Provenance in Agent-mediated Healthcare Systems

Agent-oriented cooperation techniques and standardized electronic healthcare record exchange protocols can be used to combine information regarding different facets of a therapy received by a patient from different healthcare providers at different locations. Provenance is an innovative approach to trace events in complex distributed processes, dependencies between such events, and associated decisions by human actors. We focus on three aspects of provenance in agent-mediated healthcare systems: first, we define the provenance concept and show how it can be applied to agent-mediated healthcare applications; second, we investigate and provide a method for independent and autonomous healthcare agents to document the processes they are involved in without directly interacting with each other; and third, we show that this method solves the privacy issues of provenance in agent-mediated healthcare systems.

Implementing Policy Management through BDI

The requirement for Grid middleware to be largely transparent to individual users and at the same time act in accordance with their personal needs is a difficult challenge. In e-science scenarios, users cannot be repeatedly interrogated for each operational decision made when enacting experiments on the Grid. It is thus important to specify and enforce policies that enable the environment to be configured to take user preferences into account automatically. In particular, we need to consider the context in which these policies are applied, because decisions are based not only on the rules of the policy but also on the current state of the system. Consideration of context is explicitly addressed, in the agent perspective, when deciding how to balance the achievement of goals and reaction to the environment. One commonly-applied abstraction that balances reaction to multiple events with context-based reasoning in the way suggested by our requirements is the belief-desire-intention (BDI) architecture, which has proven successful in many applications. In this paper, we argue that BDI is an appropriate model for policy enforcement, and describe the application of BDI to policy enforcement in personalising Grid service discovery. We show how this has been implemented in the myGrid registry to provide bioinformaticians with control over the services returned to them by the service discovery process.

Modelling the Provenance of Data in Autonomous Systems

Determining the provenance of data, i.e. the process that led to that data, is vital in many disciplines. For example, in science, the process that produced a given result must be demonstrably rigorous for the result to be deemed reliable. A provenance system supports applications in recording adequate documentation about process executions to answer queries regarding provenance, and provides functionality to perform those queries. Several provenance systems are being developed, but all focus on systems in which the components are textitreactive, for example Web Services that act on the basis of a request, job submission system, etc. This limitation means that questions regarding the motives of autonomous actors, or textitagents, in such systems remain unanswerable in the general case. Such questions include: who was ultimately responsible for a given effect, what was their reason for initiating the process and does the effect of a process match what was intended to occur by those initiating the process? In this paper, we address this limitation by integrating two solutions: a generic, re-usable framework for representing the provenance of data in service-oriented architectures and a model for describing the goal-oriented delegation and engagement of agents in multi-agent systems. Using these solutions, we present algorithms to answer common questions regarding responsibility and success of a process and evaluate the approach with a simulated healthcare example.