Información de la conferencia

DAI 2026: International Conference on Distributed Artificial Intelligence

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Día de Entrega:
2026-07-27
Fecha de Notificación:
2026-09-16
Fecha de conferencia:
2026-11-29
Ubicación:
Hong Kong, China
Ediciones:
CCF: C   Vistas: 13313   Seguidores: 3   Asistentes: 0

Índice Conference Partner (CP-I)

57,5 / 100
Puesto n.º 694 de 5.683 congresos · 13% superior

N.º 57 de 740 en Inteligencia artificial y aprendizaje automático

Reconocimiento académico (35%)
66
Selectividad en la revisión (20%) Sin datos: se puntúa con la línea base neutra de 50 —
Ediciones celebradas (20%)
59
Atención de la comunidad (10%)
43
Integridad del registro público (15%)
55

Datos utilizados: Categorías: CCF C · Ediciones documentadas: 8 · Investigadores que lo siguen aquí: 3 · Investigadores que abrieron esta página en los últimos 24 meses: 54

Falta en el registro público: Tasas de aceptación históricas (+4,5) · Premios al mejor artículo (+2,3)
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Confianza 80 %: la parte de la puntuación respaldada por datos observados y no por la línea base neutra. Cómo se calcula esta puntuación · Ver la clasificación · Versión del algoritmo 1.1 · Calculado el 2026-09-21

Solicitud de Artículos

DAI 2026 (International Conference on Distributed Artificial Intelligence) is a CCF C conference held in Hong Kong, China on 2026-11-29. The paper submission deadline is 2026-07-27. Acceptance notifications are sent on 2026-09-16.

Scope and Topics of Interest Topics of interest include, but are not limited to, the following areas. Authors will be asked to select one or more relevant areas during submission. Agent Engineering & Infrastructure Agent frameworks, harnesses, and operating systems: LangGraph, AutoGen, CrewAI, Smolagents, OpenAI Agents SDK Memory architectures: long-term, short-term, episodic, factual, and experiential memory Skill acquisition and atomic skills Tool use: tool selection, grounding, and reliability Context engineering: context windows, compression, and selective retrieval Agent protocols: MCP, A2A, and interoperability standards AgentOps: observability, debugging, evaluation, and failure recovery Agent identity, reputation, and provenance Foundations of Agent Learning Reinforcement learning, multi-agent reinforcement learning, and cooperative or competitive learning Post-training for agents Self-play, curriculum, and open-ended learning Continual learning, meta-learning, and transfer Reward design and credit assignment Distributed, privacy-preserving, and collaborative learning Scaling laws and empirical theory of agent learning Self-Evolving & Self-Improving Agents Self-improvement and recursive self-modification Meta-reasoning and self-reflection Experience distillation: from trajectories to transferable knowledge Co-evolution of policies and critics Multi-agent evolutionary systems Gödel-style self-rewriting agents Benchmarks and evaluation for self-evolving systems Multi-Agent Cooperation & Human-Agent Interaction Cooperative multi-agent reinforcement learning, credit assignment, and teamwork Communication, language emergence, and negotiation LLM-based multi-agent orchestration Ad-hoc teamwork and zero-shot coordination Coalition formation and distributed problem solving Collective intelligence and swarm behavior Trust, explainability, and accountability AI agents as digital employees, collaborators, and competitors Human-agent and human-robot interaction Agent-based human interaction analysis Agents for enhancing human cooperation Game Theory, Economics & Agent Markets Algorithmic game theory and equilibrium computation Mechanism and market design, auctions, and social choice Strategic behavior of LLM agents and algorithmic collusion Machine-payable APIs and agent-to-agent transactions Contract theory and principal-agent models for AI Blockchain economics and decentralized systems Behavioral game models and bounded rationality Security games Embodied Multi-Agent Systems Multi-robot learning, coordination, and swarms Vision-Language-Action models for agent teams World models for multi-agent planning Sim-to-real transfer in multi-agent settings Heterogeneous embodied teams Safety layers and hardware-software co-design for physical agents Science of AI & AI for Science Science of AI Evaluation, benchmarking, and reproducibility of agent systems Interpretability of multi-agent LLM systems Emergent behavior, scaling laws, and phase transitions Failure modes, red-teaming, and safety evaluation Theoretical foundations of agentic AI AI for Science AI agents for scientific discovery AI agents in mathematics, physics, chemistry, biology, and materials Automated experiment design and execution Scientific literature understanding and hypothesis generation Human-agent collaborative research Agent-based simulation of societies Policy, governance, and alignment of agent collectives
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