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The biannual Association for the Advancement of Artificial Intelligence Symposium (AAAI Symposium) is a series symposia scheduled for spring (since 1992, USA), summer (since 2023, Asia), and fall (since 1992, USA). The symposium affords participants a smaller, more intimate setting where they can share ideas and learn from each other’s artificial intelligence (AI) research. Topics for the symposia change each year, and the limited seating capacity and relaxed atmosphere allow for workshop like interaction.

The AAAI Spring Symposium is organized in association with the Department of Computer Science at Stanford University.

Recurring Symposia:

• 2019-present: Combining Machine Learning and Knowledge Engineering (MAKE) @ AAAI Spring Symposium
  (Organizer: Intelligent Information Systemms Research Group, School of Business, FHNW University of Applied Sciences and Arts Northwestern Switzerland)
  The AAAI-MAKE symposium aims to bring together researchers and practitioners from machine learning and knowledge engineering to reflect how combining the two fields can contribute to hybrid intelligence systems.
  2024: Large Language Models for Knowledge Graph and Ontology Engineering

  Keynotes:
  Neuro-Symbolic Cognitive Reasoning: From Theory to Practice
  The Conversational Approach for Modular Ontology (Adress)
  Recipes in AI: Representation, Recommendation and Generation
  Injecting LLMs in the ontology engineering lifecycle: What we learned so far

  Presentations:
  Investigating Open Source LLMs to Retrofit Competency Questions in Ontology Engineering
  Generating Ontology-Learning Training-Data through Verbalization
  StructuGraphRAG: Structured Document-Informed Knowledge Graphs for Retrieval-Augmented Generation
  Knowledge Aware Automated Health Claims Processing with Medical Ontologies and Large Language Models
  LLMasMMKG: LLM Assisted Synthetic Multi-Modal Knowledge Graph Creation For Smart City Cognitive Digital Twins
  Knowledge Graph Modeling-Driven Large Language Model Operating System (LLM OS) for Task Automation in Process Engineering Problem-Solving
  Improving Ontology Requirements Engineering with OntoChat and Participatory Prompting
  Open Weight AI for Querying Distributed Government Knowledge Graphs
  Ontology Text Alignment: Aligning Textual Content to Terminological Axioms
  Large Language Models for Engineering Patient Journey Knowledge Graphs
  Using OLIVE to Connect Food Informatics to Health Informatics: A Flavonoids Case Study
  Using LLMs for Complex Ontology Alignment: Modularity is key!

  Breakout Sessions:
  TBD

  Panel Discussions:
  LLMs for KGs and OE: What are realistic prospects?
  

  2024: Empowering Machine Learning and Large Language Models with Domain and Commonsense Knowledge

  Keynotes:
  AI with Common Sense — The Bigger Picture
  Reasoning in humans and machines
  What LLMs might learn from Cyc” — in memoriam of Douglas B. Lenat (1950–2023)
  Large Language Models as Cultural Technologies: Truth versus transmission
  The Next Grand Challenge in AI — Making Better Mistakes
  Incorporating explanation-based reasoning into models of human and machine intelligence

  Presentations: Papers
  Can LLMs Answer Investment Banking Questions? Using Domain-Tuned Functions to Improve LLM Performance on Knowledge-Intensive Analytical Tasks
  Human-AI Collaboration Through Data-Centric Process Models: Empowering Large Language Models in Hybrid Intelligence Systems for Knowledge-Intensive Processes
  Concept-Guided LLM Agents for Human-AI Safety Codesign
  Retrieval-Augmented Generation and Reasoning-and-Action Agents in Biomimicry
  ASMR: Aggregated Semantic Matching Retrieval Unleashing Commonsense Ability of LLM through Open-Ended Question Answering
  GLaM: Fine-Tuning Large Language Models for Domain Knowledge Graph Alignment via Neighborhood Partitioning and Generative Subgraph Encoding
  Exploring Alternative Approaches to Language Modeling for Learning from Data and Knowledge
  LLMs in Automated Essay Evaluation: A Case Study
  ChEdBot: Designing a Domain-Specific Conversational Agent in a Simulational Learning Environment using LLMs
  K-PERM: Personalized Response Generation Using Dynamic Knowledge Retrieval and Persona-Adaptive Queries
  A Framework for Enhancing Behavioral Science Research with Human-Guided Language Models
  Semantic Verification in Large Language Model-based Retrieval Augmented Generation
  Faithful Reasoning over Scientific Claims
  LLMs Among Us: Generative AI Participating in Digital Discourse
  Modeling Patterns for Neural-Symbolic Reasoning Using Energy-based Models
  What Can Computers Do Now? Dreyfus Revisited for the Third Wave of Artificial Intelligence
  Advancing Ontology Alignment in the Labor Market: Combining Large Language Models with Domain Knowledge
  Personalised Course Recommender: Linking Learning Objectives and Career Goals through Competencies
  An LLM-Aided Enterprise Knowledge Graph (EKG) Engineering Process
  Rule-Based Explanations of Machine Learning Classifiers using Knowledge Graphs
  Domain-specific Embeddings for Question-Answering Systems: FAQs for Health Coaching
  Enhancing Knowledge Graph Consistency through Open Large Language Models: A Case Study
  GPT-4V Takes the Wheel: Promises and Challenges for Pedestrian Behavior Prediction
  Fusing Domain-Specific Content from Large Language Models into Knowledge Graphs for Enhanced Zero Shot Object State Classification
  Exploring Failure Cases in Multimodal Reasoning About Physical Dynamics
  Multi-Modal Instruction-Tuning Small-Scale Language-and-Vision Assistant for Semiconductor Electron Micrograph Analysis
  Causal Event Graph-Guided Language-based Spatiotemporal Question Answering

  Plenary: (1)
  TBD
  

  2023: Challenges Requiring the Combination of Machine Learning and Knowledge Engineering

  Keynotes:
  Personal Perspectives on AI (with historical side-stories)

  Presentations: Papers
  A Systematic and Efficient Approach to the Design of Modular Hybrid AI Systems
  Embedded to Interpretive: A Paradigm Shift in Knowledge Discovery to Represent Dynamic Knowledge
  Hybrid ML/KB Systems Learning through NL Dialog with DL Models
  An Early Warning System that combines Machine Learning and a Rule-Based Approach for the Detection of Cancer Patients’ Unplanned Visits
  Robot Behavior-Tree-Based Task Generation with Large Language Models
  Explainable Reinforcement Learning Based on Q-Value Decomposition by Expected State Transitions
  AISecKG: Knowledge Graph Dataset for Cybersecurity Education
  Towards Dialog Strategies for a Hybrid Health Coach Chatbot
  Ontology-Driven Enhancement of Process Mining With Domain Knowledge
  Neuro-symbolic Rule Learning in Real-world Classification Tasks
  Rule-Based Knowledge Discovery via Anomaly Detection in Tabular Data
  Leveraging RDF Graphs, Similarity Metrics and Network Analysis for Business Process Management
  A Hybrid Intelligent Approach Combining Machine Learning and a Knowledge Graph to Support Academic Journal Publishers Addressing the Reviewer Assignment Problem (RAP)
  Towards Hybrid Intelligent Support Systems for Emergency Call Handling
  PyReason: Software for Open World Temporal Logic
  The Contribution of Knowledge in Visiolinguistic Learning: A Survey on Tasks and Challenges
  Counterfactual Edits for Generative Evaluation
  Knowledge-Based Counterfactual Queries for Visual Question Answering
  Fine-Grained ImageNet Classification in the Wild

  Presentations: Challenges
  An Independent Evaluation of ChatGPT on Mathematical Word Problems (MWP)
  Dynamic Ontology Matching Challenge

  Presentations: Short Papers
  Offline RL+CKG: A hybrid AI model for cybersecurity tasks
  Creating Dynamically Evolving Ontologies: A Use Case from the Labour Market Domain
  Cognitive Neuro-Symbolic Reasoning

  Demonstrations:
  Neural Networks for Design Thinking: A Recognition Service for Scene2Model using Haptic Modelling Objects

  Discussions:
  Towards Knowledge Standardization and Formalization
  

  2022: Machine Learning and Knowledge Engineering for Hybrid Intelligence

  Keynotes:
  Google Dataset Search

  Presentations: Papers
  Neural-Symbolic Reasoning under Open-World and Closed-World Assumptions
  Machine Learning on Simulated and Real Farm Data based on an Ontology-Controlled Data Infrastructure
  A Framework for Context-dependent Augmented Reality Applications Using Machine Learning and Ontological Reasoning
  Combining Machine Learning with Human Knowledge for Delivery Time Estimations
  Combining Learning from Human Feedback and Knowledge Engineering to Solve Hierarchical Tasks in Minecraft
  A Hybrid Intelligent Approach for the Support of Higher Education Students in Literature Discovery
  SFMGNet: A Physics-based Neural Network To Predict Pedestrian Trajectories
  Adjoined Networks: A training paradigm with applications to network compression
  KARaML: Integrating Knowledge-Based and Machine Learning Approaches to Solve the Winograd Schema Challenge
  Contrastive Visual and Language Translational Embeddings for Visual Relationship Detection
  CalBERT – Code-mixed Adaptive Language representations using BERT
  Learning Rules with Attributes and Relations in Knowledge Graphs
  Explainable Deep Feature Embedding using Multiple Instance Learning for Pathological Image Analysis
  The FATE system iterated: Fair, Transparent and Explainable Decision Making in a Juridical Case
  ConceptSuperimposition: Using Conceptual Modeling Method for Explainable AI
  Robots with Commonsense: Improving Object Recognition through Size and Spatial Awareness
  Hybrid AI system delivering highly targeted news to business professionals
  Extracting Knowledge with Constructivist Machine Learning: Conceptual and Procedural Models
  Using the Strategy Design Pattern for Hybrid AI System Design
  Do it Like the Doctor: How We Can Design a Model to Diagnose Pneumothorax Using Domain Knowledge
  Collusion Detection in Team-Based Multiplayer Games
  Agent Smith: Machine Teaching for Building Question Answering Agents
  Conceptual Edits as Counterfactual Explanations

  Presentations: Short Papers
  Commonsense Reasoning for Identifying and Understanding the Implicit Need of Help and Synthesizing Assistive Actions
  SemOI2 – Building Adaptive And Cost-Effective Recognition Applications With Semantic Augmentation
  Actionable Recommendations for Small Businesses with Hybrid AI
  Decision Transformers for Glut Control in the Active Logic Machine
  Potential Energy to Improve Link Prediction with Relational Graph Neural Networks
  

  2021: Combining Machine Learning and Knowledge Engineering

  Keynotes:
  Combining Machine Learning and Knowledge Engineering (opening)

  Presentations: Papers
  Decision Support combining Machine Learning, Knowledge Representation and Case-Based Reasoning
  Supporting Case-Based Reasoning with Neural Networks: An Illustration for Case Adaptation
  The Significance of Computational Performance for Enterprise AI in Question-Answering
  Knowledge Infused Policy Gradients for Adaptive Pandemic Control
  Evaluation of Machine Learning Algorithms in a Human-Computer Hybrid Record Linkage System
  Enhancement of Record Linkage by Using Attributes containing Natural Language Text
  Towards More Robust Fashion Recognition by Combining of Deep-Learning-Based Detection with Semantic Reasoning
  Neural-Symbolic Integration for Fairness in AI
  A Neural-Symbolic Approach for User Mental Modeling: A step towards building exchangeable identities
  Towards Kinematically Constrained Real Time Human Pose Estimation using Sparse IMUs
  Deep Learning Approaches for Forecasting Strawberry Yields and Prices Using Satellite Images and Station-Based Soil Parameters
  Anomaly Detection in Railway Infrastructure
  A Recap of Early Work on Theory and Knowledge Refinement
  Evaluating artificial short message service campaigns through rule based multi-instance multi-label classification
  Implications of Combining Domain Knowledge in Explainable Artificial Intelligence
  XAI for Operations in the Process Industry – Applications, Theses, and Research Directions
  Guiding Parameter Estimation of Agent-Based Modeling Through Knowledge-based Function Approximation
  Exploring the Hyperparameters of XGBoost Through 3D Visualizations
  Using Camera-Drones and Artificial Intelligence to Automate Warehouse Inventory
  Batch-like Online Learning for More Robust Hybrid Artificial Intelligence: Deconstruction as a Machine Learning Process
  Improving machine learning performance using conceptual modeling
  Hybrid AI using Graph Neural Networks in Scene Classification
  Fit to Measure: Reasoning about Sizes for Robust Object Recognition
  ZERO – Detect objects without training examples, by knowing their parts
  Knowledge Engineering with Image Data in Real-World Settings (DEMO)
  EmEL++ : Embeddings for the EL++ Description Logic
  Towards Automatic Ontology Alignment using BERT
  Ontology-based transfer learning in the airport and warehouse logistics domains
  Neuro-Symbolic Deductive Reasoning for Cross-Knowledge Graph Entailment
  Learning profile-based recommendations for medical search auto-complete
  Using a General Prior Knowledge Graph to Improve Data-Driven Causal Network Learning
  Hybrid Conversational AI for Intelligent Tutoring Systems
  Semantic Enrichment of Pretrained Embedding Output for Unsupervised IR
  Automated Construction of Knowledge-Bases for Safety Critical Applications: Challenges and Opportunities
  The FATE system: FAir, Transparent and Explainable decision making
  SAMS: Human-in-the-loop Approach to Combat the Sharing of Digital Misinformation
  Team Design Patterns for Moral Decisions in Hybrid Intelligent Systems: A Case Study of Bias Mitigation
  “Is depression related to cannabis?”: A Knowledge-Infused model for Entity and Relation Extraction with Limited Supervision
  Towards unsupervised knowledge extraction
  Capturing Knowledge of Emerging Entities from Extended Search Snippets
  Case-Based System of User Interface Reverse Engineering
  A Study in Practical Solutions to Sarcasm Detection with Machine Learning and Knowledge Engineering Techniques
  Combining Symbolic and Sub-symbolic AI in the Context of Education and Learning
  Common-Knowledge Concept Recognition for SEVA
  Attribution-based Salience Method towards Interpretable Reinforcement Learnin
  Using Pre-trained Transformer Deep Learning Models to Identify Named Entities and Syntactic Relations for Clinical Protocol Analysis

  Panel Discussions: (1)
  n.a.
  

  2020: Combining Machine Learning and Knowledge Engineering in Practice

  Note: Although the symposium was cancelled the proceedings have been published

  Presentations: Regular Papers
  Domain Knowledge Aided Explainable Artificial Intelligence for Intrusion Detection and Response
  Towards AI-based Solutions in the System Development Lifecycle
  An Evaluation of Knowledge Graph Embeddings for Autonomous Driving Data: Experience and Practice
  Using Pre-trained Transformer Deep Learning Models to Identify Named Entities and Syntactic Relations for Clinical Protocol Analysis
  Completion Reasoning Emulation for the Description Logic EL+
  ARO: A Memory-Based Approach to Environments with Perceptual Aliasing
  A Review on Intelligent Object Perception Methods Combining Knowledge-based Reasoning and Machine Learning
  GiCoMAF: An Artificial Intelligence Algorithm to Utilize Maps for Operators of Unmanned Aerial Vehicles
  Supporting Trust in Hybrid Intelligence Systems Using Blockchains
  Common-Knowledge Concept Recognition for SEVA
  Using Learning Algorithms to Create, Exploit and Maintain Knowledge Bases: Principles of Constructivist Machine Learning
  Using Concepts to Understand Intelligent Agents
  Cognitive Twin: A Cognitive Approach to Personalized Assistants
  Knowledge Infused Learning (K-IL): Towards Deep Incorporation of Knowledge in Deep Learning
  Multi-PCA Driven Approach for Fault Detection and Root Cause Analysis of Process Equipment
  Frequency-Based vs. Knowledge-Based Similarity Measures for Categorical Data
  Case-Based System of User Interface Reverse Engineering
  Novel Approach to Music Genre Classification using Clustering Augmented Learning Method (CALM)
  Threshold Optimization in Multiple Binary Classifiers for Extreme Rare Events using Predicted Positive Data
  Mitigating Bias in Deep Nets with Knowledge Bases: the Case of Natural Language Understanding for Robots
  Supervised Visualization of Vocabulary Knowledge towards Explainable Support of Second Language Learners

  Presentations: Short Papers
  PODNet: A Neural Network for Discovery of Plannable Options
  Vec2Struc: A Method Towards Explainable Structural-Based Node Embeddings
  Combining Symbolic and Sub-symbolic AI in the Context of Education and Learning
  Attribution-based Salience Method towards Interpretable Reinforcement Learning
  Hitachi Materials Informatics Analytics Platform Assisting Rapid Development
  EM-algorithm Enpowers Material Science: Application of Inverse Estimation for Small Angle Scattering
  A Study in Practical Solutions to Sarcasm Detection with Machine Learning and Knowledge Engineering Techniques
  Legal Artificial Intelligence – Have You Lost a Piece from Jigsaw Puzzle?
  Tutor4RL: Guiding Reinforcement Learning with External Knowledge
  

  2019: Combining Machine Learning with Knowledge Engineering

  Keynotes:
  It’s Statistical Learning and Knowledge Engineering All the Way Down
  A boxology of architectural patterns for combining learning and reasoning

  Presentations: Position Papers
  Deconstructing the Final Frontier of Artificial Intelligence: Five Theses for a Constructivist Machine Learning
  Knowledge engineering and machine learning for design and use in cyber-physical environments
  From Demonstrations and Knowledge Engineering to a DNN Agent in a Modern Open-World Video Game
  Leverage White-Collar Workers with AI

  Presentations: Full Papers
  SEVA: A Systems Engineer’s Virtual Assistant
  Virtual Bartender: A Dialog System Combining Data-Driven and Knowledge-Based Recommendation
  Building Knowledge Base through Deep Learning Relation Extraction and Wikidata
  Duplicate Removal for Overlapping Clusters: A Study Using Social Media Data
  Performance measures fusion for experimental comparison of methods for multi-label classification
  CrystalGAN: Learning to Discover Crystallographic Structures with Generative Adversarial Networks
  Tree-based Regularization for Interpretable Readmission Prediction

  Presentations: Position & Full Papers
  Collaborative Learning of Concept Representations for Video Data
  Representation and Retrieval of Images by Means of Spatial Relations Between Objects
  Modularity as a Means for Complexity Management in Neural Networks Learning
  Applying the Concept of Knowledge Blockchains to Ontologies
  Combining Machine Learning with Knowledge Engineering to detect Fake News in Social Networks – A Survey
  Improving Topic Modeling for Textual Content with Knowledge Graph Embeddings
  The Lokahi Prototype: Toward the automatic Extraction of Entity Relationship Models from Text
  DEXTER – Data EXTraction & Entity Recognition for Low Resource Datasets
  CoKE : Word sense induction using contextualized knowledge embeddings
  Towards an Assistive and Pattern Learning-driven Process Modeling Approach
  DeepLogic: Towards End-to-End Differentiable Logical Reasoning
  On the Capabilities of Logic Tensor Networks for Deductive Reasoning
  KINN: Incorporating Expert Knowledge in Neural Networks
  Learning and Engineering Similarity Functions for Business Recommenders and Clustering
  Leveraging Wikipedia for Ontology Pattern Population from Text
  Metrics for Evaluating Quality of Embeddings for Ontological Concepts

  Posters & Demonstrations:
  Using Predicate Information from a Knowledge Graph to Identify Disease Trajectories
  A proposal for determining the evidence types of biomedical documents using a drug-drug interaction ontology and machine learning
  On Datasets for Evaluating Architectures for Learning to Reason
  Learning Temporal Rules from Knowledge Graph Streams
  

“Knowledge Management” Symposia:

Conference History:

AAAI 2024 Fall Symposium, November 7-9, 2024, Arlington, VA, USA

Symposia:
Large Language Models for Knowledge Graph and Ontology Engineering (LLMs for KG and OE)

AAAI 2024 Spring Symposium, March 25-27, 2024, San Francisco, CA, USA

Symposia:
Empowering Machine Learning and Large Language Models with Domain and Commonsense Knowledge (AAAI-MAKE 2024) / Virtual participation possible

AAAI 2023 Spring Symposium, March 27–29, 2023, San Francisco, CA, USA

Symposia:
Challenges Requiring the Combination of Machine Learning and Knowledge Engineering (AAAI-MAKE)

AAAI 2022 Fall Symposium, November 17-19, 2022, Arlington, VA, USA

Symposia:
Knowledge-Guided Machine Learning (KGML)

AAAI 2022 Spring Symposium, March 21–23, 2022, Stanford University, Palo Alto, CA, USA / Virtual venue

Symposia:
Machine Learning and Knowledge Engineering for Hybrid Intelligence (AAAI-MAKE 2022; in-person w. remote attendance option)

AAAI 2021 Fall Symposium, November 4-6, 2021, Online

Symposia:
Science-Guided AI

AAAI 2021 Spring Symposium, March 22–24, 2021, Virtual venue

Symposia:
Combining Machine Learning and Knowledge Engineering (AAAI-MAKE 2021)
Challenges and Opportunities for Multi-Agent Reinforcement Learning (COMARL AAAI 2021)
Combining Machine Learning with Physical Sciences (AAAI-MLPS 2021)
Machine Learning for Mobile Robot Navigation in the Wild

AAAI 2020 Fall Symposium, November 11-14, 2020, Washington, DC, USA

Symposia:
Physics-Guided AI to Accelerate Scientific Discovery

AAAI 2020 Spring Symposium, November 11-12, 2020, Stanford University, Palo Alto, CA, USA

Note: The physical meeting of the Spring Symposium scheduled for March 23-25 has been postponed to November 11-12, 2020 and March 22–24, 2021 (virtual mode).
In some cases, organizers may opt out of this meeting, or may hold an additional virtual meeting during the previously scheduled March 23-25 timeframe. Alternately, they may opt for a virtual meeting only.

Symposia:
Combining Machine Learning and Knowledge Engineering in Practice (AAAI-MAKE 2020, Nov 11-12 | POSTPONED to 3/2021)
Challenges and Opportunities for Multi-Agent Reinforcement Learning (COMARL AAAI 2020, Nov 11-12 | POSTPONED to 3/2021)
Combining Artificial Intelligence and Machine Learning with Physical Sciences (AAAI-MLPS 2020, Mar 23-25, virtual)

AAAI 2019 Spring Symposium, March 25-27, 2019, Stanford University, Palo Alto, CA, USA

Symposia:
Combining Machine Learning with Knowledge Engineering (AAAI-MAKE 2019)

AAAI 2017 Spring Symposium, March 27-29, 2017, Stanford University, Palo Alto, CA, USA

Symposia:
Designing the User Experience of Machine Learning Systems
Learning from Observation of Humans
Wellbeing AI: From Machine Learning to Subjectivity Oriented Computing

AAAI 2016 Spring Symposium, March 21-23, 2016, Stanford University, Palo Alto, CA, USA

Symposia:
Challenges and Opportunities in Multiagent Learning for the Real World

AAAI 2015 Fall Symposium, November 12–14, 2015, Arlington, VA, USA

Symposia:
Embedded Machine Learning

AAAI Spring Symposium, March 23–25, 2015, Stanford University, Palo Alto, CA, USA

Symposia:
Knowledge Representation and Reasoning: Integrating Symbolic and Neural Approaches

AAAI 2014 Fall Symposium, November 13–15, 2014, Arlington, VA, USA

Symposia:
Knowledge, Skill, and Behavior Transfer in Autonomous Robots
Natural Language Access to Big Data

AAAI 2014 Spring Symposium, March 24–26, 2014, Stanford University, Palo Alto, CA, USA

Symposia:
Big Data Becomes Personal: Knowledge into Meaning
Knowledge Representation and Reasoning in Robotics

AAAI 2013 Fall Symposium, November 15–17, 2013, Arlington, VA, USA

Symposia:
Discovery Informatics: AI Takes a Science-Centered View on Big Data
Semantics for Big Data
Social Networks and Social Contagion: Web Analytics and Computational Social Science

AAAI 2013 Spring Symposium, March 25–27, 2013, Stanford University, Palo Alto, CA, USA

Symposia:
Lifelong Machine Learning
Weakly Supervised Learning from Multimedia

AAAI 2012 Fall Symposium, November 2–4, 2012, Arlington, VA, USA

Symposia:
Information Retrieval and Knowledge Discovery in Biomedical Text
Robots Learning Interactively from Human Teachers
Social Networks and Social Contagion

AAAI 2012 Spring Symposium, March 26–28, 2012, Stanford University, Palo Alto, CA, USA

Symposia:
Wisdom of the Crowd

AAAI 2011 Fall Symposium, November 4–6, 2011, Arlington, VA, USA

Symposia:
Open Government Knowledge: AI Opportunities and Challenges

AAAI 2011 Spring Symposium, March 21–23, 2011, Stanford University, Palo Alto, CA, USA

Symposia:
Help Me Help You: Bridging the Gaps in Human-Agent Collaboration

AAAI 2010 Fall Symposium, November 11–13, 2010, Arlington, VA, USA

Symposia:
Commonsense Knowledge
Computational Models of Narrative
Manifold Learning and Its Applications

AAAI 2010 Spring Symposium, March 22–24, 2010 Stanford University, Palo Alto, CA, USA

Symposia:
Linked Data Meets Artificial Intelligence

AAAI 2009 Spring Symposium, March 23–25, 2009, Stanford University, Palo Alto, CA, USA

Symposia:
Intelligent Narrative Technologies II Learning by Reading and Learning to Read
Social Semantic Web: Where Web 2.0 Meets Web 3.0

AAAI 2008 Spring Symposium, March 26–28, 2008, Stanford University, Palo Alto, CA, USA

Symposia:
Semantic Scientific Knowledge Integration
Social Information Processing
Symbiotic Relationships between Semantic Web and Knowledge Engineering

AAAI 2007 Fall Symposium, November 9-11, 2007, Arlington, VA, USA

Symposia:
Computational Approaches to Representation Change during Learning and Development
Emergent Agents and Socialities: Social and Organizational Aspects of Intelligence
Intelligent Narrative Technologies

AAAI 2007 Spring Symposium, March 26–28, 2007, Stanford University, Palo Alto, CA, USA

Symposia:
Control Mechanisms for Spatial Knowledge Processing in Cognitive / Intelligent Systems

AAAI 2006 Fall Symposium, October 13–15, 2006, Arlington, VA, USA

Symposia:
Semantic Web for Collaborative Knowledge Acquisition

AAAI 2006 Spring Symposium, March 27-29, 2006, Stanford University, Palo Alto, CA, USA

Symposia:
Semantic Web Meets e-Government

AAAI 2005 Fall Symposium, November 3-6, 2005, Arlington, VA, USA

Symposia:
Agents and the Semantic Web

AAAI 2005 Spring Symposium, March 21–23, 2005, Stanford University, Palo Alto, CA, USA

Symposia:
Knowledge Collection from Volunteer Contributors

AAAI 2004 Spring Symposium, March 22-24, 2004, Stanford University, Palo Alto, CA, USA

Symposia:
Knowledge Representation and Ontology for Autonomous Systems

AAAI 2003 Spring Symposium, March 24-26, 2003, Stanford University, Palo Alto, CA, USA

Symposia:
Agent-Mediated Knowledge Management (AMKM)
Intelligent Multimedia Knowledge Management (IMKM)

AAAI 2002 Spring Symposium, March 25-27, 2002, Stanford University, Palo Alto, CA, USA

Symposia:
Collaborative Learning Agents
Mining Answers from Texts and Knowledge Bases
Safe Learning Agents

AAAI 2001 Spring Symposium, March 26-28, 2001, Stanford University, Palo Alto, CA, USA

Symposia:
Answer Set Programming: Towards Efficient and Scalable Knowledge Representation and Reasoning

AAAI 2000 Spring Symposium, March 20-22, 2000, Stanford University, Palo Alto, CA, USA

Symposia:
Bringing Knowledge to Business Processes

AAAI 1997 Fall Symposium, November 8–10, 1997, Massachusetts Institute of Technology, Cambridge, MA, USA

Symposia:
Context in Knowledge Representation (KR) and Natural Language (NL)

AAAI 1997 Spring Symposium, March 24-26, 1997, Stanford University, Palo Alto, CA, USA

Symposia:
Artificial Intelligence in Knowledge Management (AIKM)

AAAI 1996 Fall Symposium, November 9-11, 1996, Massachusetts Institute of Technology, Cambridge, MA, USA

Symposia:
Knowledge Representation Systems Based on Natural Language

Co-located Conferences:
International Conference on the Principles of Knowledge Representation and Reasoning (KR), Nov 5-8

AAAI 1996 Spring Symposium, March 25 – 27, 1996, Stanford University, Palo Alto, CA, USA

Symposia:
Acquisition, Learning and Demonstration: Automating Tasks for Users

AAAI 1995 Fall Symposium, November 10 – 12, 1995, Massachusetts Institute of Technology, Cambridge, MA, USA

Symposia:
Active Learning
Adaptation of Knowledge for Reuse
AI Applications in Knowledge Navigation and Retrieval

AAAI 1995 Spring Symposium, March 27 – 29, 1995, Stanford University, Palo Alto, CA, USA

Symposia:
Information Gathering from Heterogeneous, Distributed Environments

AAAI 1994 Fall Symposium, November 4–6, 1994, New Orleans, LA, USA

Symposia:
Knowledge Representation for Natural Language Processing in Implemented Systems
Planning and Learning: On to Real Applications

AAAI 1992 Spring Symposium, March 25-27, 1992, Stanford University, Palo Alto, CA, USA

Symposia:
Cognitive Aspects of Knowledge Acquisition
Knowledge Assimilation
Propositional Knowledge Representation