ΑΙhub.org
 

Agent Teaming in Mixed-Motive Situations – an AAAI Fall symposium

by
08 January 2024



share this:

Image by Jamillah Knowles & Reset.Tech Australia / © https://au.reset.tech/ / Better Images of AI / Detail from Connected People / Licenced by CC-BY 4.0

The AAAI Symposium on Agent Teaming in Mixed-Motive Situations, held from October 25-27, 2023, showcased the challenges and innovations in multi-agent interactions with varying goals and decision-making processes. The event featured experts from diverse backgrounds, including multi-agent systems, AI, and organizational behavior. Key highlights include:

  • Professor Subbarao Khambhampati’s (Arizona State University) keynote discussed the dual nature of mental modeling in cooperation and competition. The importance of obfuscatory behavior, controlled observability planning, and the use of explanations for model reconciliation was emphasized, particularly regarding trust-building in human-robot interactions.
  • Professor Gita Sukthankar’s (University of Central Florida) talk focused on challenges in teamwork, using a case study on software engineering teams. Innovative techniques for distinguishing effective teams from ineffective ones were explored, setting the stage for discussions on the complexities of mixed-motive scenarios.
  • Dr Marc Steinberg (Office of Naval Research) moderated an interactive discussion exploring research challenges in mixed-motive teams, including modeling humans, experimental setups, and measuring and assessing mixed-motive situations. This discussion provided diverse perspectives on the evolving landscape of agent teaming.
  • Accepted papers covered a wide range of topics, including maximum entropy reinforcement learning, multi-agent path finding, Bayesian inverse planning for communication scenarios, hybrid navigation acceptability, and safety. Talks also delved into challenges in human-robot teams and the importance of aligning robot values with human preferences.
  • Panel sessions explored themes such as team structure, collaboration within diverse teams, the role of game theory, and explicit and implicit communication within teams. Meta-level parameters for multi-agent collaboration and the importance of context in human-agent communication in mixed-motive settings were discussed.
  • Breakout group discussions focused on consensus and negotiation in mixed-motive groups, considering intragroup and intergroup dynamics. The impact of consensus on trust and future work in mixed-motive teaming, including interdisciplinary collaborations and resource identification, were explored.
  • The symposium successfully brought together a community actively addressing challenges in agent teaming within mixed-motive situations. The discussions highlighted the complexities of collaboration, trust-building, and decision-making in diverse multi-agent scenarios. Ongoing research and continued collaboration were emphasized to advance understanding in this field.

Useful links



tags: ,


Suresh Kumaar Jayaraman is a postdoctoral researcher at the Robotics Institute at Carnegie Mellon University.
Suresh Kumaar Jayaraman is a postdoctoral researcher at the Robotics Institute at Carnegie Mellon University.




            AIhub is supported by:


Related posts :



AIhub coffee corner: Open vs closed science

The AIhub coffee corner captures the musings of AI experts over a short conversation.
26 April 2024, by

Are emergent abilities of large language models a mirage? – Interview with Brando Miranda

We hear about work that won a NeurIPS 2023 outstanding paper award.
25 April 2024, by

We built an AI tool to help set priorities for conservation in Madagascar: what we found

Daniele Silvestro has developed a tool that can help identify conservation and restoration priorities.
24 April 2024, by

Interview with Mike Lee: Communicating AI decision-making through demonstrations

We hear from AAAI/SIGAI Doctoral Consortium participant Mike Lee about his research on explainable AI.
23 April 2024, by

Machine learning viability modelling of vertical-axis wind turbines

Researchers have used a genetic learning algorithm to identify optimal pitch profiles for the turbine blades.
22 April 2024, by

The Machine Ethics podcast: What is AI? Volume 3

This is a bonus episode looking back over answers to our question: What is AI?
19 April 2024, by




AIhub is supported by:






©2024 - Association for the Understanding of Artificial Intelligence


 












©2021 - ROBOTS Association