Electronic contracts are a means of representing agreed responsibilities and expected behaviour of autonomous agents acting on behalf of businesses. They can be used to regulate behaviour by providing negative consequences, penalties, where the responsibili- ties and expectations are not met, i.e. the contract is violated. How- ever, long-term business relationships require some flexibility in the face of circumstances that do not conform to the assumptions of the contract, that is, mitigating circumstances. In this paper, we describe how contract parties can represent and enact policies on mitigating circumstances. As part of this, we require records of what has oc- curred within the system leading up to a violation: the provenance of the violation. We therefore bring together contract-based and prove- nance systems to solve the issue of mitigating circumstances.

Expressing contractual agreements electronically potentially allows agents to automatically perform functions surrounding contract use: establish- ment, fulfilment, renegotiation etc. For such automation to be used for real busi- ness concerns, there needs to be a high level of trust in the agent-based system. While there has been much research on simulating trust between agents, there are areas where such trust is harder to establish. In particular, contract proposals may come from parties that an agent has had no prior interaction with and, in competitive business-to-business environments, little reputation information may be available. In human practice, trust in a proposed contract is determined in part from the content of the proposal itself, and the similarity of the content to that of prior contracts, executed to varying degrees of success. In this paper, we argue that such analysis is also appropriate in automated systems, and to provide it we need systems to record salient details of prior contract use and algorithms for as- sessing proposals on their content.We use provenance technology to provide the former and detail algorithms for measuring contract success and similarity for the latter, applying them to an aerospace case study.

Both the scientific and business communities, which are beginning to rely on Grids as problem-solving mechanisms, have requirements in terms of provenance. The provenance of some data is the documentation of process that led to the data; its necessity is apparent in fields ranging from medicine to aerospace. To support provenance capture in Grids, we have developed an implementation-independent protocol for the recording of provenance. We describe the protocol in the context of a service-oriented architecture and formalise the entities involved using an abstract state machine or a three-dimensional state transition diagram. Using these techniques we sketch a liveness property for the system.

Both the scientific and business communities are beginning to rely on Grids as problemsolving mechanisms. These communities also have requirements in terms of provenance. Provenance is the documentation of process and the necessity for it is apparent in fields ranging from medicine to aerospace. To support provenance capture in Grids, we have developed an implementation-independent protocol for the recording of provenance. We describe the protocol in the context of a service-oriented architecture and formalise the entities involved using an abstract state machine or a three-dimensional state transition diagram. Using these techniques we sketch a liveness property for the system.