The IEEE International Conference on Tools with Artificial Intelligence (ICTAI) is a leading IEEE-CS annual scientific meeting for three decades. It provides a major international forum where the creation and exchange of ideas related to artificial intelligence are fostered among academia, industry, and government agencies. The conference facilitates the cross-fertilization of these ideas and promotes their transfer into practical tools, for developing intelligent systems and pursuing artificial intelligence applications. The ICTAI encompasses all technical aspects of specifying, developing and evaluating the theoretical underpinnings and applied mechanisms of the AI-based components of computer tools such as algorithms, architectures and languages.

Conference Categories

AI Foundations

    Evolutionary computing, Bayesian and Neural Networks
    Decision/Utility Theory and Decision Optimization
    Search, SAT, and CSP
    Description Logic and Ontologies

AI in Domain-specific Applications

    AI in Computational Biology, Medicine and Biomedical Applications
    AI in WWW, Communication, Social Networking, Recommender Systems, Games and E-Commerce
    AI in Finance and Risk Management

AI in Computer Systems

    AI in Robotics, Computer Vision and Games
    AI in Natural Language Processing
    AI in Software Engineering, Real-Time and Embedded Applications, and Sensor Networks
    AI in Cloud Computing, Data-Intensive Applications and Online/Streaming and Multimedia Systems
    AI in Web Search and Information Retrieval
    AI in Computer Security, Data Privacy, and Information Assurance

AI in Data Analytics and Big Data

    Visual Analytics for Big Data
    Computational Modeling for Big Data
    Large-scale Recommendation and Social Media Systems
    Cloud/Grid/Stream Data Mining for Big Velocity Data
    Semantic-based Big Data Mining

AI in Smart Cities

    Healthcare, Traffic, Transportation, Environment, etc.

AI Synergistic models

    Bridging AI Models for Advanced AI.

Machine Learning

    Dimension Reduction and Feature Selection computing
    Learning Graphical Models and Complex Networks
    Active, Cost-Sensitive, Semi-Supervised, Multi-Instance, Multi-Label and Mulit-Task Learning
    Transfer/Adaptive, Rational and Structured Learning
    Preference/Ranking, Ensemble, and Reinforcement Learning
    Learn continuous-Incremental Learning

Knowledge Representation, Reasoning and Cognitive Modelling

    Knowledge Representation, Reasoning
    Knowledge Extraction, Management and Sharing
    Case-based Reasoning and Knowledge-based Systems
    Cognitive Modelling and Semantic Web

AI and Decision Systems

    Decision Guidance and Support Systems
    Optimization-based Recommender Systems
    Group, Distributed, and Collaborative Decisions
    Crowd-sourcing and Collective Intelligence Decision-making
    Strategic, Tactical and Operational-level Decisions
    Decision-making in Social and Mobile Networks

Uncertainty in AI

    Uncertainty and Fuzziness Representation and Reasoning
    Approximate/Exact Probabilistic Inference
    Knowledge Discovery and Data Mining for Uncertain

AI and Societal Impact

    AI Fairness, Accountability and Transparency
    AI interpretability and explainability
    Ethical and Trustworthy AI