Frames represent concepts. A frame with a slot and a value is said to be a belief. A frame with a missing value is said to be a problem. Such a frame triggers problem solving as Theo attempts to infer the slot value.
Theo infers slot values of an entity by a three-step process. The first step is to directly calculate values via a LISP function in the TOGET slot of the entity. If there is no TOGET slot, the next step uses a list of methods to infer values. Methods include:
A statistical inference learning module (SE) attempts to order these methods, placing the methods most likely to succeed early in the list, so a value is produced in optimal time. The final step, implemented by the defines method, attempts to calculate the value either by definitions or by previous explanation-based learning.
During inference, Theo saves an explanation of how each slot received its value. These explanations serve as a simple truth maintenence mechanism. When any slot value in the system changes, then the values of all dependent slots are marked invalid, and recalculated when needed. The explanation is also generalized by a module named TMAC to create general macro-methods for the computation of similar slot values.
When a value has been successfully inferred, Theo caches the result, allowing efficient knowledge access . However, the caching of every single inferred value creates a huge knowledge base requiring special management techniques.
The three learning mechanisms, SE, TMAC, and caching , operate independently of each other, but in certain cases the results of one mechanism can interfere with the functioning of the other mechanisms.