Papers Human Agent Collaboration
“Human Agent Collaboration” 태그가 달린 논문 22편 · 필터 해제
Embodied AI Agents: Modeling the World
This paper describes our research on AI agents embodied in visual, virtual or physical forms, enabling them to interact with both users and their environments. These agents, which include virtual avatars, wearable device…
Human Agent CollaborationVIDEE: Visual and Interactive Decomposition, Execution, and Evaluation of Text Analytics with Intelligent Agents
Text analytics has traditionally required specialized knowledge in Natural Language Processing (NLP) or text analysis, which presents a barrier for entry-level analysts. Recent advances in large language models (LLMs) ha…
Human Agent CollaborationInteraction, Process, Infrastructure: A Unified Architecture for Human-Agent Collaboration
As AI tools proliferate across domains, from chatbots and copilots to emerging agents, they increasingly support professional knowledge work. Yet despite their growing capabilities, these systems remain fragmented: they …
Human Agent CollaborationFacilitating Trustworthy Human-Agent Collaboration in LLM-based Multi-Agent System oriented Software Engineering
Multi-agent autonomous systems (MAS) are better at addressing challenges that spans across multiple domains than singular autonomous agents. This holds true within the field of software engineering (SE) as well. The stat…
Human Agent CollaborationA Survey on Large Language Model based Human-Agent Systems
Recent advances in large language models (LLMs) have sparked growing interest in building fully autonomous agents. However, fully autonomous LLM-based agents still face significant challenges, including limited reliabili…
Human Agent CollaborationLanguage ModelingLanguage ModellingLarge Language ModelVeriLA: A Human-Centered Evaluation Framework for Interpretable Verification of LLM Agent Failures
AI practitioners increasingly use large language model (LLM) agents in compound AI systems to solve complex reasoning tasks, these agent executions often fail to meet human standards, leading to errors that compromise th…
Human Agent CollaborationLarge Language ModelOS-Kairos: Adaptive Interaction for MLLM-Powered GUI Agents
Autonomous graphical user interface (GUI) agents powered by multimodal large language models have shown great promise. However, a critical yet underexplored issue persists: over-execution, where the agent executes tasks …
Human Agent CollaborationTableTalk: Scaffolding Spreadsheet Development with a Language Agent
Despite its ubiquity in the workforce, spreadsheet programming remains challenging as programmers need both spreadsheet-specific knowledge (e.g., APIs to write formulas) and problem-solving skills to create complex sprea…
Human Agent CollaborationCollaborative Gym: A Framework for Enabling and Evaluating Human-Agent Collaboration
Recent advancements in language models (LMs) have sparked growing interest in developing LM agents. While fully autonomous agents could excel in many scenarios, numerous use cases inherently require them to collaborate w…
Human Agent CollaborationProactive Agent: Shifting LLM Agents from Reactive Responses to Active Assistance
Agents powered by large language models have shown remarkable abilities in solving complex tasks. However, most agent systems remain reactive, limiting their effectiveness in scenarios requiring foresight and autonomous …
Human Agent CollaborationTowards Explainable Goal Recognition Using Weight of Evidence (WoE): A Human-Centered Approach
Goal recognition (GR) involves inferring an agent's unobserved goal from a sequence of observations. This is a critical problem in AI with diverse applications. Traditionally, GR has been addressed using 'inference to th…
Decision MakingHuman Agent CollaborationSokobanBootstrapping Linear Models for Fast Online Adaptation in Human-Agent Collaboration
Agents that assist people need to have well-initialized policies that can adapt quickly to align with their partners' reward functions. Initializing policies to maximize performance with unknown partners can be achieved …
Human Agent CollaborationImitation LearningregressionEmbodied LLM Agents Learn to Cooperate in Organized Teams
Large Language Models (LLMs) have emerged as integral tools for reasoning, planning, and decision-making, drawing upon their extensive world knowledge and proficiency in language-related tasks. LLMs thus hold tremendous …
Decision MakingHuman Agent CollaborationWorld KnowledgeLarge Language Model-based Human-Agent Collaboration for Complex Task Solving
In recent developments within the research community, the integration of Large Language Models (LLMs) in creating fully autonomous agents has garnered significant interest. Despite this, LLM-based agents frequently demon…
Human Agent CollaborationLanguage ModelingLanguage ModellingLarge Language Model+2Enhancing Human Experience in Human-Agent Collaboration: A Human-Centered Modeling Approach Based on Positive Human Gain
Existing game AI research mainly focuses on enhancing agents' abilities to win games, but this does not inherently make humans have a better experience when collaborating with these agents. For example, agents may domina…
Human Agent CollaborationAn Abstract Specification of VoxML as an Annotation Language
VoxML is a modeling language used to map natural language expressions into real-time visualizations using commonsense semantic knowledge of objects and events. Its utility has been demonstrated in embodied simulation env…
Human Agent CollaborationHuman-Object Interaction DetectionObjectTowards Effective and Interpretable Human-Agent Collaboration in MOBA Games: A Communication Perspective
MOBA games, e.g., Dota2 and Honor of Kings, have been actively used as the testbed for the recent AI research on games, and various AI systems have been developed at the human level so far. However, these AI systems main…
Human Agent CollaborationCausal Inference for Chatting Handoff
Aiming to ensure chatbot quality by predicting chatbot failure and enabling human-agent collaboration, Machine-Human Chatting Handoff (MHCH) has attracted lots of attention from both industry and academia in recent years…
Causal InferenceChatbotcounterfactualHuman Agent Collaboration+2Toward Policy Explanations for Multi-Agent Reinforcement Learning
Advances in multi-agent reinforcement learning (MARL) enable sequential decision making for a range of exciting multi-agent applications such as cooperative AI and autonomous driving. Explaining agent decisions is crucia…
Autonomous DrivingDecision MakingHuman Agent CollaborationMulti-agent Reinforcement Learning+4"I Don't Think So": Summarizing Policy Disagreements for Agent Comparison
With Artificial Intelligence on the rise, human interaction with autonomous agents becomes more frequent. Effective human-agent collaboration requires users to understand the agent's behavior, as failing to do so may cau…
Human Agent Collaboration