# General Environment Description Language
## 1 Introduction  
For a long time, engineers have been using different techniques to control systems without directly supervising them. Various types of mechanical control systems were perfected by their creators, obtaining the highest forms as windmills, steam machines, locomotives, cars and entire factories. As a result of the development of science, however, new technical possibilities have emerged, enabling the creation of decision-making systems, e.g., using biological, chemical, quantum and electronic systems. Advanced information processing capabilities mean that we are not currently dealing only with simple control systems, but very complex systems, having the ability to acquire information, collect it, transform it into knowledge and even process the knowledge. The indicated capabilities allow us to create more and more advanced systems, such as banking advisory systems, autonomous cars, robots cooking meals or vacuum cleaners cleaning our apartments automatically. Currently, developed and manufactured systems are designed to support very specific tasks with a relatively narrow spectrum of applications. Those systems often use deep learning techniques that achieve very good results, e.g., in image recognition, creation of artificial images or text.  
The challenge now is to create general-purpose systems that would enable both learning about the environment in an autonomous manner and the performance of commissioned tasks in a diverse environment. The implementation of general-purpose systemswould not be possible without the development of systems that perform specific tasks, since they are the basis for the correct interpretation of the data flowing from the sensors, and they make it possible to perform the activities that make up the entire large task.  
However, it can be stated that technological progress is already at such a high level that it will be possible to develop an intelligent general-purpose autonomous system and implement it into practical solutions. The use of such systems would, of course, be extensive, starting with home applications, in which the robot would perform simple operations such as cooking and cleaning, continuing with applications in industry and agriculture, and ending with applications in the space industry.  
Work on general-purpose artificial intelligence has been going on for over 20 years. However, it should be noted that despite the fact that the new proposals and solutions are emerging in this area, unfortunately, no significant and noticeable breakthrough has been made that would enable widespread implementation of the proposed technologies. The vast majority of the AI field today is concerned with what might be called \"narrow AI\"--concentrated on creating programs that demonstrate intelligence in one specialized area [1]. There is still a need to work on general solutions that would constitute a framework enabling, on the one hand, the integration of already developed \"narrow AI\" solutions, and on the other hand, general problem-solving.  
In the following article, a new proposal in the field of general-purpose artificial intelligence is discussed. We present a new language that may be used to describe almost any environment in which robots or agents can potentially work. In addition, the presented language makes it possible to give meaning to objects observed in the environment and to plan tasks using the objects.