Immediate reward reinforcement learning for clustering and topology preserving mappings

We extend a reinforcement learning algorithm which has previously been shown to cluster data. Our extension involves creating an underlying latent space with some pre-defined structure which enables us to create a topology preserving mapping. We investigate different forms of the reward function, all of which are created with the intent of merging local and global information, thus avoiding one of the major difficulties with e.g. K-means which is its convergence to local optima depending on the initial values of its parameters. We also show that the method is quite general and can be used with the recently developed method of stochastic weight reinforcement learning [14]

Neuroengineering of Clustering Algorithms

Cluster analysis can be broadly divided into multivariate data visualization, clustering algorithms, and cluster validation. This dissertation contributes neural network-based techniques to perform all three unsupervised learning tasks. Particularly, the first paper provides a comprehensive review on adaptive resonance theory (ART) models for engineering applications and provides context for the four subsequent papers. These papers are devoted to enhancements of ART-based clustering algorithms from (a) a practical perspective by exploiting the visual assessment of cluster tendency (VAT) sorting algorithm as a preprocessor for ART offline training, thus mitigating ordering effects; and (b) an engineering perspective by designing a family of multi-criteria ART models: dual vigilance fuzzy ART and distributed dual vigilance fuzzy ART (both of which are capable of detecting complex cluster structures), merge ART (aggregates partitions and lessens ordering effects in online learning), and cluster validity index vigilance in fuzzy ART (features a robust vigilance parameter selection and alleviates ordering effects in offline learning). The sixth paper consists of enhancements to data visualization using self-organizing maps (SOMs) by depicting in the reduced dimension and topology-preserving SOM grid information-theoretic similarity measures between neighboring neurons. This visualization\u27s parameters are estimated using samples selected via a single-linkage procedure, thereby generating heatmaps that portray more homogeneous within-cluster similarities and crisper between-cluster boundaries. The seventh paper presents incremental cluster validity indices (iCVIs) realized by (a) incorporating existing formulations of online computations for clusters\u27 descriptors, or (b) modifying an existing ART-based model and incrementally updating local density counts between prototypes. Moreover, this last paper provides the first comprehensive comparison of iCVIs in the computational intelligence literature --Abstract, page iv

Dynamic Generalisation of Continuous Action Spaces in Reinforcement Learning: A Neurally Inspired Approach

Institute for Adaptive and Neural ComputationAward number: 98318242.This thesis is about the dynamic generalisation of continuous action spaces in reinforcement learning problems. The standard Reinforcement Learning (RL) account provides a principled and comprehensive means of optimising a scalar reward signal in a Markov Decision Process. However, the theory itself does not directly address the imperative issue of generalisation which naturally arises as a consequence of large or continuous state and action spaces. A current thrust of research is aimed at fusing the generalisation capabilities of supervised (and unsupervised) learning techniques with the RL theory. An example par excellence is Tesauro’s TD-Gammon. Although much effort has gone into researching ways to represent and generalise over the input space, much less attention has been paid to the action space. This thesis first considers the motivation for learning real-valued actions, and then proposes a set of key properties desirable in any candidate algorithm addressing generalisation of both input and action spaces. These properties include: Provision of adaptive and online generalisation, adherence to the standard theory with a central focus on estimating expected reward, provision for real-valued states and actions, and full support for a real-valued discounted reward signal. Of particular interest are issues pertaining to robustness in non-stationary environments, scalability, and efficiency for real-time learning in applications such as robotics. Since exploring the action space is discovered to be a potentially costly process, the system should also be flexible enough to enable maximum reuse of learned actions. A new approach is proposed which succeeds for the first time in addressing all of the key issues identified. The algorithm, which is based on the ubiquitous self-organising map, is analysed and compared with other techniques including those based on the backpropagation algorithm. The investigation uncovers some important implications of the differences between these two particular approaches with respect to RL. In particular, the distributed representation of the multi-layer perceptron is judged to be something of a double-edged sword offering more sophisticated and more scalable generalising power, but potentially causing problems in dynamic or non-equiprobable environments, and tasks involving a highly varying input-output mapping. The thesis concludes that the self-organising map can be used in conjunction with current RL theory to provide real-time dynamic representation and generalisation of continuous action spaces. The proposed model is shown to be reliable in non-stationary, unpredictable and noisy environments and judged to be unique in addressing and satisfying a number of desirable properties identified as important to a large class of RL problems

SEARCH, REPLICATION AND GROUPING FOR UNSTRUCTURED P2P NETWORKS

In my dissertation, I present a suite of protocols that assist in efficient content location and distribution in unstructured Peer-to-Peer overlays. The basis of these schemes is their ability to learn from past interactions, increasing their performance with time. Peer-to-Peer (P2P) networks are gaining increasing attention from both the scientific and the large Internet user community. Popular applications utilizing this new technology offer many attractive features to a growing number of users. P2P systems have two basic functions: Content search and dissemination. Search (or lookup) protocols define how participants locate remotely maintained resources. In data dissemination, users transmit or receive content from single or multiple sites in the network. P2P applications traditionally operate under purely decentralized and highly dynamic environments. Unstructured systems represent a particularly interesting class of P2P networks. Peers form an overlay in an ad-hoc manner, without any guarantees relative to lookup performance or content availability. Resources are locally maintained, while participants have limited knowledge, usually confined to their immediate neighborhood in the overlay. My work aims at providing effective and bandwidth-efficient searching and data sharing. A suite of algorithms which provide peers in unstructured P2P overlays with the state necessary in order to efficiently locate, disseminate and replicate objects is presented. The Adaptive Probabilistic Search (APS) scheme utilizes directed walkers to forward queries on a hop-by-hop basis. Peers store success probabilities for each of their neighbors in order to efficiently route towards object holders. AGNO performs implicit grouping of peers according to the demand incentive and utilizes state maintained by APS in order to route messages from content holders towards interested peers, without requiring any subscription process. Finally, the Adaptive Probabilistic REplication (APRE) scheme expands on the state that AGNO builds in order to replicate content inside query intensive areas according to demand

Machine Learning

Machine Learning can be defined in various ways related to a scientific domain concerned with the design and development of theoretical and implementation tools that allow building systems with some Human Like intelligent behavior. Machine learning addresses more specifically the ability to improve automatically through experience

On the structure of learning and transfer in machines

Machine Learning (ML) is often described as a process of learning about patterns and relationships. Structure is an example of the relationship between spaces of things; in this work, we provide a definition of learning in machines written as the process of learning unknown structure. This produces a unified view of ML that affords us concrete notions of spaces of tasks, and how they relate to chosen models. Using such a view, we define what it means to transfer between ML problems, and how to learn to transfer. Our definition embodies the notion that transfer is tightly coupled with biases, in that to transfer is to assume biases. Further, we define transfer in the same language of structure as we did vanilla learning; the key difference manifests as the structure that is learnt. This definition highlights differences between learning to transfer, and learning by transfer. We provide a framework, based on the theory of foliations that expresses our notions of transfer in the context of structure. We express popular methods of transfer in ML using our framework, and discuss how our framework informs us about the benefits of transfer. The primary goal of this thesis is to introduce the mathematical and philosophical frameworks by which learning and transfer in machines can be expressed and interpreted consistently in terms of structure.Open Acces

Improving Pan-African research and education networks through traffic engineering: A LISP/SDN approach

The UbuntuNet Alliance, a consortium of National Research and Education Networks (NRENs) runs an exclusive data network for education and research in east and southern Africa. Despite a high degree of route redundancy in the Alliance's topology, a large portion of Internet traffic between the NRENs is circuitously routed through Europe. This thesis proposes a performance-based strategy for dynamic ranking of inter-NREN paths to reduce latencies. The thesis makes two contributions: firstly, mapping Africa's inter-NREN topology and quantifying the extent and impact of circuitous routing; and, secondly, a dynamic traffic engineering scheme based on Software Defined Networking (SDN), Locator/Identifier Separation Protocol (LISP) and Reinforcement Learning. To quantify the extent and impact of circuitous routing among Africa's NRENs, active topology discovery was conducted. Traceroute results showed that up to 75% of traffic from African sources to African NRENs went through inter-continental routes and experienced much higher latencies than that of traffic routed within Africa. An efficient mechanism for topology discovery was implemented by incorporating prior knowledge of overlapping paths to minimize redundancy during measurements. Evaluation of the network probing mechanism showed a 47% reduction in packets required to complete measurements. An interactive geospatial topology visualization tool was designed to evaluate how NREN stakeholders could identify routes between NRENs. Usability evaluation showed that users were able to identify routes with an accuracy level of 68%. NRENs are faced with at least three problems to optimize traffic engineering, namely: how to discover alternate end-to-end paths; how to measure and monitor performance of different paths; and how to reconfigure alternate end-to-end paths. This work designed and evaluated a traffic engineering mechanism for dynamic discovery and configuration of alternate inter-NREN paths using SDN, LISP and Reinforcement Learning. A LISP/SDN based traffic engineering mechanism was designed to enable NRENs to dynamically rank alternate gateways. Emulation-based evaluation of the mechanism showed that dynamic path ranking was able to achieve 20% lower latencies compared to the default static path selection. SDN and Reinforcement Learning were used to enable dynamic packet forwarding in a multipath environment, through hop-by-hop ranking of alternate links based on latency and available bandwidth. The solution achieved minimum latencies with significant increases in aggregate throughput compared to static single path packet forwarding. Overall, this thesis provides evidence that integration of LISP, SDN and Reinforcement Learning, as well as ranking and dynamic configuration of paths could help Africa's NRENs to minimise latencies and to achieve better throughputs

Gesture-Based Robot Path Shaping

For many individuals, aging is frequently associated with diminished mobility and dexterity. Such decreases may be accompanied by a loss of independence, increased burden to caregivers, or institutionalization. It is foreseen that the ability to retain independence and quality of life as one ages will increasingly depend on environmental sensing and robotics which facilitate aging in place. The development of ubiquitous sensing strategies in the home underpins the promise of adaptive services, assistive robotics, and architectural design which would support a person\u27s ability to live independently as they age. Instrumentation (sensors and processing) which is capable of recognizing the actions and behavioral patterns of an individual is key to the effective component design in these areas. Recognition of user activity and the inference of user intention may be used to inform the action plans of support systems and service robotics within the environment. Automated activity recognition involves detection of events in a sensor data stream, conversion to a compact format, and classification as one of a known set of actions. Once classified, an action may be used to elicit a specific response from those systems designed to provide support to the user. It is this response that is the ultimate use of recognized activity. Hence, the activity may be considered as a command to the system. Extending this concept, a set of distinct activities in the form of hand and arm gestures may form the basis of a command interface for human-robot interaction. A gesture-based interface of this type promises an intuitive method for accessing computing and other assistive resources so as to promote rapid adoption by elderly, impaired, or otherwise unskilled users. This thesis includes a thorough survey of relevant work in the area of machine learning for activity and gesture recognition. Previous approaches are compared for their relative benefits and limitations. A novel approach is presented which utilizes user-generated feedback to rate the desirability of a robotic response to gesture. Poorly rated responses are altered so as to elicit improved ratings on subsequent observations. In this way, responses are honed toward increasing effectiveness. A clustering method based on the Growing Neural Gas (GNG) algorithm is used to create a topological map of reference nodes representing input gesture types. It is shown that learning of desired responses to gesture may be accelerated by exploiting well-rewarded actions associated with reference nodes in a local neighborhood of the growing neural gas topology. Significant variation in the user\u27s performance of gestures is interpreted as a new gesture for which the system must learn a desired response. A method for allowing the system to learn new gestures while retaining past training is also proposed and shown to be effective