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

Choosing the best topic(s) in the AAAI-27 Main Track

AAAI is a broad-based AI conference, inviting papers from different subcommunities of the field. It also encourages papers that combine different areas of research (e.g., vision and language; machine learning and planning). Finally, it also invites methodological papers focused on diverse areas of application such as healthcare or transportation.

In AAAI-27 authors are asked to choose one primary keyword (mandatory) and (optionally) up to five secondary topics. With over 200 topics available to choose from, picking the best keywords for a paper may become confusing. This brief guide describes some high-level principles for choosing the best topics.

The main purpose of these topics is to enable finding the most appropriate reviewers for each submission, which is what this guide focuses on. Note, however, that there are a variety of other signals beyond topics to match reviewers and papers, so not everything hinges on this choice.

In the end, choosing the best topics is an art; making poor choices about topics can increase your chance of getting suboptimal reviews. This guide aims to help authors understand the reasoning process to allow for the best possible matching of papers with qualified reviewers.

Choosing the primary topic

The main principle for choosing a paper’s primary topic is to identify the subarea to which the paper makes its main contribution. It should follow that a reviewer who is an expert in that subarea will be positioned to evaluate the paper most effectively.

Most of the time, it is recommended to start with the top-level area (e.g., computer vision, knowledge representation) that describes the paper’s methodological focus and then picking the best fitting keyword in that area.
However, a sizable number of papers describe work at the intersection of different fields. To give some examples, consider papers:

• developing general machine learning methods but primarily motivated by problems in NLP
• studying bias in machine learning models applied to healthcare
• designing a novel elicitation mechanism for crowdsourcing
• combining different methodological subareas of AI in an integrated way, e.g., combine learning for solving satisfiability problems.

In all such settings, it becomes trickier to choose the best primary topic. Here are some rules of thumb:

(1) Focus on where the primary contribution lies, and which community will benefit the most from reading the paper. For example, if an ML algorithm is demonstrated on both computer vision and NLP applications, it is best kept under ML (as it is a general advance, with NLP and vision serving only as applications). If, however, the paper is heavily motivated by details of a particular class of ML problems (e.g., proposing an algorithm that leverages specific structure of images, or language) then picking a topic that focuses on this class of problems (vision; NLP) is more appropriate.

(2) For papers with specific applications (e.g., healthcare or transportation), typically the application is NOT the primary topic. Usually, a AAAI main track paper will make methodological advances leading to an impact to an application. Choose a primary keyword based on the methodology. There is one exception to this rule: if the impact to the application area is much more impressive than the methodological innovation, your paper may have the best chance with the application area being the primary keyword. That said, you should carefully consider whether such a paper is more appropriate to submit to the track on AI for Social Impact or to IAAI; note that each of these evaluates papers according to different criteria than the AAAI main track.

(3) For papers genuinely at the intersection of different fields, carefully scan all topics. It is possible that a joint topic already exists in the list. For example, an ML paper studying bias applied to healthcare may naturally use the keyword “ML: Bias and Fairness” as the primary topic (since healthcare is the application component).

(4) However, perhaps no topic adequately appears to capture the intersection of AI fields to which the paper makes its primary contribution. In such cases, a judgment call is necessary about which community is likely to best appreciate the work. For example, if a paper combines learning to solve satisfiability problems by using ideas of machine learning within a satisfiability algorithm, it is likely that the paper’s most fundamental impact will be on the design of satisfiability solvers rather than on the design of new learning algorithms; hence, “CSO: Satisfiability” would be a good choice for the primary topic. However, if the paper is about solving the satisfiability problem using a deep neural network with significant innovations in machine learning, then a machine learning topic will be a better fit.

Choosing secondary topics

When choosing secondary topics, it is helpful to consider two questions. First, all things being equal, what beyond the primary topic should the reviewers be expert in? Second, if no one reviewer is likely to tick all the boxes, how would experts outside the primary subarea who would add important perspectives to the paper’s review be described? For example, ML fairness applied to healthcare should choose “APP: Healthcare & Bioinformatics Applications” as a secondary topic. Use of machine learning for satisfiability should choose the other area as the secondary topic.

As an extreme example, if mixed discrete-continuous search is used to solve a routing problem that arises when considering privacy issues in a navigation-based game, with the main contribution being to the routing problem (i.e., “PRS: Scheduling & Routing” is the primary keyword), then the paper may benefit from having multiple secondary keywords like “SO: Mixed Discrete/Continuous Search“, and “PEAI: Privacy and Security”.

Every attempt is made to find reviewers that cover all specified topics. In some cases it will be hard — such reviewers may not exist, and each reviewer can only review a limited number of papers. On the other hand, be careful what you wish for: adding secondary topics can be a double-edged sword. If the paper’s contributions are relatively simple from the point of view of an expert in a secondary topic, then that expert may give a poor rating, perhaps overlooking the paper’s value in another domain. In such situations, it is better to avoid adding secondary topics as long as experts in the primary topic will understand the paper, assuming they have broad (but not deep) knowledge of other fields of AI.