We present a class of Bayesian copula models whose major components are the marginal (limiting) distribution of a stationary time series and the internal dynamics of the series. We argue that these are the two features with which an analyst is typically most familiar, and hence that these are natural components with which to work. For the marginal distribution, we use a nonparametric Bayesian prior distribution along with a cdf-inverse cdf transformation to obtain large support. For the internal dynamics, we rely on the traditionally successful techniques of normal-theory time series. Coupling the two components gives us a family of (Gaussian) copula transformed autoregressive models. The models provide coherent adjustments of time scales and are compatible with many extensions, including changes in volatility of the series. We describe basic properties of the models, show their ability to recover non-Gaussian marginal distributions, and use a GARCH modification of the basic model to analyze stock index return series. The models are found to provide better fit and improved short-range and long-range predictions than Gaussian competitors. The models are extensible to a large variety of fields, including continuous time models, spatial models, models for multiple series, models driven by external covariate streams, and non-stationary models.

We propose that learning agents (LAs) be incorporated into simulation environments in order to model the adaptive behavior of humans. These LAs adapt to specific circumstances and events during the simulation run. They would select tasks to be accomplished among a given set of tasks as the simulation progresses, or synthesize tasks for themselves based on their observations of the environment and on information they may receive from other agents. We investigate an approach in which agents are assigned goals when the simulation starts and then pursue these goals autonomously and adaptively. During the simulation, agents progressively improve their ability to accomplish their goals effectively and safely. Agents learn from their own observations and from the experience of other agents with whom they exchange information. Each LA starts with a given representation of the simulation environment from which it progressively constructs its own internal representation and uses it to make decisions. The paper describes how learning neural networks can support this approach and shows that goal based learning may be used effectively used in this context. An example simulation is presented in which agents represent manned vehicles; they are assigned the goal of traversing a dangerous metropolitan grid safely and rapidly using goal based reinforcement learning with neural networks and compared to three other algorithms.

Based on our earlier work ("Towards networks with intelligent packets", Proc. 14th Int. Symp. on Computer and Information Sciences, p. 1-11, Oct. 1999), we discuss packet networks in which intelligent capabilities for routing and flow control are concentrated in the packets, rather than in the nodes and protocols. This paper describes a possible testbed to test and evaluate their capabilities, and presents an analytical model for the worst and best case performance of such systems.

We propose cognitive packet networks (CPN) in which intelligent capabilities for routing and flow control are concentrated in the packets, rather than in the nodes and protocols. Cognitive packets within a CPN route themselves. They are assigned goals before entering the network and pursue these goals adaptively. Cognitive packets learn from their own observations about the network and from the experience of other packets with whom they exchange information via mailboxes. Cognitive packets rely minimally on routers. This paper describes CPN and shows how learning can support intelligent behavior of cognitive packets.