Conference Information

AAAI 2027: AAAI Conference on Artificial Intelligence

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AAAI
Submission Date:
2026-07-21
Notification Date:
2026-11-30
Conference Date:
2027-02-16
Location:
Montreal, Quebec, Canada
Years:
CCF: A   ICORE: A*   QUALIS: A1   Viewed: 94769404   Tracked: 1058   Attend: 225

Call For Papers

AAAI 2027 (AAAI Conference on Artificial Intelligence) is a CCF A / ICORE A* / QUALIS A1 conference held in Montreal, Quebec, Canada on 2027-02-16. The paper submission deadline is 2026-07-21. Acceptance notifications are sent on 2026-11-30.

AAAI-27 Areas and topics Submission Areas Application Domains (APP) Audio and Speech Processing (AUD) Cognitive Modeling & Cognitive Systems (CMS) Constraint Satisfaction and Optimization (CSO) Computer Vision (CV) Data Mining & Knowledge Management (DMKM) Game Theory and Economic Paradigms (GTEP) Humans and AI (HAI) Knowledge Representation and Reasoning (KRR) Multiagent Systems (MAS) Machine Learning (ML) Natural Language Processing (NLP) Philosophy and Ethics of AI (PEAI) Planning, Routing, and Scheduling (PRS) Intelligent Robotics (ROB) Reasoning under Uncertainty (RU) Search and Optimization (SO) Areas and topics Application Domains (APP) APP: AI for Education & Learning Technologies APP: AI for Science (Natural & Physical Sciences) APP: Climate, Sustainability & Environment APP: Healthcare & Bioinformatics Applications APP: Humanities & Computational Social Science APP: IoT, Sensor Networks & Smart Cities APP: Mobility, Transportation & Autonomous Systems APP: Natural Sciences APP: Other Applications APP: Security & Privacy Applications APP: Social Networks & Web APP: Software Engineering Audio and Speech Processing (AUD) AUD: Audio Deepfake Detection & Anti-Spoofing AUD: Audio Representation Learning & Foundation Models AUD: Audio-Visual & Multimodal Learning AUD: Automatic Speech Recognition & Spoken Language Understanding AUD: Bias, Fairness, Privacy, Low-Resource & Multilingual Speech AUD: Datasets & Benchmarks for Audio & Speech AUD: Environmental Sound, Acoustic Scenes & Event Detection AUD: Music Information Retrieval & Generation AUD: Other Foundations of Audio & Speech Processing AUD: Paralinguistics & Affective Speech AUD: Speaker Recognition, Diarization & Verification AUD: Speech Enhancement, Separation & Source Separation AUD: Speech Synthesis, Voice Conversion & Generation Cognitive Modeling & Cognitive Systems (CMS) CMS: Affective Computing & Social Cognition CMS: Cognitive Architectures & Conceptual Reasoning CMS: Computational Creativity CMS: Other Foundations of Cognitive Modeling & Systems CMS: Simulating Human Behavior CMS: Symbolic Representations & Agent Architectures Constraint Satisfaction and Optimization (CSO) CSO: Constraint Optimization & Programming CSO: Constraint Satisfaction & Learning CSO: Distributed & Mixed Discrete/Continuous Optimization CSO: Other Foundations of Constraint Satisfaction CSO: Satisfiability & SMT CSO: Search, Solvers & Tools Computer Vision (CV) CV: 3D Computer Vision CV: Adversarial Attacks & Robustness CV: Bias, Fairness, Privacy & Interpretability CV: Biometrics, Face, Gesture & Pose CV: Computational Photography, Image & Video Synthesis CV: Datasets & Benchmarks for Vision CV: Diffusion & Generative Models for Vision CV: Image and Video Retrieval CV: Language, Vision & Multi-modal CV: Learning & Optimization for CV CV: Low-Level & Physics-based Vision CV: Medical and Biological Imaging CV: Motion, Tracking & Activity Analysis CV: Object Detection, Segmentation & Scene Understanding CV: Other Foundations of Computer Vision CV: Remote Sensing / Geospatial AI CV: Representation Learning & Vision Foundation Models CV: Vision for Robotics, Embodied & Autonomous Driving CV: Visual Reasoning & Symbolic Representations Data Mining & Knowledge Management (DMKM) DMKM: Anomaly Detection & Pattern Mining DMKM: Conversational, Query & Retrieval Systems DMKM: Data Stream & Spatio-Temporal Mining DMKM: Data Visualization & Summarization DMKM: Datasets & Benchmarks for Data Mining DMKM: Graph Mining & Social Network Analysis DMKM: Knowledge Graphs, Linked Data & Semantic Web DMKM: Mining of Visual, Multimedia & Multimodal Data DMKM: Other Foundations of Data Mining & Knowledge Management DMKM: Recommender Systems DMKM: Scalability, Parallel & Distributed Systems Game Theory and Economic Paradigms (GTEP) GTEP: Cooperative & Behavioral Game Theory GTEP: Coordination & Adversarial LearningCoordination, Collaboration & Adversarial Interaction GTEP: Game Theory, Equilibrium & Imperfect Information GTEP: Mechanism Design & Auctions GTEP: Other Foundations of Game Theory & Economic Paradigms GTEP: Social Choice, Voting & Fair Division Humans and AI (HAI) HAI: AI for Accessibility HAI: Emotional Intelligence & Brain-Sensing HAI: Explainable AI for Human Understanding HAI: Game Design & Procedural Generation HAI: Human-AI Collaboration, Trust & Teaming HAI: Human-Aware Planning & Decision Support HAI: Human-Computer Interaction & Interfaces HAI: Human-in-the-loop ML & Crowd Sourcing HAI: Learning Human Values & Preferences HAI: Other Foundations of Human Computation & AI Knowledge Representation and Reasoning (KRR) KRR: Action, Change & Spatio-Temporal Reasoning KRR: Automated Reasoning & Theorem Proving KRR: Common-Sense, Causal & Qualitative Reasoning KRR: Diagnosis, Abduction & Argumentation KRR: Knowledge Acquisition, Engineering & Ontologies KRR: KR Languages, Preferences & Beliefs KRR: Logic Programming & Description Logics KRR: Neuro-Symbolic Reasoning KRR: Nonmonotonic Reasoning & Computational Complexity KRR: Other Foundations of Knowledge Representation & Reasoning Multiagent Systems (MAS) MAS: Agent Theories, Architectures & Communication MAS: Agent-Based Simulation & Emergent Behavior MAS: Agentic Safety, Security & Alignment MAS: LLM-based Agents & Agentic Systems MAS: MAS under Uncertainty & Adversarial Agents MAS: Mechanism Design & Modeling other Agents MAS: Multiagent Learning MAS: Multiagent Planning & Coordination MAS: Negotiation, Argumentation & Agreement MAS: Other Foundations of Multiagent Systems MAS: Tool Use, Orchestration & Multi-Agent Coordination for LLMs Machine Learning (ML) ML: Adversarial Learning & Robustness ML: AutoML & Hyperparameter Tuning ML: Bayesian Learning & Uncertainty Quantification ML: Causal Learning ML: Classification, Regression & Kernel Methods ML: Clustering & Unsupervised/Self-Supervised Learning ML: Data-Centric AI, Synthetic Data & Data Curation ML: Deep Generative Models & Autoencoders ML: Deep Learning Algorithms, Architectures & Foundation Models ML: Deep Learning Theory & Learning Theory ML: Dimensionality Reduction, Manifolds & Matrix/Tensor Methods ML: Distributed & Federated Learning ML: Efficient, Edge, Green & Hardware-aware ML ML: Ensemble & Multi-class/Multi-label Learning ML: Ethics, Bias, Fairness & Privacy ML: Evaluation, Benchmarking, Datasets & Analysis ML: Evolutionary Learning ML: Graph-based Machine Learning ML: Machine Unlearning, Data Deletion & Model Editing ML: Mixture of Experts (MoE) ML: Multimodal & Large Multimodal Models (LMMs) ML: Neuro-Symbolic Learning ML: Online Learning & Bandits ML: Optimization for ML ML: Other Foundations of Machine Learning ML: Post-Training, Fine-Tuning & Model Alignment ML: Probabilistic Circuits & Graphical Models ML: Quantum Machine Learning ML: Reasoning & Test-Time Compute ML: Reinforcement, Imitation & Inverse RL ML: Representation Learning ML: Scalability of ML Systems ML: Semi-Supervised & Active Learning ML: Time-Series & Data Streams ML: Transfer, Domain Adaptation & Continual Learning ML: Transparent, Interpretable & Explainable ML ML: World Models, Simulation & Environment Models Natural Language Processing (NLP) NLP: (Large) Language Models NLP: Code Generation / Program Synthesis NLP: Conversational AI & Dialog Systems NLP: Datasets & Benchmarks for NLP NLP: Fact-Checking & Misinformation Detection NLP: Generation & Summarization NLP: Information Extraction & Question Answering NLP: Interpretability, Analysis & Evaluation (incl. Factuality & Hallucination) NLP: Language Grounding & Multi-modal NLP NLP: Machine Translation & Multilinguality NLP: Other Foundations of Natural Language Processing NLP: Prompt Engineering & In-Context Learning NLP: Retrieval-Augmented Generation & Knowledge-Grounded NLP NLP: Safety, Ethics, Bias & Fairness NLP: Semantics, Textual Inference & Discourse NLP: Sentiment, Stylistic & Text Classification NLP: Syntax, Morphology & Lexical Semantics Philosophy and Ethics of AI (PEAI) PEAI: Accountability, Interpretability & Explainability PEAI: AI Alignment & Oversight PEAI: AI Evaluation, Auditing & Red Teaming PEAI: AI, Law, Justice, Regulation & Governance PEAI: Bias, Fairness & Equity PEAI: Generative AI Safety, Provenance & Misuse PEAI: Morality & Value-based AI PEAI: Other Foundations of Philosophy & Ethics of AI PEAI: Philosophical Foundations, Epistemology & AGI PEAI: Privacy & Security PEAI: Safety, Robustness & Trustworthiness PEAI: Societal Impact, Jobs & Labor Planning, Routing, and Scheduling (PRS) PRS: Deterministic & Temporal Planning PRS: Learning for Planning & Scheduling PRS: Mixed Discrete/Continuous Planning & Model-Based Reasoning PRS: Optimization of Spatio-temporal Systems PRS: Other Foundations of Planning, Routing & Scheduling PRS: Plan Execution, Monitoring, Replanning & Recognition PRS: Planning under Uncertainty & Markov Models PRS: Planning with Language Models & Agentic Planning PRS: Scheduling & Routing Intelligent Robotics (ROB) ROB: Datasets & Benchmarks for Robotics ROB: Embodied AI ROB: Human-Robot Interaction ROB: Localization, Mapping & Navigation ROB: Manipulation & Cognitive Robotics ROB: Motion & Path Planning ROB: Multi-Robot Systems ROB: Other Foundations and ApplicationsOther Foundations of Intelligent Robotics ROB: Perception, Sensor Fusion & State Estimation ROB: Robot Learning, Control & Foundation Models Reasoning under Uncertainty (RU) RU: Causality RU: Decision/Utility Theory & Sequential Decision Making RU: Other Foundations of Reasoning under Uncertainty RU: Probabilistic & Relational Probabilistic Models RU: Probabilistic Inference & Graphical Models RU: Stochastic Optimization RU: Uncertainty Representations Search and Optimization (SO) SO: Algorithm Configuration & Sampling-based Search SO: Combinatorial & Non-convex Optimization SO: Distributed & Mixed Discrete/Continuous Search SO: Evolutionary Computation SO: Heuristic, Adversarial & Local Search SO: Metareasoning, Metaheuristics & Learning to Search SO: Other Foundations of Search & Optimization
Last updated by Admin Agent on

Acceptance Ratio

Average acceptance rate: 24.2% over 33 years (1986–2025).

YearSubmittedAcceptedAccepted(%)
202512957303223.4%
20249862234223.7%
20238777172119.6%
20229020134915%
20217911169221.4%
20207737159120.6%
20197095115016.2%
2018380093324.6%
2017259063824.6%
2016213254925.8%
2015199153126.7%
2014140639828.3%
201197524224.8%
201098226426.9%
200893722724.2%
200792125327.5%
200677417122.1%
200580314818.4%
200445312126.7%
200246912125.8%
200043114333.2%
199940010927.3%
199847514430.3%
199732311736.2%
199664319730.6%
199478022228.5%
199352412624%
199263613320.9%
199160314223.5%
199089216118%
198885014817.4%
198771514920.8%
198681718722.9%

Best Papers

YearBest Papers
2023Misspecification in Inverse Reinforcement Learning
2022Online certification of preference-based fairness for personalized recommender systems
2021Exploration-Exploitation in Multi-Agent Learning: Catastrophe Theory Meets Game Theory
2021Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting
2020WinoGrande: An Adversarial Winograd Schema Challenge at Scale
2020Fair Division of Mixed Divisible and Indivisible Goods
2020A Distributed Multi-Sensor Machine Learning Approach to Earthquake Early Warning
2019Zero Shot Learning for Code Education: Rubric Sampling with Deep Learning Inference
2019How to Combine Tree-Search Methods in Reinforcement Learning
2018Counterfactual Multi-Agent Policy Gradients
2018Memory-Augmented Monte Carlo Tree Search
2017The Option-Critic Architecture
2017Label-Free Supervision of Neural Networks with Physics and Domain Knowledge
2016Toward a Taxonomy and Computational Models of Abnormalities in Images
2016Bidirectional Search That Is Guaranteed to Meet in the Middle
2015Surpassing Human-Level Face Verification Performance on LFW with GaussianFace
2015From Non-Negative to General Operator Cost Partitioning
2014Recovering from Selection Bias in Causal and Statistical Inference
2013SMILe: Shuffled Multiple-Instance Learning
2013HC-Search: Learning Heuristics and Cost Functions for Structured Prediction
2012Document Summarization Based on Data Reconstruction
2012Learning SVM Classiiers with Indeinite Kernels
2011Dynamic Resource Allocation in Conservation Planning
2011Complexity of and Algorithms for Borda Manipulation
2010How Incomplete Is Your Semantic Web Reasoner?
2010A Novel Transition Based Encoding Scheme for Planning as Satisfiability
2008Optimal False-Name-Proof Voting Rules with Costly Voting
2008How Good is Almost Perfect?
2007PLOW: A Collaborative Task Learning Agent
2007Thresholded Rewards: Acting Optimally in Timed, Zero-Sum Games
2006Towards an Axiom System for Default Logic
2006Model Counting: A New Strategy for Obtaining Good Bounds
2005The Max K-Armed Bandit: A New Model of Exploration Applied to Search Heuristic Selection
2004Learning and Inferring Transportation Routines
2002On Computing all Abductive Explanations
2000The Game of Hex: An Automatic Theorem Proving Approach to Game Programming
1999PROVERB: The Probabilistic Cruciverbalist
1998Learning Evaluation Functions for Global Optimization and Boolean Satisfiability
1998Acceleration Methods for Numeric CSPs
1998The Interactive Museum Tour-Guide Robot
1997A Practical Algorithm for Finding Optimal Triangulations
1997Fast Context Switching in Real-Time Propositional Reasoning
1997Statistical Parsing with a Context-Free Grammar and Word Statistics
1997Building Concept Representations from Reusable Components
1996A Novel Application of Theory Refinement to Student Modeling
1996Verification of Knowledge Bases Based on Containment Checking
1996Pushing the Envelope: Planning, Propositional Logic and Stochastic Search
1994A Prototype Reading Coach that Listens
1993Equations for Part-of-Speech Tagging
1992Hard and Easy Distributions of SAT Problems
1991Improving Rule-Based Systems Through Case-Based Reasoning
1988Qualitative Results Concerning the Utility of Explanation-Based Learning
1988Approach to Qualitative Algebraic Reasoning
1987An Approach to Default Reasoning Based on a First-Order Conditional Logic
1987Energy Constraints on Deformable Models: Recovering Shape and Non-Rigid Motion
1987Word-Order Variation in Natural Language Generation
1987Defining Operationality for Explanation-based Learning
1987Non-Deterministic Lisp with Dependency-directed Backtracking
1987Curing Anomalous Extensions
1987PROMPT: An Innovative Design Tool
1987Incremental Causal Reasoning
1986Default Reasoning, Nonmonotonic Logics, and the Frame Problem
1986Generating Tests by Exploiting Designed Behavior
1984The Tractability of Subsumption in Frame-Based Description Languages
1984Choices without Backtracking
1984A Logic of Implicit and Explicit Belief
1984Shading into Texture

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