@author By Zhenhua Cai
@date   2011-08-01

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What is MAM?

MAM stands for Multilayer Affective Modulation, which is an emotional model for multi-agents modelling or robotics in the real world. The origin of the project is for my PhD thesis. However, I finally decided to make something really useful. My goal is to make MAM flexible enough that can be used for both cognitive/ emotional research and engineering usage. MAM borrowed many source code from my previous project TinyPsi (at http://sourceforge.net/projects/tinypsi/ ), which is not active any more. 

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Architecture

Lowest layer, reactive layer, related to unconsciousness behavior
Middle layer, goal oriented layer, related to rational planning
Abstract layer, reflective layer, related to self-consciousness

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Knowledge representation

Long term, preserve for leaning (data mining), graph storage
Short term, ready to use (reasoning), all sorts of representations

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Directory arrangement

mind:        base class of mind server and agent
mam:         mind agents for multilayer affective modulation 
knowledge:   get/set knowledge base (using Object Relation Mapping)
reasoning:   backward/ forward chaining
learning:    all sorts of learning/ data mining algorithms on long term memory
message:     message system that connects different components, especial mind and world
world:       sample worlds and their corresponding world interface, knowledge
             database and scripts. We should add ros for robotics in future
visualizer:  visualization tools to better understand the whole system
tutorial:    demos of using mam (low priority)
test:        unit tests (low priority)
lib:         searching library cmake scripts
util:        miscellaneous utilities               
third_party: third party packages            
doc:         all sorts of documents, including our papers and references
bin:         all sorts of executive files         

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Planning

deadline: submit a paper by the end of October

01 week: technical ideas for all the components 
02 week: paper ideas, outlines and reuse code from tinypsi
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