Discrete and fuzzy dynamical genetic programming in the XCSF learning classifier system

A number of representation schemes have been presented for use within learning classifier systems, ranging from binary encodings to neural networks. This paper presents results from an investigation into using discrete and fuzzy dynamical system representations within the XCSF learning classifier system. In particular, asynchronous random Boolean networks are used to represent the traditional condition-action production system rules in the discrete case and asynchronous fuzzy logic networks in the continuous-valued case. It is shown possible to use self-adaptive, open-ended evolution to design an ensemble of such dynamical systems within XCSF to solve a number of well-known test problems

Asynchronous Gossip for Averaging and Spectral Ranking

We consider two variants of the classical gossip algorithm. The first variant is a version of asynchronous stochastic approximation. We highlight a fundamental difficulty associated with the classical asynchronous gossip scheme, viz., that it may not converge to a desired average, and suggest an alternative scheme based on reinforcement learning that has guaranteed convergence to the desired average. We then discuss a potential application to a wireless network setting with simultaneous link activation constraints. The second variant is a gossip algorithm for distributed computation of the Perron-Frobenius eigenvector of a nonnegative matrix. While the first variant draws upon a reinforcement learning algorithm for an average cost controlled Markov decision problem, the second variant draws upon a reinforcement learning algorithm for risk-sensitive control. We then discuss potential applications of the second variant to ranking schemes, reputation networks, and principal component analysis.Comment: 14 pages, 7 figures. Minor revisio

Sharing learning experiences through correspondence on the WWW

Asynchronous learning networks are facilities and procedures to allow members of learning communities to be more effective and efficient in their learning. One approach is to see how the `sharing' of knowledge can be augmented through meta-data descriptions attached to portfolios and project work. Another approach is to facilitate the reflection upon individual or collaborative learning experiences (Okamoto, Cristea, Matsui, & Miwata, 2000). The position that I defend in this paper is that both the meta-data approach and the attempts to capture the students' meta-knowledge are rather complicated because of social and emotional reason

Scaffolding Discourse in Asynchronous Learning Networks

Discourse, a form of collaborative learning, is fundamentally a communications process. This in-progress study adapts Clark and Brennan’s grounding in communications principles to investigate how to “scaffold” asynchronous discourse. Scaffolding is defined as providing support for the learner at his or her level until the support is no longer needed. This paper presents early results from an experimental study measuring learning effectiveness. In the experiment, content and process scaffolding are manipulated based on pedagogic principles. A major contribution of the study is building and testing a technologymediated, discourse-centered, teaching and learning model called the Asynchronous Learning Networks Cognitive Discourse Model (ALNCDM). As discourse is one of the most widely used online methods of teaching and learning, the results of the study are expected to add to the body of knowledge on how to structure asynchronous online discourse assignments for more effective student learning

Structuring Courses for Asynchronous Learning Networks

Drexel University is delivering a graduate degree in information systems by asynchronous learning network (ALN). Students in this program never attend a face-to-face class. This paper discusses methods of structuring course material for delivery in this environmen

Practical Block-wise Neural Network Architecture Generation

Convolutional neural networks have gained a remarkable success in computer vision. However, most usable network architectures are hand-crafted and usually require expertise and elaborate design. In this paper, we provide a block-wise network generation pipeline called BlockQNN which automatically builds high-performance networks using the Q-Learning paradigm with epsilon-greedy exploration strategy. The optimal network block is constructed by the learning agent which is trained sequentially to choose component layers. We stack the block to construct the whole auto-generated network. To accelerate the generation process, we also propose a distributed asynchronous framework and an early stop strategy. The block-wise generation brings unique advantages: (1) it performs competitive results in comparison to the hand-crafted state-of-the-art networks on image classification, additionally, the best network generated by BlockQNN achieves 3.54% top-1 error rate on CIFAR-10 which beats all existing auto-generate networks. (2) in the meanwhile, it offers tremendous reduction of the search space in designing networks which only spends 3 days with 32 GPUs, and (3) moreover, it has strong generalizability that the network built on CIFAR also performs well on a larger-scale ImageNet dataset.Comment: Accepted to CVPR 201