team level interactions can inform the structuration of roles and disciplines
in an attempt to tackle macro level problems.Comment: Presented at COINs13 Conference, Chile, 2013 (arxiv:1308.1028
Goals are Enough: Inducing AdHoc cooperation among unseen Multi-Agent systems in IMFs
Intent-based management will play a critical role in achieving customers'
expectations in the next-generation mobile networks. Traditional methods cannot
perform efficient resource management since they tend to handle each
expectation independently. Existing approaches, e.g., based on multi-agent
reinforcement learning (MARL) allocate resources in an efficient fashion when
there are conflicting expectations on the network slice. However, in reality,
systems are often far more complex to be addressed by a standalone MARL
formulation. Often there exists a hierarchical structure of intent fulfilment
where multiple pre-trained, self-interested agents may need to be further
orchestrated by a supervisor or controller agent. Such agents may arrive in the
system adhoc, which then needs to be orchestrated along with other available
agents. Retraining the whole system every time is often infeasible given the
associated time and cost. Given the challenges, such adhoc coordination of
pre-trained systems could be achieved through an intelligent supervisor agent
which incentivizes pre-trained RL/MARL agents through sets of dynamic contracts
(goals or bonuses) and encourages them to act as a cohesive unit towards
fulfilling a global expectation. Some approaches use a rule-based supervisor
agent and deploy the hierarchical constituent agents sequentially, based on
human-coded rules.
In the current work, we propose a framework whereby pre-trained agents can be
orchestrated in parallel leveraging an AI-based supervisor agent. For this, we
propose to use Adhoc-Teaming approaches which assign optimal goals to the MARL
agents and incentivize them to exhibit certain desired behaviours. Results on
the network emulator show that the proposed approach results in faster and
improved fulfilment of expectations when compared to rule-based approaches and
even generalizes to changes in environments.Comment: Accepted for publication in IEEE CCNC 2024 conferenc
Human-agent collectives
We live in a world where a host of computer systems, distributed throughout our physical and information environments, are increasingly implicated in our everyday actions. Computer technologies impact all aspects of our lives and our relationship with the digital has fundamentally altered as computers have moved out of the workplace and away from the desktop. Networked computers, tablets, phones and personal devices are now commonplace, as are an increasingly diverse set of digital devices built into the world around us. Data and information is generated at unprecedented speeds and volumes from an increasingly diverse range of sources. It is then combined in unforeseen ways, limited only by human imagination. People’s activities and collaborations are becoming ever more dependent upon and intertwined with this ubiquitous information substrate. As these trends continue apace, it is becoming apparent that many endeavours involve the symbiotic interleaving of humans and computers. Moreover, the emergence of these close-knit partnerships is inducing profound change. Rather than issuing instructions to passive machines that wait until they are asked before doing anything, we will work in tandem with highly inter-connected computational components that act autonomously and intelligently (aka agents). As a consequence, greater attention needs to be given to the balance of control between people and machines. In many situations, humans will be in charge and agents will predominantly act in a supporting role. In other cases, however, the agents will be in control and humans will play the supporting role. We term this emerging class of systems human-agent collectives (HACs) to reflect the close partnership and the flexible social interactions between the humans and the computers. As well as exhibiting increased autonomy, such systems will be inherently open and social. This means the participants will need to continually and flexibly establish and manage a range of social relationships. Thus, depending on the task at hand, different constellations of people, resources, and information will need to come together, operate in a coordinated fashion, and then disband. The openness and presence of many distinct stakeholders means participation will be motivated by a broad range of incentives rather than diktat. This article outlines the key research challenges involved in developing a comprehensive understanding of HACs. To illuminate this agenda, a nascent application in the domain of disaster response is presented
A Persistent Simulation Environment for Autonomous Systems
The age of Autonomous Unmanned Aircraft Systems (AUAS) is creating new challenges for the accreditation and certification requiring new standards, policies and procedures that sanction whether a UAS is safe to fly. Establishing a basis for certification of autonomous systems via research into trust and trustworthiness is the focus of Autonomy Teaming and TRAjectories for Complex Trusted Operational Reliability (ATTRACTOR), a new NASA Convergent Aeronautics Solution (CAS) project. Simulation Environments to test and evaluate AUAS decision making may be a low-cost solution to help certify that various AUAS systems are trustworthy enough to be allowed to fly in current general and commercial aviation airspace. NASA is working to build a peer-to-peer persistent simulation (P3 Sim) environment. The P3 Sim will be a Massively Multiplayer Online (MMO) environment were AUAS avatars can interact with a complex dynamic environment and each other. The focus of the effort is to provide AUAS researchers a low-cost intuitive testing environment that will aid training for and assessment of decisions made by autonomous systems such as AUAS. This presentation focuses on the design approach and challenges faced in development of the P3 Sim Environment is support of investigating trustworthiness of autonomous systems
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