Distributed Algorithms
Engagement and Cooperating in Motivated Agent Modelling
Proceedings of the First Australian Workshop on DAI: Distributed Artificial Intelligence: Architecture and Modelling
The Grid 2: Blueprint for a New Computing Infrastructure
The Grid 2: Blueprint for a New Computing Infrastructure
Brain Meets Brawn: Why Grid and Agents Need Each Other
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 1
Lineage retrieval for scientific data processing: a survey
ACM Computing Surveys (CSUR)
A survey of data provenance in e-science
ACM SIGMOD Record
Provenance in Agent-Mediated Healthcare Systems
IEEE Intelligent Systems
PrIMe: a software engineering methodology for developing provenance-aware applications
Proceedings of the 6th international workshop on Software engineering and middleware
The provenance of electronic data
Communications of the ACM - The psychology of security: why do good users make bad decisions?
Enhancing workflow with a semantic description of scientific intent
ESWC'08 Proceedings of the 5th European semantic web conference on The semantic web: research and applications
AgentPrIMe: adapting MAS designs to build confidence
AOSE'07 Proceedings of the 8th international conference on Agent-oriented software engineering VIII
Efficient querying of distributed provenance stores
Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing
The Foundations for Provenance on the Web
Foundations and Trends in Web Science
AgentSwitch: towards smart energy tariff selection
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
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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 reactive, 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 agents, 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.