Handling Mitigating Circumstances for Electronic Contracts

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 responsibilities and expectations are not met, i.e. the contract is violated. However, 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 occurred within the system leading up to a violation: the provenance of the violation. We therefore bring together contract-based and provenance systems to solve the issue of mitigating circumstances.

Determining the Trustworthiness of New Electronic Contracts

Expressing contractual agreements electronically potentially allows agents to automatically perform functions surrounding contract use: establishment, fulfilment, renegotiation etc. For such automation to be used for real business 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 assessing 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.

Electronic Business Contracts between Services

Electronic contracts mirror the paper versions exchanged between businesses today, and offer the possibility of dynamic, automatic creation and enforcement of restrictions and compulsions on service behaviour that are designed to ensure business objectives are met. Where there are many contracts within a particular application, it can be difficult to determine whether the system can reliably fulfil them all, yet computer-parsable electronic contracts may allow such verification to be automated. In this chapter, we describe a conceptual framework and architecture specification in which normative business contracts can be electronically represented, verified, established, renewed, and so on. In particular, we aim to allow systems containing multiple contracts to be checked for conflicts and violations of business objectives. We illustrate the framework and architecture with an aerospace aftermarket example.

Using Normative Markov Decision Processes for Evaluating Electronic Contracts: A Case Study in a Simulated Aerospace Aftermarket

Before signing electronic contracts, a rational agent should estimate the expected utilities of these contracts and calculate the violation risks related to them. In order to perform such pre-signing procedures, this agent has to be capable of computing a policy taking into account the norms and sanctions in the contracts. In relation to this, the contribution of this work is threefold. First, we present the Normative Markov Decision Process, an extension of the Markov Decision Process for explicitly representing norms. In order to illustrate the usage of our framework, we model an example in a simulated aerospace aftermarket. Second, we specify an algorithm for identifying the states of the process which characterize the violation of norms. Finally, we show how to compute policies with our framework and how to calculate the risk of violating the norms in the contracts by adopting a particular policy.