1 / 22

On a Game Method for M  delling with Intuitionistic Fuzzy Estimations. Part 2

This paper introduces a new extension of the standard Conway's Game of Life (CGL) by incorporating intuitionistic fuzzy estimations and the game-method for modeling. The authors propose a set of rules for object movements and interactions, and define a proximity criterion for determining closeness between configurations. The method allows for logical consistency of the rules and can be applied to various domains.

ialbert
Download Presentation

On a Game Method for M  delling with Intuitionistic Fuzzy Estimations. Part 2

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. On a Game Methodfor Mdelling with Intuitionistic Fuzzy Estimations. Part 2 Lilija Atanassova Institute of Information and Communication Technologies Krassimir Atanassov Institute of Biophysics and Biomedical Engineering 8th IMACS Seminar on Monte Carlo Methods, Borovets, 29 August – 2 September 2011

  2. Introduction • In a series of papers, the authors extended the standard Conway's Game of Life (CGL), adding in it intuitionistic fuzzy estimations. • On the other hand, more than 30 years ago the authors introduced the idea for another extension of CGL, called “Game-method for modelling” (GMM). • Here, we will introduce a new extension of the standard CGL on the basis of both discussed modifications.

  3. Crisp Description of GMM • The standard CGL has a “universe" which is an infinite two-dimensional orthogonal grid of square cells. • Each cell is in one of two possible states: alive or dead, or (as an equivalent definition) in the square there is an asterisk, or not. The first situation corresponds to the case when the cell is alive and the second – to the case when the cell is dead.                       

  4. Extension of the Crisp GMM • Let us have a set of symbols S and an n-dimensional simplex comprising of n-dimensional cubes (at n = 2, a two-dimensional net of squares). • Let material points (or, for brief, objects) be found in some of the vertices of the simplex and let a set of rules A be given, containing: 1) rules for the movements of the objects along the vertices of the simplex; 2) rules for the interactions among the objects. • Let the rules from the i-th type be marked as i-rules, where i = 1, 2: • When S = {}, we obtain the standard CGL.

  5. Extension of the Crisp GMM • To each object are associated: • its number, • n-tuple of coordinates characterizing its location in the simplex, and • a symbol from S reflecting the peculiarity of the object (e.g. in physical applications: mass, charge, concentration, etc.).  (5, (1,3), )   (3, (1,2), ) (4, (3,2), )   (1, (2,1), ) (2, (3,1), )

  6. Extension of the Crisp GMM • Initial configuration is every ordered set of (n+2)-tuples with an initial component being the number of the object; the second, third, etc. until the (n+1)-st components being its coordinates; and the (n+2)-nd is its symbol from S. • Final configuration the ordered set of (n+2)-tuples having the above form and being a result of a (fixed) initial configuration, modified during a given number of times when the rules from A have been applied.

  7. Extension of the Crisp GMM • The single application of a rule from A over a given configuration K will be called an elementary step in the transformation of the model and will be denoted by A1(K). • In this sense, if K is an initial configuration, and L is a final configuration derived from K through multiple applications of the rules from A, then configurations K0,K1,…,Km will exist, for which K0 = K, Ki+1 = A1(Ki) for 0 ≤ i ≤ m and this will be denoted by L = A(K)  A1(A1(…A1(K)…))

  8. Extension of the Crisp GMM • For example, if k-th element of M (1 ≤ k ≤ s, where s is the number of elements of M) is a rectangular with p×q squares and if the square staying on (i,j)-th place(1 ≤ i ≤ p; 1 ≤ j ≤ q) contains number dki,j {0,1,…,9}, then on the (i,j)-th place of P(M) stays: • minimal number • maximal number • average number

  9. Extension of the Crisp GMM • Let B be a criterion derived from physical or mathematical considerations. For two given configurations K1 and K2, it answers the question whether they are close to each other or not. For example, for two configurations K1and K2 having the form from the above example, or where C1 and C2 are given constants.

  10. Extension of the Crisp GMM • For the set of configurations M and the set of rules A we shall define the set of configurations • The rules A will be called statistically correct, if for a great enough (from a statistical point of view) natural number N: (1)

  11. Extension of the Crisp GMM • The essence of the method is in the following: • The set of rules A, the proximity criterion B and the concentrate rule P are fixed preliminarily. • A set of initial configurations M is chosen and the set of the corresponding final configurations is constructed. • If the equation (1) is valid we may assume that the rules from the set A are correct in the frames of the model, i.e. they are logically consistent. Otherwise, we replace a part (or all) of them with others.

  12. Extension of the Crisp GMM • For example, if we would like to model (in a plane) the Solar System, we can mark the Sun by 9, Jupiter and Saturn by 8, the Earth and Venus by 7, the rest of the planets by 6, the Moon and the rest bigger satellites by 5, the smaller satellites by 4, the bigger asteroids by 3, the smaller asteroids by 2 and the cosmic dust by 1. • If we would like to model the forest dynamics, then the digits will correspond to the number of the territory trees in a square unit of the forest territory. If a river flows through the forest, then we can mark its cells, e.g., by letter R; if there are stones without trees, we can mark them by cells with letter S.

  13. GMM with IF estimations • The intuitionistic fuzzy (IF) propositional calculus has been introduced more than 20 years ago. In it, if x is a variable, then its truth-value is represented by the ordered couple V(x) = <a, b> so that a, b; a + b [0; 1] where a and b are the degrees of validity (existence, membership, etc., and of non-validity, non-existence, etc.) of x. • Below, we shall assume that for the two variables x and y the equalities hold: V(x) = <a, b> , V(y) = <c, d> where a, b, c, d, a + b; c + d [0; 1].

  14. GMM with IF estimations • Let us have a fixed universe E and its subset A. The set A* = {<x, μA(x), νA(x)> | x E} where 0≤ μA(x) + νA(x) ≤ 1 is called intuitionistic fuzzy set (IFS) and functionsμA : E → [0, 1] and νA: E → [0, 1] represent thedegree of membership (validity, etc.) and the degree of non-membership (non-validity, etc.), respectively. • For brevity, we shall write below A instead of A*.

  15. Distances between IFSs • In the literature, different IF parametric distances between two IFSs have been discussed. In the present research, we use 10 of them. • Let everywhere below 0 ≤  ≤  ≤ 1. • Example: d',;str_opt(A,B)(x) = < min(μA(x), νB(x) + ) + min(μB(x), νA(x) + ) – min(μA(x), νB(x) + ).min(μB(x), νA(x) + ), max(νA(x), μB(x) – ).max(νB(x), μA(x) – ) >

  16. Distances between IFSs • As a first step of the research in the present direction,it is convenient to assume that the objects do not change their IF-parameters as a result of movement from one cell to adjacent one. • In a future research, we will discuss the more complex case, when in the result of the movement the IF-parameters changes (e.g., decreases). • Let us assume that the square <i, j> is assigned a pair of real numbers <μi,j, νi,j>, so that μi,j + νi,j ≤ 1. We can call the numbers μi,jandνi,j - a degree of existence and a degree of non-existence of an object, or (in CGL and its IF-extension), of a symbol  in square <i, j>.

  17. Distances between IFSs • Therefore, π(i, j) = 1 – μi,j – νi,j≤ 1 will correspond to the degree of uncertainty, e.g., lack of information about existence of an asterisk in the respective cell. • In a previous research, we formulated seven criteria for existence of an object in a cell, that will include as a particular case the standard CGL. From these criteria it follows that if one is valid – let it be the s-th criterion (1 ≤ s ≤ 7), then we can assert that the object exists with respect to the s-th criterion and, therefore, it will exist with respect to all other criteria the validity of which follows from the validity of the s-th criterion.

  18. Distances between IFSs • If s-th criterion is not valid, then we say that the object does not exist with respect to s-th criterion. But in this case the square may not be totally empty. We may tell that the square is “s-full” if it contains an object with respect to s-th criterion, or that it is “s-empty” if it is empty or contains an object, that does not satisfy the s-th criterion. • For the aims of the GMM, it will be suitable to use (depending on the concrete model) one of the first four criteria for existence of an object. • Let us say for each fixed square <i, j> that there is an object by s-th criterion (1 ≤ s ≤ 4), if this criterion confirms the existence of the object.

  19. Distances between IFSs • For the aims of the present paper, we put  =  = 0in the formulas of distances between IFSs and these distances will obtain the form dstr_opt(A,B)(x) = d'0,0;str_opt(A,B)(x) = d"0,0;str_opt(A,B)(x) dopt(A,B)(x) = d'0,0;opt(A,B)(x) = d"0,0;opt(A,B)(x) daver(A,B)(x) = d'0,0;aver(A,B)(x) = d"0,0;aver(A,B)(x) dpess(A,B)(x) = d'0,0;pess(A,B)(x) = d"0,0;pess(A,B)(x) dstr_pess(A,B)(x) = d'0,0;str_pess(A,B)(x) = d"0,0;str_pess(A,B)(x) • Example: dstr_opt(A,B)(x) = < min(μA(x), νB(x)) + min(μB(x), νA(x)) – min(μA(x), νB(x)).min(μB(x), νA(x)),max(νA(x), μB(x)).max(νB(x), μA(x)) >

  20. Distances between IFSs • Now we see that, each GMM-configuration K can be interpreted as an IFS K = {<<i,j>, μi,j , νi,j >|1 ≤ i ≤ p & 1 ≤ j ≤ q} • Each of the above distances can be used as a criterion B for proximity between two configurations K1 and K2, so that symbols K1 and K2 will stay on places of A and B in the formulas of distances. • Therefore, we obtain intuitionistic fuzzy interpretation of criterion for distances for GMM.

  21. Distances between IFSs • As of rule P, if we use interpretations that are analogous to “strongly optimistic”, “optimistic”, “pessimistic”, or “strongly pessimistic” distances, we will change the sense of the model in some (either μ- or ν-) direction. • Only the analogue of the “average” distance is suitable for our aims. In this case, we must use the following operation for the n IFSs A1,…,An: • Therefore, for a set of (initial or final) configurations M, we construct the average configuration

  22. Thank yu! Lilija Atanassova Institute of Information and Communication Technologies Krassimir Atanassov Institute of Biophysics and Biomedical Engineering krat@bas.bg Krassimir Atanassov is grateful for the support provided by the projectsDID-02-29 “Modelling processes with fixed development rules” andBIn-2-09 “Design and development of intuitionistic fuzzy logic toolsin information technologies” funded by the National Science Fund,Bulgarian Ministry of Education, Youth and Science.

More Related