Adaptive critic designs (ACDs) are designs that approximate dynamic programming in the general case. A typical ACD consists of three modules that can be implemented using neural networks -- a Values module, a Prediction module , and a Decision module. This project aims to collaborate with industry in developing and implementing learning traffic control schemes for broadband communication networks using ACD's. An open and challenging problem facing the designer of the next generation of communication networks is to design schemes that integrate multimedia traffic efficiently and that guarantee the quality of service (QoS) for each traffic source. The challenge here is to maximize the network bandwidth utilization and at the same time to guarantee QoS. For this purpose, traffic control schemes including call admission control and traffic enforcement have been studied extensively in the past. However, most of the existing schemes sacrifice bandwidth utilization (i.e., waste network resources/lose revenue) in order to guarantee QoS. The preliminary study of this project has shown that, with the same QoS goals, higher bandwidth utilization than existing schemes can be achieved using a self-learning approach based on ACDs. Integrated self-learning traffic control at different levels (packet/call/network levels) will be implemented in this project. These include token bucket with optimized parameters, call admission control, and routing and congestion control. Two fundamental issues in the field of neural network-based ACDs will be investigated: (1) structural robustness analysis of neural network-based ACDs and (2) problems relating to convergence in neural network training. Education activities include: (1) Course, laboratory and curriculum development: To develop a graduate course on ACDs, to develop the Intelligent Systems Laboratory at Stevens, and the interdisciplinary effort which will emphasize ACDs/brain-like intelligent systems; (2) Research experience for undergraduate students: To expose undergraduate students to the numerous Opportunities available in a variety of ACDs research areas; and (3) Pedagogical initiatives: To develop a web-based interactive computer tool for automated homework submission and grading and to implement a white board system over the computer network to aid student learning; the evaluation of the pedagogical initiatives will use a web-based, on-line education assessment system in which the self-learning feature of ACDs is applied to optimal decision making. ***
