Ho-Pun Lam

Due to pressure from regulatory authorities, the requirement to remain compliant has tremendously increased over the last decade. To support compliance-related activities, a plethora of compliance management frameworks (CMFs), compliance languages and systems have emerged, which is on one hand advantageous, but may cause confusion when deciding which CMF can be used to best fulfil the organisation’s internal requirements. This is due to the lack of acceptable compliance tools and methodologies in the compliance domain to uncover and compare the multidimensionality of capability between different frameworks and users’ needs, which give raise to the question of how to formally evaluate a CMF. In this paper, we propose methodologies to formally evaluate CMFs, compliance languages and systems, in particular the underlying formal language of a CMF; and present the formal evaluation of two prominent formal language-based CMFs, namely, PENELOPE and PCL, with a business contract using formal analysis approach. Our evaluations formally validate that the proposed methodologies are instrumental in deciding on the suitability of a CMF when is comes to evaluating the underlying formal logic of the framework to represent different types of norms.

In order to automate verification process, regulatory rules written in natural language need to be translated into a format that machines can understand. However, none of the existing formalisms can fully represent the elements that appear in legal norms. For instance, most of these formalisms do not provide features to capture the behavior of deontic effects, which is an important aspect in automated compliance checking. This paper presents an approach for transforming legal norms represented using legalruleml to a variant of modal defeasible logic (and vice versa) such that a legal statement represented using LegalRuleML can be transformed into a machine-readable format that can be understood and reasoned about depending upon the client's preferences.

In order to automate verification process, regulatory rules written in natural language need to be translated into a format that machines can understand. However, none of the existing formalisms can fully represent the elements that appear in legal norms. For instance, most of these formalisms do not provide features to capture the behavior of deontic effects, which is an important aspect in automated compliance checking. This paper presents an approach for transforming legal norms represented using LegalRuleML to a variant of Modal Defeasible Logic (and vice versa) such that a legal statement represented using LegalRuleML can be transformed into a machine-readable format that can be understood and reasoned about depending upon the client's preferences.

This article shows how a non-monotonic rule based system (defeasible logic) can be integrated with numerical computation engines, and how this can be applied to solve the Vehicle Routing Problem. To this end, we have simulated a physical system from which we can obtain numerical information. The physical system perceives information from its environment and generates predicates that can be reasoned by a defeasible logic engine. The conclusions/decisions derived will then be realized by the physical system as it takes actions based on the conclusion derived. Here we consider a scenario where a ‘flock’ of Unmanned Autonomous Vehicles (UAVs) have to navigate within an urban canyon environment. The UAVs are self-autonomous without centralized control. The goal of the UAVs is to navigate to their desired destinations without colliding with each other. In case of possible collision, the UAVs concerned will communicate with each other and use their background knowledge or travel guidelines to resolve the conflicts.

Adaptation decisions made by context-aware applications on behalf of users are based on evaluations of current context and preferences of users. This context information is imperfect by nature and can cause applications to behave in ways that users do not expect. Applications that exhibit unwanted behaviour will negatively impact their usability and violate the trust users have in them. Intelligibility and control in applications can help users to understand why they decided to behave in certain ways, and to forgive the applications by enabling users to override the undesirable adaptation. This paper presents a non-monotonic rule based approach (defeasible logic) for modelling user preferences, which serves as the basis of decision-making of application adaptations. It facilitates automatic generation of explanations regarding reasoning of defeasible preferences. The model also supports creation of feedback mechanisms for nontechnical users to formulate their own preferences independently, and modify the adaptation decision process to control application behaviours. Moreover, to demonstrate its applicability we have designed a set of evolvable situations and a context model, which complement the defeasible preferences for building smart home applications to enhance health care of the elderly.