A policy for a minimal reactive agent is a set of condition-action rules used to
determine its response to perceived environmental stimuli. When the policy pre-disposes the
agent to achieving a stipulated goal we call it a teleo-reactive policy. This paper presents a
framework for constructing and evaluating teleo-reactive policies for one or more minimal
agents, based upon discounted-reward evaluation of policy-restricted subgraphs of complete
situation-graphs. The main feature of the method is that it exploits explicit and definite
associations of the agent’s perceptions with states. The combinatorial burden that would
potentially ensue from such associations can be ameliorated by suitable use of abstractions.
The framework allows one to plan for a number of agents by focusing upon the behaviour
of a single representative of them. It allows for varied behaviour to be modelled, including
communication between agents. Simulation results presented here indicate that the method
affords a good degree of scalability and predictive power.