This year AAAI-18 was co-organized with IAAI-18 and EAAI-18. The technical programme for AAAI-18 can be found here. The main categories for AAAI-18 were:

• Planning and Scheduling (PS)(HSO)
• Game Theory and Economics (GTEP)
• Cognitive Systems (CS)(CM)
• Computational Sustainability (CSAI)(MLA)(ML)
• Vision and Video Retrieval(VIS)(MLA)(APP)
• Language and Learning (NLPML)(NLPTM)(KRR)(NLPKR)
• Policy Learning (ML)(PS)(RU)
• Knowledge Representation and Reasoning (KR)
• Classic Paper Award Talk:
• PROMPT: Algorithm and Tool for Automated Ontology Merging and Alignment
• Machine Learning Applications (MLA)(APP)
• Machine Learning and Graphical Models (ML)(RU)(NLPML)
• Reinforcement Learning (ML)(PS)(RU)
• Search and Constraint Satisfaction (SCS)(HS)
• What's Hot Talk:
• What's Hot in ICAPS, SoCS, SAT, CP, IJCAI, and AAMAS
• What's Hot in AAMAS

• What's Hot in SoCS

• What's Hot in CP

• What's Hot in ICAPS

• Security, Trust and Privacy (APP)(ML)(HAC)(NLPML)(HAI)(MLA)
• Deep Learning and Bayesian Methods (ML)(NLPML)(AIW)
• Learning Theory (ML)
• Machine Learning Applications and Recommender Systems (MLA)(ML)(AIW)
• Relational and Graph-­Based Learning (RU)(ML)
• Search and Machine Learning (ML)(HSO)(SCS)
• Learning and Stochastic Processes (ML)(MLA)(APP)(RU)
• AAAI-­18 Senior Member Track: Blue Sky Papers
• Engineering Pro-­Sociality with Autonomous Agent(Ana Paiva, Fernando P. Santos, Francisco C. Santos)
• Learning Fast and Slow: Levels of Learning in General Autonomous Intelligent Agents(John E. Laird, Shiwali Mohan)
• Imagination Machines: A New Challenge for Artificial Intelligence(Sridhar Mahadevan)
• AI Meets Chemistry (Akihiro Kishimoto, Beat Buesser, Adi Botea)
• Clustering-­ What Both Theoreticians and Practitioners Are Doing Wrong (Shai Ben-­David)
• Multiagent Systems (MAS)(GTEP)
• Heuristic Search (HSO)(ML)(PS)
• Uncertainty in AI (RU)(ML)
• Robotics (ROB)(HAC)(NLPML)(VIS)
• Unsupervised and Online Learning (ML)
• Reasoning under Uncertainty (RU)(AIW)(ML)
• Learning, Uncertainty, and Kernels (RU)(ML)(KR)
• Search, Constraint Satisfaction, and Optimization (SCS)(HSO)
• Machine Learning, Preferences, and Ranking (ML)
• Machine Learning and Time Series (ML)(MLA)(NLPML)
• An Adversarial Hierarchical Hidden Markov Model for Human Pose Modeling and Generation
• Dynamic Optimization of Neural Network Structures Using Probabilistic Modeling
• cw2vec: Learning Chinese Word Embeddings with Stroke n-­gram Information

Invited Talks

• From Naive Physics to Connotation: Learning and Reasoning about the World using Language (Yejin Choi)
• How Machines Learn Best from Humans (Charles Isbell)
• Probabilistic Machine Learning and AI (Zoubin Ghahramani)
• Actual Causality: A Survey (Joe Halpern)
• How Should We Evaluate Machine Learning for AI? (Percy Liang)

More detailed overview by David Abel