Empirically Evaluating Decision-Analytic Models

Empirically Evaluating Multiagent Learning Algorithms

There exist many algorithms for learning how to play repeated bimatrix games. Most of these algorithms are justified in terms of some sort of theoretical guarantee. On the other hand, little is known about the empirical performance of these algorithms. Most such claims in the literature are been based on small experiments, which has hampered understanding as well as the development of new multi...

Empirically Evaluating the Electoral College

Empirically Evaluating Multiagent Reinforcement Learning Algorithms

This article makes two contributions. First, we present a platform for running and analyzing multiagent reinforcement learning experiments. Second, to demonstrate this platform we undertook and evaluated an empirical test of multiagent reinforcement learning algorithms from the literature, which to our knowledge is the largest such test ever conducted. We summarize some conclusions from our exp...

Empirically Evaluating CORBA Component Model Implementations

Commercial off-the-shelf (COTS) middleware is increasingly used to develop distributed real-time and embedded (DRE) systems. DRE systems are increasingly combined using wireless and wireline networks to form “systems of systems” having multiple quality of service (QoS) requirements. Conventional COTS middleware does not facilitate the separation of QoS policies from application functionality, m...

Empirically Evaluating an Adaptable Spoken Dialogue System

Recent technological advances have made it possible to build real-time, interactive spoken dialogue systems for a wide variety of applications. However, when users do not respect the limitations of such systems, performance typically degrades. Although users differ with respect to their knowledge of system limitations, and although different dialogue strategies make system limitations more appa...