Combinations of Intelligent Methods

Intelligent Systems Reference Library bookseries, Springer


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.


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

Paper Submission

The intention of this book is to provide a concise coverage of the particular topics. As such, each chapter must be complete within itself. Each chapter must include an abstract, as well as a bibliography of references to additional or more advanced material. Co-authored chapters are acceptable. We are interested in high quality research papers on the above or related topics.

Paper format guidelines: https://www.springer.com/gp/computer-science/lncs/conference-proceedings-guidelines ).

All contributions should be submitted in pdf format at easychair https://easychair.org/conferences/?conf=cima2018
All papers will be refereed by at least two reviewers, experts in the field.

Past Books-Publications

Important Dates

Paper Submission :   30 April 2019
Author Notification:   30 May 2019
Revised paper Submission: 30 June 2019
Final Paper Submission: 30 July 2019
Final Decision Notification 30 August 2019


Ioannis Hatzilygeroudis (Contact Editor)
Department of Computer Engineering & Informatics
University of Patras
26504 Patras
URL: http://aigroup.ceid.upatras.gr/ihatz

Foteini Grivokostopoulou
Department of Computer Engineering & Informatics
University of Patras
26504 Patras
URL: http://aigroup.ceid.upatras.gr/grivokostopoulou

Isidoros Perikos
Department of Computer Engineering & Informatics
University of Patras
26504 Patras
URL: http://aigroup.ceid.upatras.gr/perikos

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