An agent-based model to simulate and analyse behaviour under noisy and deceptive information

Abstract

This paper presents an agent-based model to analyse behaviour produced under noisy and deceptive information conditions. A simple yet powerful simulation environment is developed where adaptive agents act and adapt to varying levels of information quality that they sense about their environment. The simulation environment consists of two types of agents moving in a bounded two-dimensional continuous plane: a neuro-evolutionary learning agent that adapts its manoeuvreing strategies to escape a pre-programmed deceptive agent; and a pre-programmed agent, whose goal is to capture the adaptive agent, that acts on noisy information about the adaptive agent’s manoeuvres that it senses from the environment. The pre-programmed agent is also able to produce deceptive actions to confuse the adaptive agent. The behaviour is represented in terms of the manoeuvreing strategies that the agents adopt as their actions to the environmental changes. A behaviour analysis methodology is developed to compare agent actions under different information conditions, that elicits interesting relationships between behaviour and the studied information conditions. The framework is easily extendable to analyse human behaviour in similar environments by replacing the adaptive agent with an interactive human–machine interface.