This paper presents Cyclone, an intelligent agent based visual framework offering a means for the... more This paper presents Cyclone, an intelligent agent based visual framework offering a means for the user to exploit, analyze and categorize unstructured information from various sources into a more structured and manageable form. The intelligent agent performs two processes, the first of which gathers the information, analyzes it and determines physical forces on visual objects which represent the information thus achieving unsupervised graph-based clustering based on lightweight metadata of the information, i.e. tags. Once an equilibrium state has been reached, the arrangement of similar information into visual fuzzy clusters and the intuitive interface of Cyclone aid the user in the process of categorization. The second process of the agent consists of monitoring the users and learning their categorization behavior in an online and nonintrusive fashion. Over time, as the derived categorization model for a particular user becomes increasingly confident, the Cyclone agent switches to an auto-categorization mode, thus automating the process of categorization for new or unassigned information and reducing the cognitive load for the user. The updated categorization model adapts the forces on the visual objects so that the visual clusters presented take into account users' behavior, combining aspects from both the unsupervised and supervised (learnt) approaches. We have conducted several multi-user experiments using real data from different application contexts in order to gain both a qualitative understanding of the user experience as well as collect quantitative data on how well the system performs, in particular, how presenting visual fuzzy clusters of the information affects a user's categorization behavior. The results illustrate that Cyclone's intelligent agent, performing clustering and categorization, coupled with an intuitive visualization interface represent an effective way of aiding users in generating a taxonomy on-the-fly and automating the process.
The problem facing the security and defence communities is the volume, complexity and timeliness ... more The problem facing the security and defence communities is the volume, complexity and timeliness of information. In particular the ability to locate and access the right ICT service at the right time is crucial to achieving real-time responsiveness and situational awareness. The Nexus system is a Peer-to-Peer (P2P) agent-based middleware that creates a fully distributed and highly resilient Service Oriented Architecture (SOA). The combination of a structured P2P overlay network and autonomous service discovery, delivers a powerful capability to support real-time operations in either security or defence applications. This paper outlines the overall architecture of the Nexus system and its application in a defence scenario with a detailed review of the service selection algorithm utilised, termed Mercury. Mercury provides an autonomous, efficient and distributed service selection framework and collaborative algorithms for SOA construction and real-time adaptation.
Cyclone is a mixed-initiative and adaptive clustering and structure generation environment which ... more Cyclone is a mixed-initiative and adaptive clustering and structure generation environment which is capable of learning categorization behavior through user interaction as well as conducting auto-categorization based on the extracted model. The strength of Cyclone resides in its integration of several visualization and interface techniques with data mining and AI learning processes. This paper presents the intuitive visual interface of Cyclone which empowers the user to explore, analyze, exploit and structure unstructured information from various sources generating a personalized taxonomy in real-time and on-the-fly.
The Essex intelligent dormitory, iDorm, uses embedded agents to create an ambient-intelligence en... more The Essex intelligent dormitory, iDorm, uses embedded agents to create an ambient-intelligence environment. In a five-and-a-half-day experiment, a user occupied the iDorm, testing its ability to learn user behavior and adapt to user needs. The embedded agent discreetly controls the iDorm according to user preferences. Our work focuses on developing learning and adaptation techniques for embedded agents. We seek to provide online, lifelong, personalized learning of anticipatory adaptive control to realize the ambient-intelligence vision in ubiquitous-computing environments. We developed the Essex intelligent dormitory, or iDorm, as a test bed for this work and an exemplar of this approach.
We present the iDorm which is an experimental test bed for ubiquitous computing environments rese... more We present the iDorm which is an experimental test bed for ubiquitous computing environments research at Essex University. The iDorm is equipped with a number of passive and intelligent embedded agents with different capabilities and restrictions. In this work we define a passive agent as an embedded computer that delivers purely sensory information, whereas the intelligent embedded agents are embedded-computers enhanced with intelligent reasoning and learning.
This paper presents Cyclone, an intelligent agent based visual framework offering a means for the... more This paper presents Cyclone, an intelligent agent based visual framework offering a means for the user to exploit, analyze and categorize unstructured information from various sources into a more structured and manageable form. The intelligent agent performs two processes, the first of which gathers the information, analyzes it and determines physical forces on visual objects which represent the information thus achieving unsupervised graph-based clustering based on lightweight metadata of the information, i.e. tags. Once an equilibrium state has been reached, the arrangement of similar information into visual fuzzy clusters and the intuitive interface of Cyclone aid the user in the process of categorization. The second process of the agent consists of monitoring the users and learning their categorization behavior in an online and nonintrusive fashion. Over time, as the derived categorization model for a particular user becomes increasingly confident, the Cyclone agent switches to an auto-categorization mode, thus automating the process of categorization for new or unassigned information and reducing the cognitive load for the user. The updated categorization model adapts the forces on the visual objects so that the visual clusters presented take into account users' behavior, combining aspects from both the unsupervised and supervised (learnt) approaches. We have conducted several multi-user experiments using real data from different application contexts in order to gain both a qualitative understanding of the user experience as well as collect quantitative data on how well the system performs, in particular, how presenting visual fuzzy clusters of the information affects a user's categorization behavior. The results illustrate that Cyclone's intelligent agent, performing clustering and categorization, coupled with an intuitive visualization interface represent an effective way of aiding users in generating a taxonomy on-the-fly and automating the process.
The problem facing the security and defence communities is the volume, complexity and timeliness ... more The problem facing the security and defence communities is the volume, complexity and timeliness of information. In particular the ability to locate and access the right ICT service at the right time is crucial to achieving real-time responsiveness and situational awareness. The Nexus system is a Peer-to-Peer (P2P) agent-based middleware that creates a fully distributed and highly resilient Service Oriented Architecture (SOA). The combination of a structured P2P overlay network and autonomous service discovery, delivers a powerful capability to support real-time operations in either security or defence applications. This paper outlines the overall architecture of the Nexus system and its application in a defence scenario with a detailed review of the service selection algorithm utilised, termed Mercury. Mercury provides an autonomous, efficient and distributed service selection framework and collaborative algorithms for SOA construction and real-time adaptation.
Cyclone is a mixed-initiative and adaptive clustering and structure generation environment which ... more Cyclone is a mixed-initiative and adaptive clustering and structure generation environment which is capable of learning categorization behavior through user interaction as well as conducting auto-categorization based on the extracted model. The strength of Cyclone resides in its integration of several visualization and interface techniques with data mining and AI learning processes. This paper presents the intuitive visual interface of Cyclone which empowers the user to explore, analyze, exploit and structure unstructured information from various sources generating a personalized taxonomy in real-time and on-the-fly.
The Essex intelligent dormitory, iDorm, uses embedded agents to create an ambient-intelligence en... more The Essex intelligent dormitory, iDorm, uses embedded agents to create an ambient-intelligence environment. In a five-and-a-half-day experiment, a user occupied the iDorm, testing its ability to learn user behavior and adapt to user needs. The embedded agent discreetly controls the iDorm according to user preferences. Our work focuses on developing learning and adaptation techniques for embedded agents. We seek to provide online, lifelong, personalized learning of anticipatory adaptive control to realize the ambient-intelligence vision in ubiquitous-computing environments. We developed the Essex intelligent dormitory, or iDorm, as a test bed for this work and an exemplar of this approach.
We present the iDorm which is an experimental test bed for ubiquitous computing environments rese... more We present the iDorm which is an experimental test bed for ubiquitous computing environments research at Essex University. The iDorm is equipped with a number of passive and intelligent embedded agents with different capabilities and restrictions. In this work we define a passive agent as an embedded computer that delivers purely sensory information, whereas the intelligent embedded agents are embedded-computers enhanced with intelligent reasoning and learning.
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Papers by Hakan Duman