Ubiquitous Cognitive Computing: A Vector Symbolic Approach
Abstract: A wide range of physical things are currently being integrated with the infrastructure of cyberspace in a process that is creating the so-called Internet of Things. It is expected that Internet-connected devices will vastly outnumber people on the planet in the near future. Such devices need to be easily deployed and integrated, otherwise the resulting systems will be too costly to configure and maintain. This is challenging to accomplish using conventional technology, especially when dealing with complex or heterogeneous systems consisting of diverse components that implement functionality and standards in different ways. In addition, artificial systems that interact with humans, the environment and one-another need to deal with complex and imprecise information, which is difficult to represent in a flexible and standardized manner using conventional methods. This thesis investigates the use of cognitive computing principles that offer new ways to represent information and design such devices and systems. The core idea underpinning the work presented herein is that functioning systems can potentially emerge autonomously by learning from user interactions and the environment provided that each component of the system conforms to a set of general information-coding and communication rules. The proposed learning approach uses vector-based representations of information, which are common in models of cognition and semantic spaces. Vector symbolic architectures (VSAs) are a class of biology-inspired models that represent and manipulate structured representations of information, which can be used to model high-level cognitive processes such as analogy-making. Analogy-making is a central element of cognition that enables animals to identify and manage new information by generalizing past experiences, possibly from a few learned examples. The work presented herein is based on a VSA and a binary associative memory model known as sparse distributed memory. The thesis outlines a learning architecture for the automated configuration and interoperation of devices operating in heterogeneous and ubiquitous environments. To this end, the sparse distributed memory model is extended with a VSA-based analogy-making mechanism that enables generalization from a few learned examples, thereby facilitating rapid learning. The thesis also presents a generalization of random indexing, which is an incremental and lightweight feature extraction method for streaming data that is commonly used to generate vector representations of semantic spaces. The impact of this thesis is twofold. First, the appended papers extend previous theoretical and empirical work on vector-based cognitive models, in particular for analogy-making and learning. Second, a new approach for designing the next generation of ubiquitous cognitive systems is outlined, which in principle can enable heterogeneous devices and systems to autonomously learn how to interoperate.
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