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