Policy-based asset sharing in collaborative environments

Resource sharing is an important but complex problem to be solved. The problem is exacerbated in a dynamic coalition context, due to multi-partner constraints (imposed by security, privacy and general operational issues) placed on the resources. Take for example scenarios such as emergency response operations, corporate collaborative environments, or even short-lived opportunistic networks, where multi-party teams are formed, utilizing and sharing their own resources in order to support collective endeavors, which otherwise would be difficult, if not impossible, to achieve by a single party. Policy-Based Management Systems (PBMS) have been proposed as a suitable paradigm to reduce this complexity and provide a means for effective resource sharing. The overarching problem that this thesis deals with, is the development of PBMS techniques and technologies that will allow in a dynamic and transparent way, users that operate in collaborative environments to share their assets through high-level policies. To do so, it focuses on three sub-problems each one of which is related to a different aspect of a PBMS, making three key contributions. The first is a novel model, which proposes an alternative way for asset sharing, better fit than the traditional approaches when dealing with collaborative and dynamic environments. In order for all of the existing asset sharing approaches to comply with situational changes, an extra overhead is needed due to the fact that the decision making centre – and therefore, the policy making centre – is far away from where the changes take place unlike the event-driven approach proposed in this thesis. The second contribution is the proposal of an efficient, high-level policy conflict analysis mechanism, that provides a more transparent – in terms of user interaction – alternative way for maintaining unconflicted PBMS. Its discrete and sequential execution, breaks down the analysis process into discrete steps, making the conflict analysis more efficient compared to existing approaches, while eases human policy authors to track the whole process interfacing with it, in a near to natural language representation. The contribution of the third piece of research work is an interest-based policy negotiation mechanism, for enhancing asset sharing while promoting collaboration in coalition environments. The enabling technology for achieving the last two contributions (contribution 2 & 3) is a controlled natural language representation, which is used for defining a policy language. For evaluating the proposed ideas, in the first and third contributions we run simulation experiments while we simulate and also conduct formal analysis for the second one

Conversational Sensing

Recent developments in sensing technologies, mobile devices and context-aware user interfaces have made it possible to represent information fusion and situational awareness as a conversational process among actors - human and machine agents - at or near the tactical edges of a network. Motivated by use cases in the domain of security, policing and emergency response, this paper presents an approach to information collection, fusion and sense-making based on the use of natural language (NL) and controlled natural language (CNL) to support richer forms of human-machine interaction. The approach uses a conversational protocol to facilitate a flow of collaborative messages from NL to CNL and back again in support of interactions such as: turning eyewitness reports from human observers into actionable information (from both trained and untrained sources); fusing information from humans and physical sensors (with associated quality metadata); and assisting human analysts to make the best use of available sensing assets in an area of interest (governed by management and security policies). CNL is used as a common formal knowledge representation for both machine and human agents to support reasoning, semantic information fusion and generation of rationale for inferences, in ways that remain transparent to human users. Examples are provided of various alternative styles for user feedback, including NL, CNL and graphical feedback. A pilot experiment with human subjects shows that a prototype conversational agent is able to gather usable CNL information from untrained human subjects

Human-machine conversations to support multi-agency missions

In domains such as emergency response, environmental monitoring, policing and security, sensor and information networks are deployed to assist human users across multiple agencies to conduct missions at or near the 'front line'. These domains present challenging problems in terms of human-machine collaboration: human users need to task the network to help them achieve mission objectives, while humans (sometimes the same individuals) are also sources of mission-critical information. We propose a natural language-based conversational approach to supporting humanmachine working in mission-oriented sensor networks. We present a model for human-machine and machine-machine interactions in a realistic mission context, and evaluate the model using an existing surveillance mission scenario. The model supports the flow of conversations from full natural language to a form of Controlled Natural Language (CNL) amenable to machine processing and automated reasoning, including high-level information fusion tasks. We introduce a mechanism for presenting the gist of verbose CNL expressions in a more convenient form for human users. We show how the conversational interactions supported by the model include requests for expansions and explanations of machine-processed information

Interest-based negotiation for asset sharing policies

Resource sharing is an important but complex problem to be solved. The problem is exacerbated in a coalition context due to policy constraints placed on the resources. Thus, to effectively share resources, members of a coalition need to negotiate on policies and at times refine them to meet the needs of the operating environment. Towards achieving this goal, in this work we propose a novel policy negotiation mechanism based on the interest-based negotiation paradigm. Interest-based negotiation promotes collaboration when compared with more traditional negotiation approaches such as position-based negotiations

Sharing policies for multi-partner asset management in smart environments

Abstract—Smart environments are ecosystems, which seam-lessly embed IT assets into physical world’s objects and hold promise for improving the services we receive from our social and economic ecosystems. The management of smart environment assets in multi-partner, dynamic collaboration scenarios where different sets of assets are owned and operated by different partners is a non-trivial problem, due to restrictive asset sharing policies applied by collaborating partners. In this work we formalize, evaluate and compare two asset sharing policies, investigating their impact on MSTA-P, a policy-regulated version of an existing asset-task assignment protocol. The first sharing policy is based on a traditional asset ownership model while the second is based on an edge model allowing asset sharing among collaborating partnes through cross-partner team formations. We find that while the traditional ownership model allows slightly better performance, the difference is only marginal, so a team-sharing model offers a viable alternative sharing approach. I

Human-machine conversations to support mission-oriented information provision

Mission-oriented sensor networks present challenging problems in terms of human-machine collaboration. Human users need to task the network to help them achieve mission objectives, while humans (sometimes the same individuals) are also sources of mission-critical information. We propose a natural language-based conversational approach to supporting human-machine working in mission-oriented sensor networks. We present a model for human-machine and machine-machine interactions in a realistic mission context, and evaluate the model using an existing surveillance mission scenario. The model supports the flow of conversations from full natural language to a form of Controlled Natural Language (CNL) amenable to machine processing and automated reasoning, including high-level information fusion tasks. We introduce a mechanism for presenting the gist of verbose CNL expressions in a more convenient form for human users. We show how the conversational interactions supported by the model include requests for expansions and explanations of machine-processed information