The following is an abstract I'm working on for a conference next year:
Our recently developed "Asa H" software architecture (KAS Trans. 109 (3/4): 159-167) consists of a hierarchical memory assembled out of clustering modules and feature detectors. Various experiments have been performed with Asa H 2.0: 1. Don't advance the time step and record input components until an input changes significantly. 2. Time can be made a component of the case vector. 3. There is a tradeoff between time spent organizing the knowledge base (to reduce search needed later) versus search through a less organized knowledge base. 4. If utility is low search. Stop search if utility rises. 5. Cost of action can be a vector. 6. Before deleting a small vector component test if utility is changed when its deleted. 7. Asa H has a number of parameters which are not easy to set. This set of parameters can be treated as a vector and Asa H can be run for a period of time while we record the utility gains. A second set of parameters can be employed during a run in the same environment and the utility gain again is recorded. With these vectors and utilities we can use the Asa H extrapolation algorithm in order to improve the parameter settings.