Proficient decision makers, in relatively familiar situations,
rapidly settle on situation interpretations and plans in the
face of new observations and changing goals. This kind of
situaion understanding and planning can be largely understood
in terms of causal knowledge structures that we call mental
The reflexive subsystem shows how mental models can be generated
and maintained in a dynamic situation. The starting point
of the reflexive subsystem was a system called Shruti,
developed by Professor
Lokendra Shastri, of the
International Computer Science Institute, University of California
at Berkeley (Shastri & Ajjanagadde, 1993). Dr. Shastri
is a long-time collaborator of CTI.
Shruti is unique in combining speed and scalability,
with representation of crucial relational aspects
of real-world decision making. To accomplish speed and scalability,
Shruti utilizes parallel, connectionist processing. To keep
track of relational reasoning, Shruti uses temporal synchrony
of firing in nodes throughout the network to represent information
about the same object. (Most connectionist models represent
fuzzy similarity relations that blur the way objects interact.
Symbolic architectures easily represent relational facts,
but are usually slow and non-scalable.)
In Shruti, activation goes out from nodes that represent
either sensory or linguistic inputs and/or an internally generated
question to be settled, and returns when circuit is completed
by other nodes that are linked to them. Shruti represents
a situation model and/or decision by the emergence of a stable
activation cycle within the network.
Extensions to Shruti were necessary both to improve its representation
of reflexive reasoning and to make it work in conjunction
with a reflective subsystem. Among the extensions that we
have developed are:
- Propagation of utility as well as belief, so that Shruti
settles on actions at the same time as it settles on a situation
- Reasoning both backward, to find explanations, and forward,
to generate predictions.
- Mechanisms required for shifting attention, such as (a)
priming effects that temporarily store results through a
series of attentional shifts, and (b) long-term storage
of aggregated results at the edge of the currently active
part of the network.
- Tuning of link strengths through backpropagation.
Attentional limits on dynamic access to long
term memory (LTM) emerge naturally from the computational
structure of Shruti and the neuro-biological constraints it
respects. These inmply that not all information known by the
agent can be brought to bear on a problem at the same time
by purely reflexive processes.