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Properties



Knowledge Representation

The knowledge of the system is rooted firmly in frames. The frame represents
all
kinds of knowledge, including problem spaces, statements of fact, and beliefs
of those facts.  This representation aids the indexing of knowledge to 
relevant problem spaces, because these frames should have the same prefix.




Learning

Learning in Theo is used to infer new stimulus-response behavior rules which it
can store with its current set of reactive rules. 
Theo's planning capability is invoked when the none of the preconditions 
for this current set apply.  That is, Theo plans only 
when necessary.



When Theo is forced to plan, a new rule is learned to cover the recently 
planned decision, such that the rule will make the same decision as the
planner.  The learning in Theo can therefore be described as impasse-driven
learning.




Organization

Here is a list of the properties this architecture benefits from:


Here is a list of the properties this architecture is hampered by:





General Performance

Theo is equipped with a reactive decision maker, such that it can quickly 
react to changes in a dynamic environment.  It does this with the use of
both eager and lazy sensing.  Eager sensing is used in order to make the 
system reactive; it constantly indexes a set of stimulus-response rules, and
when the preconditions for one is met, it acts. Lazily sensed features 
however are only sensed when needed.  This keeps the system focused, because
less sensing equates to less thinking, which frees our limited resources.
In addition, the learning capability of Theo is
used to constantly update and revise the behavioral rules.




Other