Chaos and Correlation
International Journal, March 16, 2008

Artificial Intelligence System for Identification of Social Categories of Natives Based on Astronomical Parameters

Eugene Lutsenko (Russia), Alexander Trunev (Canada)

The cognitive simulation of AstroDatabank records by using the Artificial Intelligence System – AIDOS, is reviewed in this paper. The technology of simulation is described and the mostly important results are discussed.  

Keywords: Semantic Information Models, astrodatabank, Astronomical and Sociological Databases, Neuron-net Training, Numerical Experiment.

Introduction

New method of identification of a birth chat based on system-cognitive analysis and on the advanced information theory [1] was developed recently [2-3]. This method differs from the normal astrological models so that the birth chat is not interpreted, but it is identified by using a number of attributes and categories, by comparing with the astrological database [4-5], which includes a description of the many key events in real life of real persons. As a result of the identification each person receives a customized description contains classes and categories of events, indicating the likelihood of their implementation.  In this research not used any astrological interpretation or any astrological rules. Statistical patterns and the correlation revealed in the data processing of the artificial intelligence system by comparing birth charts and biography. Test examples demonstrate the effectiveness of the system for the recognition of certain classes of entities.

Input Databases

The main source of astrological database prepared for the artificial intelligence system simulation is the original (first version) Lois Rodden's AstroDatabank [4] and AstroDatabank v. 4.0 [5]. These databases contain biography of famous and ordinary people so that all the categories and events of life are classified and ordered.

Data imported from AstroDatabank v. 4.0 were converted into a DBF4 format database. Only 9897 records have been utilized including 5 categories shown below with corresponding number of records:

Table 1: Four classes, 5 categories and related number of records

Note, 184 records are repeated among 9897 since they related to 2, 3 or 4 categories listed above.  Records were cooperated in four classes as shown in Table 1. Every record has 23 active numerical cells consist of coordinates of celestial bodies, Ascendant and Midhaven at the moment of birth and in the place of birth, i.e.:

·        Longitude (degree) of the Sun, the Moon, Mercury, Venus, Mars, Jupiter, Saturn, Uranus, Neptune, Pluto, North Node, Ascendant and Midhaven;

·        Declination (degree) of the Sun, the Moon, Mercury, Venus, Mars, Jupiter, Saturn, Uranus, Neptune, Pluto.

From this database were derived two databases to study a declination effect on the similarity parameter:

1.      Database1 with 23 active numerical cells in every of 9897 records as described above but all Declination parameters were adapted to the longitude interval (0; 360) by using formula: Declination1 = (Declination +30)*6;

2.      Database0 with 23 active numerical cells in every of 9897 records as described above, but all Declination parameters were recalculated as follows: Declination0 = Declination *0, also for all records we put Ascendant= Midhaven =0, therefore only Longitude of the Sun, the Moon, Mercury, Venus, Mars, Jupiter, Saturn, Uranus, Neptune, Pluto and North Node have been utilized in this database.

After this minor adaptation all 23 cells have one scale and format, therefore they could be analyzed in the same manner as well as the declination parameter effect on the simulated outcomes could be studied. 

The data imported from original Lois Rodden's AstroDatabank were converted into the Borland JDataStore format databases. Then, the data were sorted using SQL queries and special functions written in Java.  Only 20007 records related to 1931 categories and events have been utilized in this research. For these records were calculated coordinates of celestial bodies (latitude and longitude in degrees, and the distance in astronomical units).  12 cusps of astrological houses in the Placidus system were calculated for records with the exact time of birth. The ephemeredes following celestial bodies and points were established: the Sun, the Moon, Mercury, Venus, Mars, Jupiter, Saturn, Uranus, Neptune, Pluto, and North Node.  The next step is sorting by category of records.  As result XML tree categories reference database was obtained. Next, the database has been completely exported in Excel and then it converted to the DBF4 format (which accepted by the artificial intelligence system).  Only 23 active numerical cells in every of 20007 records were utilized in this research, i.e.: Longitude (degree) of the Sun, the Moon, Mercury, Venus, Mars, Jupiter, Saturn, Uranus, Neptune, Pluto, North Node, and 12 cusps of the astrological houses (houses in the Placidus system). From this database were derived several databases:

1.      Database A  of 20007 records related to 500 representative categories (category represented in the database at least 26 times);

2.      Database B of 15007 records related to 500 representative categories - training data set;

3.      Database C of 5000 records which are not used in the Database B (but used in the Database A) – recognized data set.

4.      Database D  of 20007 records related to 240 unrepresentative categories (number of records related to category higher than 2 and less than 25) – low frequency limit;

5.      Database E  of 20007 records related to 870 categories (number of records related to any category higher than 2) – mostly complete database;

6.      Database F of 20007 records related to 37 categories (number of records related to any category higher than 1000) – higher frequency limit;

7.      Database F1 of 20007 records related to 100 categories (number of records related to any category higher than 174);

8.      Database G of 20007 records related to 4 categories listed below in Table 2, b. In this database 8150 records are not involved in a simulation.

Table 2: Four classes, four categories and related number of records in a case of Database G

Note 20007 records are related to the original (first version) Lois Rodden's AstroDatabank [4] and AstroDatabank v. 4.0 [5] as well.  The difference between these databases is that latest version updated with more than 5000 records, and it is a reason why the same category SPORT has different records in Table 1 and 2.   

The Model and the Artificial Intelligence System - AIDOS

As well know there are several ways to decompose Zodiac circle in a process of analyzing a birth chart:

·        day and night houses partition – 2 sectors;

·        Cardinal signs, fixed and mutable signs – 3 multiply connected sectors.

·        squares – 4 sectors;

·        partition based on element of fire, earth, air and water – 4х3 sectors;

·        zodiac signs – 12 sectors;

·        decants – 36 sectors;

·        terms – 60 sectors;

·        Degree – 360 sectors.

Decomposition combinations such as those listed above seem to resemble algorithms of grid simulation widely used in a modern science, in which condensation of the grid helps improve convergence in solving the task. We utilized this method in order to perform packet recognition of 9897 or 20,007 records exported from AstroDatabank and presented as DBF4 format databases.  In order to do this a solution founded based on data from 172 grids of various dimensions, containing 2, 3, 4, ..., 173 sectors consequently (it is a limit for this task at the moment). Thus the net entropy effect could be established during this simulation with the system of artificial intelligence AIDOS [2]. 

Standard AIDOS package includes 7 subsystems and 85 programmable applications organized in a block structure – see Table 1. Generally speaking it is a neuron-net computer application running under Windows XP in MS-DOS mode, designed with CLIPPER 5.01, Tools-II and BiGraph 3.01, provided the following objectives:

1.      Synthesis and adaptation of the semantic data model.

2.      Identification and forecasting.

3.      Precise analysis of the semantic data model.

Table 3: Generalized Structure of the Universal Cognitive Analytical System AIDOS, v. 12.03.2008

The cognitive simulation of AstroDatabank records including the neuron-net training and recognition was realized for any grid of fixed dimension N=2, 3, 4…, 173 sectors. Thus there are many models – M2, M3, M4… M173 corresponding to the number of sectors in a given partition of Zodiac. For every model could be established own catalog (they are numbered simply as 002, 003, 004 …) and a copy of the system AIDOS.  To manage the input parameters and outcomes of all models a special system has been designed [3], which would be implemented "collectives decisive rules» i. e., would the ability to automatically generate a number of models that would form one coherent system, which called "multi-model". This system consists of few programmable applications which allow setup any combination of models; run the neuron-net training and recognition for all models,   organize and summarize the results of the identification of the respondents in different models for a set of categories.

Main Results

The technology of simulation described in papers [6-8]. In fact the system AIDOS operates with Object Code like numbers in a left column in Tables 1, 2. Astronomical parameters also have own code called “scale or graduation code”, for instance, in a case of model M3 we have 23 main scales and 69=23*3 graduations; six of them shown below:

If any record in a training database shows a longitude of the Sun belongs to the interval (0.000, 120.000) then a frequency of the corresponding code 1 increases on a unit. Therefore a frequency of scales in the training database could be calculated and the frequency matrix and the information matrix could be established. For example, in a case of model M2 trained with Database F, a fragment of the frequency matrix and a fragment of the information matrix are shown in Table 4a and 4b consequently:

Table 4a: The frequency matrix (fragment) in a case of model M2 trained with Database F (frequency is given in absolute value) [7]

Actually an information counted in the system with 8 decimal places, but in Table 4b it shown with  2 decimal position (*100) only. A positive or negative value of information in a cell ij in Table 4b means that category j has a positive or negative correlation with scale i.

Table 4b: The information matrix (fragment) in a case of model M2 trained with Database F (information given in Bit*100) [7]