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Parallel between Definition of Chess Playing Program and Definition of AI
No full textIn this report we will explain some earlier papers [1, 2] which are about definition of Artificial
Intelligence and about perfect AI. The definition of AI is intuitive in [1] and formal in [2]. The perfect AI is a
program that satisfies the definition for AI but which is absolutely useless because of the combinatory explosion.
Most people do not understand these papers because they never saw AI and that is why for them the notion of AI
is too abstract. In this report we will make parallel between definition of chess playing program and definition of
AI. Of course, the definition of chess playing program is useless because people already know what this is.
Anyway, we will give you this definition because its construction follows closely the construction of the definition
of AI. Also the results are almost the same with the only difference that we can optimise the perfect chess playing
program in order to obtain a real chess playing program, but for the moment we cannot optimise the perfect AI in
order to obtain a real AI.
In this report we will not speak about AI. The only matter which we will observe will be about chess playing
programs. If you understand the construction and the results about chess playing programs then you can read the
papers [1, 2] and to see similar results about AI
Before we can find a model, we must forget about perfection
Get PDFWith 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
Language for Description of Worlds
Get PDFWe 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
The IQ of Artificial Intelligence
Get PDFAll it takes to identify the computer programs which are Artificial Intelligence
is to give them a test and award AI to those that pass the test. Let us say that
the scores they earn at the test is called IQ. We cannot pinpoint a minimum IQ
threshold that a program has to cover in order to be AI; however, we will choose
a certain value. Thus, our definition for AI will be any program whose IQ is above
the chosen value. While this idea has already been implemented in [3], here we
will revisit this construct in order to introduce certain improvements
Formal Definition of Artificial Intelligence
Get PDF* This publication is partially supported by the KT-DigiCult-Bg project.A definition of Artificial Intelligence (AI) was proposed in [1] but this definition was not absolutely formal
at least because the word "Human" was used. In this paper we will formalize the definition from [1]. The biggest
problem in this definition was that the level of intelligence of AI is compared to the intelligence of a human being.
In order to change this we will introduce some parameters to which AI will depend. One of this parameters will be
the level of intelligence and we will define one AI to each level of intelligence. We assume that for some level of
intelligence the respective AI will be more intelligent than a human being. Nevertheless, we cannot say which is
this level because we cannot calculate its exact value
Two Fundamental Problems Connected with AI
Get PDFThis paper is about two fundamental problems in the field of computer science. Solving these two
problems is important because it has to do with the creation of Artificial Intelligence. In fact, these two problems
are not very famous because they have not many applications outside the field of Artificial Intelligence.
In this paper we will give a solution neither of the first nor of the second problem. Our goal will be to formulate
these two problems and to give some ideas for their solution
Second Attempt to Build a Model of the Tic-Tac-Toe Game
Get PDF* This publication is partially supported by the Bulgarian Ministry of Education (contract БОЕ 4-02/2004)We want to make a program which can play any game or in other words we want to make AI. It is
impossible to include in this program the rules of all games and that is why our program should be able to find
these rules by itself. We cannot solve this problem in the general case. So, our first task will be to make a
program which is able to find the rules of the Tic-Tac-Toe game. Even this task is too complicated. So, first we
will try to find these rules manually and this will help us make a program which is able to find these rules
automatically
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
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