Conference Information

AAAI 2027: AAAI Conference on Artificial Intelligence

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Submission Date:
2026-07-21
Notification Date:
2026-11-30
Conference Date:
2027-02-16
Location:
Montreal, Quebec, Canada
Years:
41
CCF: a   CORE: a*   QUALIS: a1   Viewed: 92531447   Tracked: 1050   Attend: 216

Call For Papers

AAAI 2027 (AAAI Conference on Artificial Intelligence) is a CCF A / CORE 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.

We are pleased to announce the Forty-First AAAI Conference on Artificial Intelligence (AAAI-27), which will be held in Montréal, Canada, from February 16 to February 23, 2027. The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-27 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.
Last updated by Dou Sun in

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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