Intelligent Multi-Agent Fuzzy Control System Under Uncertainty

PeachFuzz 2000. 19th International Conference of the North American Fuzzy Information Processing Society - NAFIPS (Cat. No.00TH8500), 2000

Lately, rapid advances in intelligent agents and agent-based systems have been attracting the attention of academicians and practitioners. Intelligent agents have attractive properties including: autonomy; proactiveness; intelligence; social ability and other useful features. The field of DSS can benefit significantly by focusing on the above concerns through employment of agent-based technologies. This work proposes a framework for a single decision task DSS based on multi-agent architecture. The agents in the DSS employ fuzzy rules and fuzzy objectives when generating alternative decisions. The approach is demonstrated using the investment Multi-Agent DSS (MADSS) prototype. The experiments show that the fuzzy MADSS outperforms the traditional model plus data based DSS.

2004 IEEE International Conference on Fuzzy Systems (IEEE Cat. No.04CH37542), 2004

Programs for contemporary industrial control systems are designed using Object Oriented methods and software agents. It is required that the system should reach its objectives even when unexpected events occur in an uncertain environment. A trend is that agents are becoming more autonomous, more complex and are having more responsibilities. There is a need for a high-level information representation such that a predictable behavior can be achieved in a uniform way for all agents. A fuzzy automaton-based approach offers clear benefits for developing agents of reconfigurable architecture. This paper reports on a research project that is currently underway to develop a Generic Encapsulated Fuzzy Automaton Software Agent for Object Oriented Control Systems. A laboratory has been set up to develop and evaluate the performance of new methods and architectures.

Lecture Notes in Computer Science, 2007

A method for Information Extraction (IE) in a set of knowledge is proposed in this paper in order to answer to user consultations using natural language. The system is based on a fuzzy logic engine, which takes advantage of its flexibility for managing sets of accumulated knowledge. These sets can be built in hierarchic levels by a tree structure. A method of consultation based on a fuzzy logic application provided with an interface that one may interact with in natural language is also proposed. The eventual aim of this system is the implementation of an intelligent agent to manage the information contained in an internet portal.

2013

Abstract: The most rational mode of communication between intelligent robot and human-operator is bilateral speech dialogue using a problem-oriented language. The dialogue mode of control raises the problem of compliance between the human and robot perception of external world, logic, behavior planning and decision making. We suppose that linguistic variables and fuzzy logic is the most suitable approach for the mentioned problems. The model of the world formed on the base of natural space-time relations allows formulating the basic robot operations in terms of linguistic variables and to solve the problem of planning of robot behavior in previously undetermined environment. Using the fuzzy neural networks it is possible to train robot to fulfill complicated operations by human-operator. Relationship between fuzzy logic approach and the procedures of speech recognition are also under consideration

Expert Systems with …, 2010

Expert system Fuzzy rule based systems Business management Knowledge base minimal structure Operations management a b s t r a c t Nowadays firms are required to reach high levels of specialisation in order to increase their competitiveness in complex markets. Knowledge management plays a fundamental role in this process as the correct implementation of strategies is determined by the information transfer and dissemination within the organisation. In this paper, a fuzzy expert system focused on increasing accuracy and quality of the knowledge for decision making is designed. A model based on fuzzy rules to simulate the behavior of the firms, is presented under the assumption of determined input parameters previously detected and an algorithm is developed to achieve the minimal structure of the model. The result is a fuzzy expert system tool, called ESROM, which gives valuable information to help managers to improve the achievement of the strategic objectives of the company. A main contribution of this work it that the system is general and can be adapted to different scenarios.

This report contains а general description of the ROBCON knowledgebased program system. ТЬе system "\ а) understands fuzzy natural language tasks for two-dimentiona-l motion of а robot; Ь) plans the robot actions working off the fuzziness; с) simulates the robot movements; d) performs а number of service functions.

benthamscience.com

2006

This study deals with the interface environment of an intelligent fuzzy agent model, including the evaluation of the fuzzy approach in decision making mechanism in the flexible manufacturing environment. The behavior style of this decision making mechanism is expressed in multi agent system. The fuzzy logic approach is preferred to expedite the process of decision making in the system. This will also reduce uncertainty in the process in the interface environment. The suggested system provides decision-making and evaluation of results for manufacturing activities in the fuzzy situation. The evaluation of the decisionmaking process includes the agent list, task list, rule list and knowledge list structures that are combined in the fuzzy inference mechanism.