INTELLIGENT AND COOPERATIVE INFORMATION SYSTEMS MEET MACHINE LEARNING

Abstract

Intelligent and Cooperative Information Systems (ICIS) will have large numbers of distributed, heterogeneous agents interacting and cooperating to solve problems regardless of location, original mission, or platform. The agents in an ICIS will adapt to new and possibly surprising situations, preferably without human intervention. These systems will not only control a domain, but also will improve their own performance over time, that is, they will learn. This paper describes five heterogeneous learning agents and how they are integrated into an Integrated Learning System (ILS) where some of the agents cooperate to improve performance. The issues involve coordinating distributed, cooperating, heterogeneous problem-solvers, combining various learning paradigms, and integrating different reasoning techniques. ILS also includes a central controller, called The Learning Coordinator (TLC), that manages the control of flow and communication among the agents, using a high-level communication protocol. In order to demonstrate the generality of the ILS architecture, we implemented an application which, through its own experience, learns how to control the traffic in a telephone network, and show the results for one set of experiments. Options for enhancements of the ILS architecture are also discussed.