Simulating Behaviors Of Children With Autism Spectrum Disorders Through Reversal Of The Autism Diagnosis Process

Children affected by Autism Spectrum Disorders (ASD) exhibit behaviors that may vary drastically from child to child. The goal of achieving accurate computer simulations of behavioral responses to given stimuli for different ASD severities is a difficult one, but it could unlock interesting applications such as informing the algorithms of agents designed to interact with those individuals. This paper demonstrates a novel research direction for high-level simulation of behaviors of children with ASD by exploiting the structure of available ASD diagnosis tools. Building on the observation that the simulation process is in fact the reverse of the diagnosis process, we take advantage of the structure of the Autism Diagnostic Observation Schedule (ADOS), a state-of-the-art standardized tool used by therapists to diagnose ASD, in order to build our ADOS-Based Autism Simulator (ABASim). We first define the ADOS-Based Autism Space (ABAS), a feature space that captures individual behavioral differences. Using this space as a high-level behavioral model, the simulator is able to stochastically generate behavioral responses to given stimuli, consistent with provided child descriptors, namely ASD severity, age and language ability. Our method is informed by and generalizes from real ADOS data collected on 67 children with different ASD severities, whose correlational profile is used as our basis for the generation of the feature vectors used to select behaviors.