Concluding my last two posts, Artificial Intelligence Systems in Easy Word, there are four units and two phases for the majority of artificial intelligence systems, shown in the table below.
There are different ways of creating intelligent agents such as Neural Networks, Fuzzy Logic, and Classifier Systems. Sometime researchers combine these methods to create better agents for their specific question or they are using genetic algorithm to improve the intelligence of their system. In these series of blog posts, I don’t discuss these methods of creating intelligent agents, but help you to understand the requirements of creating artificial intelligence system and finding questions for this solution. Yes, finding some question to solve them with AI solution.
Here in these series of my blog post, I am creating a simple AI agent to improve the driving conditions of a semi-intelligent car, using the concepts of classifier systems. This example is simplified version of eXtended Classifier System (XCS) example from my notes from my very first AI class with Dr. Masoud Shariat Panahi, Associate professor of University of Tehran. In this example, we develop an intelligent agent to improve the driving conditions of a passenger car.
This car is equipped with six sensors in order to watch the environment and five actuators to make an action. Here’s the list of sensors and actuators.
Each true or wrong statement can be shown with a chromosome of numbers. In other words, we can standardize our statements by using chromosome filled with 0/1/# (# for a sensor stands for the condition of 0 or 1; and for an actuator stands for do nothing). For example, the statement “If windshield is wet, then turns on the wipers” is represented by:
Or even we can simplify it to 1#####:1####.
Out intelligent agent is a set of thousands rules like 1#####:1#### that can be true, false, or partially true. These rules are generated randomly. In my next blog post, I will train the system and explain how it is possible to create intelligent agent.