In making practical decisions, agents are expected to comply with ideals of behaviour, or norms. In reality, it may not be possible for an individual, or a team of agents, to be fully compliant-actual behaviour often differs from the ideal. The question we address in this paper is how we can design agents that act in such a way that they select collective strategies to avoid more critical failures (norm violations), and mitigate the effects of violations that do occur. We model the normative requirements of a system through contrary-to-duty obligations and violation severity levels, and propose a novel multi-agent planning mechanism based on Decentralised POMDPs that uses a qualitative reward function to capture levels of compliance: N-Dec-POMDPs. We develop mechanisms for solving this type of multi-agent planning problem and show, through empirical analysis, that joint policies generated are equally as good as those produced through existing methods but with significant reductions in execution time.
Norms specify ideal behaviour. Agents, however, are autonomous, and may fail to comply with the ideal. Contrary to Duty obligations can be used to specify reparational behaviour that mitigates the effects of a violation. In addition to specifying reparational behaviours, it is important to understand how robust a system is against possible violations. Depending on what kind of system property we want to preserve, non-compliance with different norms may be of varying severity. We propose a method for analysing robustness of normative systems, with support for Contrary to Duty obligations. We introduce violation severity as a concept orthogonal to reparational behaviour and specify it by means of a partial order over norms. We use this severity partial order, together with normative specifications, to rank the possible worlds from the most to the least compliant. In this way, we are able to use model checking to analyse robustness to a certain severity, or whether it is possible to achieve a certain goal, without violating any norm of a given severity.
Existing approaches for the verification of normative systems consider limited representations of norms, often neglecting collective imperatives, deadlines and contrary-to-duty obligations. In order to capture the requirements of real-world scenarios, these structures are important. In this paper we propose methods for the specification and formal verification of complex normative systems that include contraryto-duty, collective and event-driven imperatives with deadlines. We propose an operational syntax and semantics for the specification of such systems. Using Maude and its linear temporal logic model checker, we show how important properties can be verified for such systems, and provide some experimental results for both bounded and unbounded verification.
Over the last few years there has been a rapid development of technologies such as ubiquitous computing and distributed multi-agent systems. As a consequence an increasing need to share information securely in a distributed dynamic environment has arisen. Risk-aware access control (RAAC) has recently shown promise as an approach to addressing this need of flexible and dynamical access control requirements. Additionally, OASIS proposed XACML as a new standard XML-based language for writing access control policies, requests and responses. The standard specification also defines reference architecture for implementing an XACML based system. Despite the fact that XACML is designed to support various access control models, we believe it doesn't provide a natural way for defining RAAC policies. In this paper we propose an approach that uses standard XACML features to implement RAAC. In particular, we abstract core components of RAAC relevant to risk assessment and risk mitigation, and illustrate how to define XACML policies to implement these components. We also propose a modular architecture for the XACML obligations service to handle both system and user obligations, which are typically used as risk mitigation methods in RAAC.
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