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.

Towards a Monitoring Framework for Agent-Based Contract Systems

The behaviours of autonomous agents may deviate from those deemed to be for the good of the societal systems of which they are a part. Norms have therefore been proposed as a means to regulate agent behaviours in open and dynamic systems, and may be encoded in electronic contracts in order to specify the obliged, permitted and prohibited behaviours of agents that are signatories to such contracts. Enactment and management of electronic contracts thus enables the use of regulatory mechanisms to ensure that agent behaviours comply with the encoded norms. To facilitate such mechanisms requires monitoring in order to detect and explain violation of norms. In this paper we propose a framework for monitoring that is to be implemented and integrated into a suite of contract enactment and management tools. The framework adopts a non-intrusive approach to monitoring, whereby the states of a contract with respect to its contained norms can be inferred on the basis of messages exchanged. Specifically, the framework deploys agents that observe messages sent between contract signatories, where these messages correspond to agent behaviours and therefore indicate whether norms are, or are in danger of, being violated.

A Framework for Monitoring Agent-Based Normative Systems

The behaviours of autonomous agents may deviate from those deemed to be for the good of the societal systems of which they are a part. Norms have therefore been proposed as a means to regulate agent behaviours in open and dynamic systems, where these norms specify the obliged, permitted and prohibited behaviours of agents. Regulation can effectively be achieved through use of enforcement mechanisms that result in a net loss of utility for an agent in cases where the agent's behaviour fails to comply with the norms. Recognition of compliance is thus crucial for achieving regulation. In this paper we propose a generic architecture for observation of agent behaviours, and recognition of these behaviours as constituting, or counting as, compliance or violation. The architecture deploys monitors that receive inputs from observers, and processes these inputs together with transition network representations of individual norms. In this way, monitors determine the fulfillment or violation status of norms. The paper also describes a proof of concept implementation and deployment of monitors in electronic contracting environments.

A Framework for Monitoring Agent-Based Normative Systems

The behaviours of autonomous agents may deviate from those deemed to be for the good of the societal systems of which they are a part. Norms have therefore been proposed as a means to regulate agent behaviours in open and dynamic systems, where these norms specify the obliged, permitted and prohibited behaviours of agents. Regulation can effectively be achieved through use of enforcement mechanisms that result in a net loss of utility for an agent in cases where the agent’s behaviour fails to comply with the norms. Recognition of compliance is thus crucial for achieving regulation. In this paper we propose a generic architecture for observation of agent behaviours, and recognition of these behaviours as constituting, or counting as, compliance or violation. The architecture deploys monitors that receive inputs from observers, and processes these inputs together with transition network representations of individual norms. In this way, monitors determine the fulfillment or violation status of norms. The paper also describes a proof of concept implementation and deployment of monitors in electronic contracting environments.

Monitoring and Explanation of Contract Execution: A Case Study in the Aerospace Domain

In the domain of aerospace aftermarkets, which often has long supply chains that feed into the maintenance of aircraft, contracts are used to establish agreements between aircraft operators and maintenance suppliers. However, violations at the bottom of the supply chain (part suppliers) can easily cascade to the top (aircraft operators), making it difficult to determine the source of the violation, and seek to address it. In this context, we have developed a global monitoring architecture that ensures the detection of norm violations and generates explanations for the origin of violations. In this paper, we describe the implementation and deployment of a global monitor in the aerospace domain of [8] and show how it generates explanations for violations within the maintenance supply chain. We show how these explanations can be used not only to detect violations at runtime, but also to uncover potential problems in contracts before their deployment, thus improving them.

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.