Yasser Hassan

Emergent rough set data analysis

Many systems in nature produce complicated behaviors, which emerge from the local interactions of relatively simple individual components that live in some spatially extended world. Notably, this type of emergent behavior formation often occurs without the existence of a central control. The rough set concept is a new mathematical approach to imprecision, vagueness and uncertainty. This paper introduces the emergent computational paradigm and discusses its applicability and potential in rough sets theory. In emergence algorithm, the overall system dynamics emerge from the local interactions of independent objects or agents. For accepting a system is displaying an emergent behavior, the system should be constructed by describing local elementary interactions between components in different ways than those used in describing global behavior and properties of the running system over a period of time. The proposals of an emergent computation structure for implementing basic rough sets theory operators are also given in this paper.

Decision making using hybrid rough sets and neural networks.

A methodology for using rough sets theory for preference modeling in decision problem is presented in this paper. We will introduce a new method where neural network systems and rough sets theory are completely integrated into a hybrid system and are used cooperatively for decision and classification support. At the first glance, the two methods we discuss have not much in common. But, in spite of the differences between them, it is interesting to try to incorporate both into one combined system, and apply it in the building of a decision support system.

Rough neural classifier system

The methodology for using rough set theory for preference modeling in a decision problem is presented in which we will introduce a new method where a neural network system and rough set theory are completely integrated into a hybrid system and used cooperatively for decision and classification support. At the first glance, the two methods we talk about have not too much in common. But, in spite of the differences between these two methods, it is interesting to try to incorporate both into one combined system, and apply it in the building of a decision support system.

Adaptive behaviorin cellular automata using rough set theory

The paper uses ideas from machine learning and artificial intelligence to provide the model of cellular automata based on rough set theory and the response to it in simulated cars. Recently, the examination and modeling of vehicular traffic has become an important subject of research. We propose in this paper, a road-traffic system based on two-dimensional cellular automata combined with rough set theory, to model the flow and jamming that is common in an urban environment. The modeled development process in this paper involves simulated processes of evolution, learning, and self-organization. The main value of the model is that it provides an illustration of how simple learning processes may lead to the formation of the state machine behavior, which can give emergence to the model.

Emergence decision using hybrid rough sets/cellular automata

Purpose – The aim is identifying and analyzing some well‐defined types of emergence where the paper uses ideas from machine learning and artificial intelligence to provide the model of cellular automata based on rough set theory and response in simulated cars.Design/methodology/approach – This paper proposes, as practical part, a road traffic system based on two‐dimensional cellular automata combined with rough set theory to model the flow and jamming that is suitable to an urban environment.Findings – The automaton mimics realistic traffic rules that apply in everyday experience.Research limitations/implications – The modeled development process in this paper involves simulated processes of evolution, learning and self‐organization.Practical implications – Recently, the examination and modeling of vehicular traffic has become an important subject of research.Originality/value – The main value of the model is that it provides an illustration of how simple learning processes may lead to the formation of th...

Combination method of rough set and genetic programming

A methodology for using rough set for preference modeling in decision problem is presented in this paper; where we will introduce a new approach for deriving knowledge rules from database based on rough set combined with genetic programming. Genetic programming belongs to the most new techniques in applications of artificial intelligence. Rough set theory, which emerged about 20 years back, is nowadays a rapidly developing branch of artificial intelligence and soft computing. At the first glance, the two methodologies that we discuss are not in common. Rough set construct is the representation of knowledge in terms of attributes, semantic decision rules, etc. On the contrary, genetic programming attempts to automatically create computer programs from a high‐level statement of the problem requirements. But, in spite of these differences, it is interesting to try to incorporate both the approaches into a combined system. The challenge is to obtain as much as possible from this association.

Interpretation of rough neural networks as emergent model

The need for more effective methods to generate and maintain global nonfunctional properties suggests an approach analogous to those of natural processes in generating emergent properties. Emergent model allows the constraints of the task to be represented more naturally and permits only pertinent task specific knowledge to emerge in the course of solving the problem. The paper describes some basics of emergent phenomena and its implementation in the rough hybrid systems.

INDUCTION OF KNOWLEDGE USING EVOLUTIONARY ROUGH SET THEORY

Rough set theory, which emerged about 20 years ago, is nowadays a rapidly developing branch of artificial intelligence and soft computing. We will use rough set theory for modeling a classification system and applying genetic operations to a population of trees, which will be induced randomly or via the C4.5 method from the decision table with different pruning-constant settings. At first glance, the methodologies we discuss, namely, rough set theory and genetic programming, have nothing in common. However, it is interesting to try to incorporate these approaches into the hybrid system. The challenge is to get as much as possible from this association.

Emergent computation using a new model of cellular automata

In recent years, an approach-termed emergence system has gained popularity in a variety of fields, however, emergent behavior in decentralized spatially extended systems, such as in Cellular Automata, is still not well understood. The difficulties we face in adopting a definition of the concept of emergence are reminiscent of the complications faced by early Artificial Intelligence (AI) researchers in defining intelligence. Emergent computation allows the constraints of the task to be represented more naturally and permits only pertinent task-specific knowledge to emerge in the course of solving the problem. For accepting that a system is displaying emergent behavior, the system should be constructed by describing local elementary interactions between components in a different way than describing global behavior and properties of the running system over a period of time. In this paper, we introduce a general model for describing the emergent computational strategies.

Emergent phenomena in cellular automata modeling

It has recently been shown that an approach termed emergence system has gained popularity in a variety of fields, however, emergent computation in decentralized spatially extended systems, such as in cellular automata, is still not well understood. To accept that a system is displaying emergent behavior, the system should be constructed by describing local elementary interactions between components. This is achieved in a different way by describing global behavior and properties of the running system over a period of time. This paper introduces the emergent computational paradigm, and discusses its theoretical formulation using a new general model of cellular automata. We have also developed a technique to study the structure of the state transition of cellular automata in the limit of large system size.