Automated rule Generation Maryam Mustafa Sarah Karim

Progress Read and understood the following papers –Harmtmut Surmann “Learning a fuzzy rule based knowledge representation” –L.X.Wang “Generating Fuzzy rules by learning from example”

Progress cont. After reading some of the research papers we have finally managed to define the scope of our problems We will not be dealing with membership functions in fuzzy logic, their determination or evaluation. The linguistic variables will also be be defined by the system.

Progress Cont. The system will take as input the linguistic variables that it needs to work with. The system will also take the rules as input. So what the system will expect is A -> B B -> C Once these rules have been given to the system, a start and end node is specified from among the given linguistic variables.

Progress cont. Based on the start and goal node the system will define its fitness function. For this system TWO fitness functions need to be defined. First the algo will convert the rules to chromosomes. i.e. A->B could be encoded as 0000(A)1010(B) Based on these chromosomes the algorithms will perform cross overs.

Progress Cont. Once all possible combinations have been generated by crossing over chromosomes, each one of the resulting chromosomes will be tested by the first fitness function. The first fitness function will check to see if the given chromosome allows the goal node to be reached.

Progress cont. The selected chromosomes from this function will then be put through a second fitness function. This second fitness function will be based on the total distance each solution takes to get to the goal node. This fitness function will select the final solution.

Extensions to the scope Once the above described basic algorithm has been implemented we may add modifications to it. We could introduce the concept of mutations associated with each linguistic variable. The determination of the membership function of mutated variable would allow it to be incorporated into the rule set.