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Decision making in open agent systems
In many real‐world applications of AI, the set of actors and tasks are not constant, but
instead change over time. Robots tasked with suppressing wildfires eventually run out of …
instead change over time. Robots tasked with suppressing wildfires eventually run out of …
Efficient Human-AI Coordination via Preparatory Language-based Convention
Mutual theory of mind in human-ai collaboration: An empirical study with llm-driven ai agents in a real-time shared workspace task
Theory of Mind (ToM) significantly impacts human collaboration and communication as a
crucial capability to understand others. When AI agents with ToM capability collaborate with …
crucial capability to understand others. When AI agents with ToM capability collaborate with …
[PDF][PDF] Multi-objective Optimization-based Selection for Quality-Diversity by Non-surrounded-dominated Sorting.
Abstract Quality-Diversity (QD) algorithms, a subset of evolutionary algorithms, maintain an
archive (ie, a set of solutions) and simulate the natural evolution process through iterative …
archive (ie, a set of solutions) and simulate the natural evolution process through iterative …
Open Human-Robot Collaboration Systems (OHRCS): A Research Perspective
Human-robot collaboration (HRC) is the paradigm of humans and robots working
synergistically in a shared workspace toward common goals. Prior research models such …
synergistically in a shared workspace toward common goals. Prior research models such …
Fast peer adaptation with context-aware exploration
Fast adapting to unknown peers (partners or opponents) with different strategies is a key
challenge in multi-agent games. To do so, it is crucial for the agent to probe and identify the …
challenge in multi-agent games. To do so, it is crucial for the agent to probe and identify the …
Few is More: Task-Efficient Skill-Discovery for Multi-Task Offline Multi-Agent Reinforcement Learning
As a data-driven approach, offline MARL learns superior policies solely from offline datasets,
ideal for domains rich in historical data but with high interaction costs and risks. However …
ideal for domains rich in historical data but with high interaction costs and risks. However …
Beyond Single Stationary Policies: Meta-Task Players as Naturally Superior Collaborators
In human-AI collaborative tasks, the distribution of human behavior, influenced by mental
models, is non-stationary, manifesting in various levels of initiative and different …
models, is non-stationary, manifesting in various levels of initiative and different …
Opponent Transformer: Modeling Opponent Policies as a Sequence Problem
C Wallace, U Siddique, Y Cao - Coordination and Cooperation for Multi … - openreview.net
The ability of an agent to understand the intentions of others in a multi-agent system, also
called opponent modeling, is critical for the design of effective local control policies. One …
called opponent modeling, is critical for the design of effective local control policies. One …