Submitted in partial fulfilment of the requirements

detection of change is important. We consider process mining in such non-stationary environments and show that using a probabilistic view of processes, timely and confident detection of change is possible. 1

Environments that vary over time present a fundamental problem to adaptive systems. Although in the worst case there is no hope of effective adaptation, some forms environmental variability do provide adaptive opportunities. We consider a broad class of non-stationary environments, those which

Learning in non-stationary environments is an increasingly important problem in a wide variety of real-world applications. In non-stationary environments data arrives incrementally, however the underlying generating function may change over time. In addition to the environments being non-stationary

algorithms which do not require re-start every time a change is recorded. In this paper non-stationary problems (i.e., problems, which change in time) are discussed. We describe different types of changes in the environment. A new model for non-stationary problems and a classification of these problems

In this paper we introduce RL-CD, a method for solving reinforcement learning problems in non-stationary environments. The method is based on a mechanism for creating, updating and selecting one among several partial models of the environment. The partial models are incrementally built according

Abstract. In this paper, we study the use of particle swarm optimization (PSO) for a class of non-stationary environments. The dynamic problems studied in this work are restricted to one of the possible types of changes that can be pro-duced over the fitness landscape. We propose a hybrid PSO

whose strategy varies with time, assuming no prior information about its negotiation parameters. In so doing, we present a new framework for adaptive negotiation in such non-stationary environments and develop a novel learning algorithm, which is guaranteed to converge, that an agent can use

in the sense of finding a best-response policy, rather than in reaching an equilibrium. We present the first learning al-gorithm that is provably optimal against restricted classes of non-stationary opponents. The algorithm infers an accu-rate model of the opponent’s non-stationary strategy, and simultaneously

This paper introduces a neural network capable of dynamically adapting its architecture to realize time variant non-linear input-output maps. This network has its roots in the mixture of experts framework but uses a localized model for the gating network. Modules or experts are grown or pruned