Interoperability Infrastructure and Incremental learning for unreliable heterogeneous communicating Systems
Abstract: In a broader sense the main research objective of this thesis (and ongoing research work) is distributed knowledge management for mobile dynamic systems. But the primary focus and presented work focuses on communication/interoperability of heterogeneous entities in an infrastructure less paradigm, a distributed resource manipulation infrastructure and distributed learning in the absence of global knowledge. The research objectives achieved discover the design aspects of heterogeneous distributed knowledge systems towards establishing a seamless integration. This thesis doesn’t cover all aspects in this work; rather focuses on interoperability and distributed learning.Firstly a discussion on the issues in knowledge management for swarm of heterogeneous entities is presented. This is done in a broader and rather abstract fashion to provide an insight of motivation for interoperability and distributed learning towards knowledge management. Moreover this will also serve the reader to understand the ongoing work and research activities in much broader perspective.Primary focus of this thesis is communication/interoperability of heterogeneous entities in an infrastructure less paradigm, a distributed resource manipulation infrastructure and distributed learning in the absence of global knowledge. In dynamic environments for mobile autonomous systems such as robot swarms or mobile software agents there is a need for autonomic publishing and discovery of resources and just-in-time integration for on-the-fly service consumption without any a priori knowledge. SOA (Service-Oriented Architecture) serves the purpose of resource reuse and sharing of services different entities. Web services (a SOA manifestation) achieves these objectives but its exploitation in dynamic environments, where the communication infrastructure is lacking, requires a considerable research. Generally Web services are exploited in stable client-server paradigms, which is a pressing assumption when dynamic distributed systems are considered. UDDI (Universal Description Discovery and Integration) is the main pediment in the exploitation of Web services in distributed control and dynamic natured systems. UDDI can be considered as a directory for publication and discovery of categorized Web services but assumes a centralized registry; even if distributed registries and associated mechanism are employed problems of collaborative communication in infrastructure less paradigms are ignored.Towards interoperability main contribution this thesis is a mediator-based distributed Web services discovery and invocation middleware, which provides a collaborative and decentralized services discovery and management middleware for infrastructure-less mobile dynamic systems with heterogeneous communication capabilities. Heterogeneity of communication capabilities is abstracted in middleware by a conceptual classification of computing entities on the basis of their communication capabilities and communication issues are resolved via conceptual overlay formation for query propagation in system.The proposed and developed middleware has not only been evaluated extensively using Player Stage simulator but also been applied in physical robot swarms. Experimental validations analyze the results in different communication modes i. active and ii. passive mode of communication with and without shared resource conflict resolution. I analyze discoverable Web services with respect to time, services available in complete view of cluster and the impact and resultant improvements in distributed Web services discovery by using caching and semantics.Second part of this thesis focuses on distributed learning in the absence of global information. This thesis takes the argument of defeasibility (common-sense inference) as the basis of intelligence in human-beings, in which conclusions/inferences are drawn and refuted at the same time as more information becomes available. The ability of common-sense reasoning to adapt to dynamic environments and reasoning with uncertainty in the absence of global information seems to be best fit for distributed learning for dynamic systems.This thesis, thus, overviews epistemic cognition in human beings, which motivates the need of a similar epistemic cognitive solution in fabricated systems and considers formal concept analysis as a case for incremental and distributed learning of formal concepts. Thesis also presents a representational schema for underlying logic formalism and formal concepts. An algorithm for incremental learning and its use-case for robotic navigation, in which robots incrementally learn formal concepts and perform common-sense reasoning for their intelligent navigation, is also presented. Moreover elaboration of the logic formalism employed and details of implementation of developed defeasible reasoning engine is given in the latter half of this thesis.In summary, the research results and achievements described in this thesis focus on interoperability and distributed learning for heterogeneous distributed knowledge systems which contributes towards establishing a seamless integration in mobile dynamic systems.
CLICK HERE TO DOWNLOAD THE WHOLE DISSERTATION. (in PDF format)