The main challenge of Multi-agent Intelligent Systems is in the development of
integrated knowledge-intensive computation processes and structures capable of
acquiring data, integrating it with knowledge, transforming knowledge, learning
from experience, and creating new knowledge by incorporating arrivals from
multiple disciplines and sensors. This formidable problem can be successfully
addressed using a novel approach termed Neural Modeling Fields or Dynamic
Logic, which consists of an iterative, self-consistent  process of maximizing
similarity between models and incoming signals.
This special session provides a forum for the presentation of the latest data,
models, results, and future research directions on the theoretical
understanding as well as on the applications of Neural Modeling Fields to
real-world problems with emphasis on the fusion of  distinct nature input
signals.
The special session invites submissions in any of the following areas:

•    Models and Similarity Measures for Recognition and Understanding of Images,
Text, Language, and Situation Analysis
•    Neural network models of higher cognitive functions
o    including the beautiful, sublime, wisdom
•    Neural mechanisms of emotions, cognition
•    Neural mechanisms of cultures
•    Neural mechanisms of interactions among language and cognition
•    Biological evolution of communication and language
•    Robots with high cognition and language
•    Applications in the above areas