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
