The Effect of Parameters on the Cybernetic Automaton

The Cybernetic Automaton, created by Dr. Brian L. Stuart, is an adaptive automaton that exhibits machine learning. This particular automaton demonstrates the different properties in classical conditioning such as first-order delay conditioning, extinction, and latent inhibition. These properties are based upon three parameters that control the rate at which expectation is gained and lost, the rate at which confidence is lost, and the rate at which learning occurs. The parameters are then adjusted both higher and lower from the original value. The ultimate goal of this research is to analyze the effects the parameters had on the adaptive automaton.

In this research project, a program was developed that implemented the steps of the Cybernetic Automaton to examine the different properties of learning. The machine ran with its original set of parameters along with its newly adjusted set of parameters. From there, the data of the experiment was collected and graphed. This study shows the accuracy of the adaptive automaton and how each of the parameters affected the original results.

College of Computing & Informatics

Daniel Schwartz

College of Computing & Informatics

Computer Science

Faculty Mentor: Dr. Brian L. Stuart

Computer Science

Emulating Natural Intelligence in an Adaptive Finite State Automaton with Probabilistic Outputs

Artificial Intelligence (AI) is the theory and development of computer systems able to perform tasks that normally require human intelligence. In previous AI research, there has been little success in creating biologically plausible models that mimic naturally intelligent behavior. This research focused on understanding how learning works through decision making. The model developed was an adaptive finite state automaton with probabilistic outputs. Through a series of experiments, we determined if the model in question presented key properties psychologists have found in nature through classical and operant conditioning. The objectives of this research were to reproduce past results to corroborate the validity of the model illustrating natural intelligence and portraying a biologically accurate structure. This investigation was accomplished through simulating 100 trials of each experiment to compare the results to data observed in nature. In the end, the results were positive and showed that this model is a compelling candidate for mirroring low level natural intelligence and clearly demonstrated many properties of conditioning studied by psychologists.