Fuzzy linear assignment problem: an approach to vehicle fleet deployment

This paper proposes and examines a new approach using fuzzy logic to vehicle fleet deployment. Fleet deployment is viewed as a fuzzy linear assignment problem. It assigns each travel request to an available service vehicle through solving a linear assignment matrix of defuzzied cost entries. Each cost entry indicates the cost value of a travel request that "fuzzily aggregates" multiple criteria in simple rules incorporating human dispatching expertise. The approach is examined via extensive simulations anchored in a representative scenario of taxi deployment, and compared to the conventional case of using only distances (each from the taxi position to the source point and finally destination point of a travel request) as cost entries. Discussion in the context of related work examines the performance and practicality of the proposed approach

Expert supervision of an anti-skid control system of a commercial aircraft

A rule-based supervising system that incorporates fuzzy logic has been designed to back-up a conventional anti-skid braking system (ABS). Expressing the expert knowledge about the ABS in terms of linguistic rules, the supervising fuzzy system adapts the reference wheel slip of the ABS with respect to the actual runway condition. Two approaches are presented: The first uses a simple rule-based decision logic, which evaluates a new reference slip directly from the measured system variables. The second approach employes an explicit identification of the runway condition, which is used as input information of a fuzzy system to evaluate a new reference slip. This application example demonstrates the capabilities of a parallel use of conventional control techniques and fuzzy logic

Focusing for Pronoun Resolution in English Discourse: An Implementation

Anaphora resolution is one of the most active research areas in natural language processing. This study examines focusing as a tool for the resolution of pronouns which are a kind of anaphora. Focusing is a discourse phenomenon like anaphora. Candy Sidner formalized focusing in her 1979 MIT PhD thesis and devised several algorithms to resolve definite anaphora including pronouns. She presented her theory in a computational framework but did not generally implement the algorithms. Her algorithms related to focusing and pronoun resolution are implemented in this thesis. This implementation provides a better comprehension of the theory both from a conceptual and a computational point of view. The resulting program is tested on different discourse segments, and evaluation and analysis of the experiments are presented together with the statistical results.Comment: iii + 49 pages, compressed, uuencoded Postscript file; revised version of the first author's Bilkent M.S. thesis, written under the supervision of the second author; notify Akman via e-mail ([email protected]) or fax (+90-312-266-4126) if you are unable to obtain hardcopy, he'll work out somethin

Intelligent control based on fuzzy logic and neural net theory

In the conception and design of intelligent systems, one promising direction involves the use of fuzzy logic and neural network theory to enhance such systems' capability to learn from experience and adapt to changes in an environment of uncertainty and imprecision. Here, an intelligent control scheme is explored by integrating these multidisciplinary techniques. A self-learning system is proposed as an intelligent controller for dynamical processes, employing a control policy which evolves and improves automatically. One key component of the intelligent system is a fuzzy logic-based system which emulates human decision making behavior. It is shown that the system can solve a fairly difficult control learning problem. Simulation results demonstrate that improved learning performance can be achieved in relation to previously described systems employing bang-bang control. The proposed system is relatively insensitive to variations in the parameters of the system environment

Intelligent systems in manufacturing: current developments and future prospects

Global competition and rapidly changing customer requirements are demanding increasing changes in manufacturing environments. Enterprises are required to constantly redesign their products and continuously reconfigure their manufacturing systems. Traditional approaches to manufacturing systems do not fully satisfy this new situation. Many authors have proposed that artificial intelligence will bring the flexibility and efficiency needed by manufacturing systems. This paper is a review of artificial intelligence techniques used in manufacturing systems. The paper first defines the components of a simplified intelligent manufacturing systems (IMS), the different Artificial Intelligence (AI) techniques to be considered and then shows how these AI techniques are used for the components of IMS