The combination of different intelligent methods is a very active research area in Artificial Intelligence (AI). The aim is to create integrated or hybrid methods that benefit from each of their components. It is generally believed that complex problems can be easier solved with such integrated or hybrid methods. Some of the existing efforts combine what are called soft computing methods (fuzzy logic, neural networks and evolutionary algorithms) either among themselves or with more traditional AI technologies such as logic and rules. Another stream of efforts integrates case-based reasoning and machine learning with soft computing and traditional AI methods. Yet another integrates agent-based approaches with logic and non-symbolic approaches. Some of the combinations have been quite important and have been more extensively used, like neuro-symbolic methods, neuro-fuzzy methods and methods combining rule-based and case-based reasoning. However, there are other combinations that are still under investigation, such as those related to semantic web and deep learning as well as to swarm intelligence algorithms. In some cases, combinations are based on first principles, but in most cases, they are created in the context of specific applications.
The Workshop is intended to become annual forum for exchanging experience and ideas among researchers who are dealing with combining intelligent methods either based on first principles or in the context of specific applications. Topics of interest include (but not limited to) the following:

Topics of interest include (but are not limited to) the following:

Bayesian Networks
Case-Based Reasoning
Deep Learning
Ensemble learning, Ensemble methods
Evolutionary Algorithms
Evolutionary Neural Systems
Expert Systems and Knowledge-based Systems
Fuzzy-Evolutionary Systems
Hybrid Approaches for the Web
Hybrid Knowledge Representation Methods & Approaches
Hybrid and Distributed Ontologies
Information Fusion Techniques
Integrations of Neural Networks
Intelligent Agents Integrations
Integrations of Statistical and Symbolic AI Approaches
Intelligent Agents Integrations
Machine Learning Combinations
Neuro-Fuzzy Approaches/Systems
Reinforcement learning
Semantic Web Technologies Integrations
Swarm intelligence methods & integrations
Applications
Agents & Multi-agent Systems
Big Data
Biology, Computational Biology & Bioinformatics
Decision Support and Recommender systems
Economics, Business and Forecasting Applications
Education & Distance Learning
Industrial & Engineering applications
Medicine & Health Care
Multimodal Human Computer Interaction
Natural language Processing and Understanding
Planning, Scheduling, Search & Optimization
Robotics
Social Networks

This year we are very interested in combinations of intelligent methods for sentiment analysis and emotion recognition.