A key challenge in multiagent environments is the construction of agents that are able to learn while acting in the presence of other agents that are simultaneously learning and adapting. These domains require on-line learning methods without the benefit of repeated training examples, as well as the ability to adapt to the evolving behavior of other agents in the environment. The difficulty is ...

Social/cultural learning is an effective way to reduce uncertainty about the environment, helping individuals adopt an adaptive behavior cheaply. Although this is evident for learning about temporally stable targets, such as acquisition of an avoidance of toxic foods, the utility of social/cultural learning in a temporally unstable environment is less clear, since knowledge acquired by social l...

Most real-world applications operate in dynamic environments. In such environments often it is necessary to modify the current solution due to various changes in the environment (e.g., machine breakdowns, sickness of employees , etc). Thus it is important to investigate properties of adaptive algorithms which do not require restart every time a change is recorded. In this paper non-stationary p...

One of the main mechanisms to generate non-stationary data is that the system's environment is always changing with time. It is appropriate to approximate non-stationary time series using the model: X n+1 = F (X n ; U n); where U n is the system's environment at the time n. If the U n is not observable, we may consider to use the model: X n+1 = F (X n ; ^ U n); by somehow learning the function ...

In this paper we propose a neural architecture for solving continuous time and space reinforcement learning problems in non-stationary environments. The method is based on a mechanism for creating, updating and selecting partial models of the environment. The partial models are incrementally estimated using linear approximation functions and are built according to the system’s capability of mak...

Detection of anomalous changes in social networks has been studied in various applications such as change detection of social interests and virus infections. Among several kinds of network changes, we concentrate on the structural changes of relatively small stationary communities. Such a change is important because it implies that some crucial changes have happened in a special group, such as ...

Abstract. Evolutionary algorithms (EA) are commonly used for solving optimization problems that can be stationary or dynamic. From an EA viewpoint, dynamic problems differ from stationary problems, mainly because in a changing environment, the EA needs to be able to track the optima through generations. Performance of an EA in dynamic environments is improved if the EA is modified to address th...

in recent years there has been considerable interest in the synthesis and separation of enantiomers of organic compounds especially because of their importance in the biochemistry and pharmaceutical industry. high-performance liquid chromatography is a very useful method for the direct separation of enantiomers. however, about 30−40 years ago, commercially available chiral stationary phases wer...

in analyzing a signal, especially a non-stationary signal, it is often necessarythe desired signal to be segmented into small epochs. segmentation can beperformed by splitting the signal at time instances where signal amplitude orfrequency change. in this paper, the signal is initially decomposed into signals withdifferent frequency bands using wavelet transform. then, fractal dimension of thed...