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DAI 2026: International Conference on Distributed Artificial Intelligence

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投稿締切日:
2026-07-27
通知日:
2026-09-16
開催日:
2026-11-29
開催地:
Hong Kong, China
開催回数:
CCF: C   閲覧: 12739   フォロー: 3   参加: 0

会伴インデックス (CP-I)

57.5 / 100
全 5,647 件中 第 672 位 · 上位 12%

人工知能・機械学習 分野 734 件中 第 55 位

学術的評価 (35%)
66
投稿の選択性 (20%) データなし — 中立の基準値 50 点として算入 —
開催回数 (20%)
59
コミュニティの注目度 (10%)
43
公開情報の充実度 (15%)
55

使用した入力: 収録ランク：CCF C · 確認できる開催回数：8 · 会伴でフォローしている研究者：3 人 · 過去 24 か月にこのページを開いた研究者：52 人

公開情報で不足しているもの: 過去の採択率 (+4.5) · 最優秀論文の記録 (+2.3)
主催者は会議を認証申請したうえで、このページから直接追加できます。スコアは毎晩再計算されます。このスコアを上げるには

信頼度 80% — スコアのうち、中立の基準値ではなく実際に観測されたデータに基づく割合。 このスコアの算出方法 · ランキングを見る · アルゴリズム版 1.1 · 算出日 2026-09-03

論文募集

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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