In this paper we offer a formal definition of Artificial Intelligence and this directly gives us an algorithm for construction of this object. Really, this algorithm is useless due to the combinatory explosion. The main innovation in our definition is that it does not include the knowledge as a part of the intelligence. So according to our definition a newly born baby also is an Intellect. Here we differs with Turing's definition which suggests that an Intellect is a person with knowledge gained through the years.

With Reinforcement Learning we assume that a model of the world does exist. We assume furthermore that the model in question is perfect (i.e. it describes the world completely and unambiguously). This article will demonstrate that it does not make sense to search for the perfect model because this model is too complicated and practically impossible to find. We will show that we should abandon the pursuit of perfection and pursue Event-Driven (ED) models instead. These models are generalization of Markov Decision Process (MDP) models. This generalization is essential because nothing can be found without it. Rather than a single MDP, we will aim to find a raft of neat simple ED models each one describing a simple dependency or property. In other words, we will replace the search for a singular and complex perfect model with a search for a large number of simple models.

The aim of the study was to develop a suitable pre-treatment (and more specifically, the etching operation) of 3D-printed PET and PETG samples with different filling densities of the inner layers for subsequent electroless metallization. The influence of temperature, etching time, and sodium hydroxide concentration in the etching solution on the deposition rate, adhesion, and composition of Ni-P coatings was determined. The studies show that a high temperature and concentration of the etching solution do not improve the properties of the coating. The etching not only plays an important role in improving adhesion but also affects the composition and thickness of the nickel layer. It was also established how the degree of filling densities of the inner layers affects the uniformity, penetration depth, and thickness of electrolessly deposited Cu and Ni-P coatings on 3D PETG samples.

This is the second part of the paper. In this part we will use the world of the chess game in order to create the language we are looking for. We will show how a complex world can be described in a simple and understandable way. Before describing the movement of chess pieces, we will need to extend the concept of algorithm. The new concept describes the algorithm as a sequence of actions performed in an arbitrary world. In the meaning of the new concept, a cooking recipe is also an algorithm. If we look at a world in which there is an infinite tape and a head which travels over the tape, then the algorithm of that world will be a Turing machine. This means that the new concept of algorithm is a generalization of the old one. Computer programs are algorithms both in the new concept and in the old one, however, there are many other sequences of actions which extend the concept.

We will reduce the task of creating AI to the task of finding an appropriate language for description of the world. This will not be a programing language because programing languages describe only computable functions, while our language will describe a somewhat broader class of functions. Another specificity of this language will be that the description will consist of separate modules. This will enable us look for the description of the world automatically such that we discover it module after module. Our approach to the creation of this new language will be to start with a particular world and write the description of that particular world. The point is that the language which can describe this particular world will be appropriate for describing any world. This is the first part of the paper. In this part we will define the basic theoretical concepts which will we will need when creating the language we are looking for.

We will consider all policies of the agent and will prove that one of them is the best performing policy. While that policy is not computable, computable policies do exist in its proximity. We will define AI as a computable policy which is sufficiently proximal to the best performing policy. Before we can define the agent's best performing policy, we need a language for description of the world. We will also use this language to develop a program which satisfies the AI definition. The program will first understand the world by describing it in the selected language. The program will then use the description in order to predict the future and select the best possible move. While this program is extremely inefficient and practically unusable, it can be improved by refining both the language for description of the world and the algorithm used to predict the future. This can yield a program which is both efficient and consistent with the AI definition.