Sunday, 27 January 2019

Part 1: Artificial cognitive systems, Logical approach

I started my PhD studies in artificial intelligence, cognitive systems and machine learning. I would like to start brief introduction into the field starting by this post.

 The artificial cognitive systems aims to study the design and modeling of artificial agents to perform intelligent tasks. The artificial cognitive systems in general are divided into two main categories; artificial intelligence and machine learning.
The first category is artificial intelligence is the logical symbolic system that is based on rules and knowledge. In this category, the system is designed according to predefined set of logical rules. The agent follows these rules until it deducts the answer. In interactive system the deduction leads to the suitable action to take.
We implemented a simple toy example using an Arduino Uno microcontroller and a motor controller to control a mini car with two 12v motors. The task is to roam around avoiding any obstacle appears in front of it. The agent uses ultrasonic sensors for distance measurements. The actions are mainly turn right, turn left and break.
The rules where simply based on the sensor readings, the car turns to the opposite direction of the sensed obstacle, but if the obstacle is covering all sensors, it breaks. The car was able to roam around in a small hall occupied with some furniture.

Although the AI category sounds cool and smart, it lacks some important capabilities. First, it is restricted with the rules and knowledge it is programmed with. For example, if the mentioned mini car is staggering and not stable, we needs to reprogram the code to add this feature. On a bigger scale this is inappropriate, because it  requires checking the details of the programs that no rules are contradicting.
Second, another drawback is the need for symbolic representation. In the mentioned example the ultrasonic sensors are straight forward representation of the distance measurements. If more complicated sensors to be added, for example a camera, objects needs to be defined and labeled to manipulate rules on them. The logical method can't operate on non symbolic representation like raw images. This example will be examined in a separate post about machine learning (Part 1).

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