An approach to the development of commonsense knowledge modeling systems for land selection

The land use methods which are ergonomically and environmentally appropriate are determined first and foremost by characteristics and location. For instance, land selection in architectural construction domain is considered as an area in land use methods, which involves commonsense knowledge of architects. This is because land selection criteria are very personal and there is no theory behind how it should be done. Sometime, there are too many redundancies in the process selection of lands. In this paper we present an approach to modeling commonsense knowledge in a sub field of architecture domain of land selection to come up with land classifications as psychological, physical and social events. This gives three-phase knowledge modeling approach for modeling commonsense knowledge in, which enables holistic approach for land selection. At the initial stage commonsense knowledge is converted into a questionnaire. Removing dependencies among the questions are modeled using principal component analysis. Classification of the knowledge is processed through fuzzy logic module, which is constructed on the basis of principal components. Further explanations for classified knowledge are derived by expert system technology. This paper describes one such approach using classification of human constituents in Ayurvedic medicine. Evaluation of the system has shown 77% accuracy

Exploiting bulk agent approach for conflict resolution in multi agent systems

Conflicts are inevitable when autonomous agents operating in Single Multi Agent System to achieve their own goals. Therefore various conflict resolution techniques were presented in the literature. Argumentation Based Negotiation (ABN) has been considered as one of the best approach so far. Evading and re- planning are also two different cost effective options which should be considered as the first option in resolving conflicts. On the other hand, nature can be considered as a one big natural multi agent environment, where all elementary agents interact with no visible conflicts. Cosmological studies and theories have been used to explain most of the natural phenomena that we scientifically experienced. How brane particles interacts each other in a universal extra dimension (bulk) and share the same governing rules such as gravity is the main inspiration for our research. We postulate that the conflicts can be avoided or resolved with minimal computational time and resources by introducing bulk agents which represent extra dimensions of a multi agent system

Dynamic partitional clustering using multi-agent technology

Most of the well established clustering algorithms assume that the underlying clustering structure of dataset does not change over the time. Hence, those algorithms fail to identify underlying cluster structures in currently available large scale dynamic data sources in an efficient manner. This paper presents a Multi Agent based approach to identify partitional clusters in a dynamic data source. Set of partitional clusters in a dynamic data source is identified by interactions and negotiations among the agents who represent data records in the data source. After identification of potential clusters for data records that are assigned to what are called cluster agents. By interactions and negotiations between cluster agents and data record agents, the identified cluster configuration is continuously improved according to the internal cluster evaluation measures. The proposed method is evaluated by synthetic data sets with different number of clusters in 2D and 3D spaces. Results indicate that the proposed method successfully identifies the clusters in those datasets with minimal human intervention

Novel Technique for Optimizing the hidden layer architecture in Artificial Neural Networks

The architecture of an artificial neural network has a great impact on the generalization power. More precisely, by changing the number of layers and neurons in each hidden layer generalization ability can be significantly changed. Therefore, the architecture is crucial in artificial neural network and hence, determining the hidden layer architecture has become a research challenge. In this paper a pruning technique has been presented to obtain an appropriate architecture based on the backpropagation training algorithm. Pruning is done by using the delta values of hidden layers. The proposed method has been tested with several benchmark problems in artificial neural networks and machine learning. The experimental results have been shown that the modified algorithm reduces the size of the network without degrading the performance. Also it tends to the desired error faster than the backpropagation algorithm.Keywords: , , , , hidde

DEVELOPMENT OF FUZZY EXPERT SYSTEMS FOR TACIT KNOWLEDGE MODELING IN STRATEGIC DECISION-MAKING

Knowledge modelling gives the intention of knowledge engineering which is applicable for managing information systems. Tacit knowledge is the key issue of knowledge modelling aspect because all knowledge is rooted in tacit knowledge. In recognizing knowledge as a new resource in gaining organizational competitiveness, knowledge management suggests a method in managing and applying knowledge for improving organizational performance. Much knowledge management research has focused on identifying, storing, and disseminating process related knowledge in an organized manner. Applying knowledge to decision making has a significant impact on organizational performance than solely processing transactions for knowledge management. This paper presents a research that incorporates modelling of tacit knowledge for strategic decision-making. Here we have used fuzzy expert system for developing an approach for modelling tacit knowledge. We primarily used fuzzy logic together with statistical technique of principal component analysis as techniques for modelling tacit domains. Tacit knowledge in Ayurvedic sub-domain of individual classification has been acquired through a questionnaire and analysed to identify the dependencies, which lead to make tacit knowledge in the particular domain. It has shown 77% accuracy in using the tacit knowledge for reasoning in the relevant domain. Keywords: Fuzzy Expert System, Tacit Knowledge, Principal Component Analysis, and Strategic Decision-making, Ayurvedic MedicineFor full Paper: [email protected]

A FUZZY EXPERT SYSTEM FOR BUSINESS INTELLIGENCE

Business Intelligence (BI) is recognized as an increasingly important support for business decision making in emerging business environment, where a huge amount of data is growing fast and scattered around. Explicit knowledge can be presented formally and capable of effective (fast and good quality) communication of data to the user where as commonsense knowledge can be represented in informal way and further modeling needed for BI. Acquiring useful Business Intelligence (BI) for decision-making is a challenging task in dynamic business environment. In this paper we present an approach for modeling commonsense knowledge in Business Intelligence. A fuzzy expert system based on principal component analysis (PCA) and statistical fuzzy inference system for modeling Business Intelligence in commonsense knowledge is introduced in, which enables holistic approach for disaster management. This paper describes one such approach using classification of human constituents in Ayurvedic medicine. Evaluation of the system has shown 77% accuracy. Key words: Business Intelligence, Statistical inference system, Common sense knowledge, Principal component analysis and Ayurvedic medicineFor full Paper: [email protected]