Fuzzy logic programs are a useful framework for handling uncertainty in logic programming; nevertheless, there is the need for modelling adaptation of fuzzy logic programs. In this paper, we first overview weighted fuzzy programs, which bring fuzzy logic programs and connectionist models closer together by associating significance weights with the atoms of a logic rule: by exploiting the existe...

In a graph G, a vertex dominates itself and its neighbors. A subset S of V is called a dominating set in G if every vertex in V-S is adjacent to at least one vertex in S. The minimum cardinality taken over all, the minimal double dominating set which is called Fuzzy Double Domination Number and which is denoted as ) (G fdd  . A set V S  is called a Triple dominating set of a graph G if every ...

The chromatic sum of a graph G, ∑ (G), is introduced in the dissertation of Kubicka [3]. It is defined as the smallest possible total over all vertices that can occur among all colorings of G using natural numbers for the colors. It is known that computing the chromatic sum of an arbitrary graph is an NP-complete problem. The vertex-strength of the graph G, denoted by s(G), is the smallest inte...

A generic de.nition of fuzzy morphism between graphs (GFM) is introduced that includes classical graph related problem de.nitions as sub-cases (such as graph and subgraph isomorphism). The GFM uses a pair of fuzzy relations, one on the vertices and one on the edges. Each relation is a mapping between the elements of two graphs. These two fuzzy relations are linked with constraints derived from ...

Let G= (V,E,σ,μ) be a simple connected undirected fuzzy graph. In this paper, we use a fuzzy graph model to represent a traffic network of a city and discuss a method to find the different type of accidental zones in a traffic flows. This paper is based on fuzzy coloring of fuzzy graphs and fuzziness of vertices. Using this approach, we can minimize the total waiting time of a traffic flow. It ...

In this paper, a new global path planning and obstacle avoidance scheme for mobile robot is proposed. The key to avoiding the obstacle is to fuzzify them and construct a fuzzy graph from the environment. Then, based on human performance a set of fuzzy rules is defined for robot navigation using these fuzzy sets and sub-optimal paths to the goal are generated. A fuzzy rule is defined for avoidin...