Abstraction is a valuable tool for dealing with scalability in large state
space contexts. This paper addresses the design, using abstraction, of good policies
for minimal autonomous agents applied within a situation-graph-framework.
In this framework an agent’s policy is some function that maps perceptual inputs
to actions deterministically. A good policy disposes the agent towards achieving
one or more designated goal situations, and the design process aims to identify
such policies. The agents to which the framework applies are assumed to have
only partial observability, and in particular may not be able to perceive fully a
goal situation. A further assumption is that the environment may influence an
agent’s situation by unpredictable exogenous events, so that a policy cannot take
advantage, of a reliable history of previous actions. The Bellman discount measure
provides a means of evaluating situations and hence the overall value of a
policy. When abstraction is used, the accuracy of the method can be significantly
improved by modifying the standard Bellman equations. This paper describes
the modification and demonstrates its power through comparison with simulation
results.