Causal models provide a formal framework for identifying and reasoning about the causes of observed phenomena, making them valuable for decision-support contexts where understanding causality is essential. Yet applying these models in practice requires automated tools for key reasoning tasks. We present an Answer Set Programming (ASP)-based tool that supports three core capabilities for all acyclic binary causal models: (1) checking whether an event is an actual cause of another; (2) finding all minimal subsets of a failed candidate that do qualify as causes; and (3) inferring all actual causes of an outcome without assuming any candidate. Our tool is the first to support all three tasks within a unified framework, guaranteeing minimal contingency sets and outperforming prior implementations in both runtime and memory. We describe the system’s design and report on an empirical evaluation using existing benchmarks.

The integration of wireless power technology in large sensor networks is highly sought for in many applications, including agriculture. This is due to the accessibility and lack of wiring complexity such technologies have to offer. In an agricultural setting, the working environment can be harsh on sensing equipment due to factors that include weather, constant deconstruction and re-installation with the changing plant cycles, and vehicle traffic. Since many agriculture plots reside in difficult to access locations, the use of self-sufficient energy capturing methods have become popular. These contemporary methods generally rely on the collection of solar, wind, or ambient radio waves to charge battery banks connected to the sensing device. These methods have major limitations as sunlight can be shadowed as crops mature, wind creates obstacles for equipment to navigate, and radio frequencies do not penetrate well through soil or plants. This ultimately reduces the quantity of sensors that can be instrumented throughout a field. To address such limitations, a new wireless power transfer method will be presented that utilizes a buried transmitter to generate conduction currents through the soil to power distant sensing devices scattered throughout a field. Impedance spectra of the soil is used to determine the optimal depth of the transmitter. The power capabilities of the system are demonstrated by operating, without a battery, a moisture sensor connected to a microcontroller at a 10 m distance from the transmitter.

Reinforcement Learning (RL) suffers from several difficulties when applied to domains with no obvious goal-state defined; this leads to inefficiency in RL algorithms. We consider a solution within the context of a widely-used testbed for RL: RoboCup Keepaway. We introduce Argumentation-Based RL (ABRL), using methods from argumentation theory to integrate domain knowledge, represented by arguments, into the SMDP algorithm for RL by using potential-based reward shaping. Empirical results show that ABRL outperforms the original SMDP algorithm, for this game, by improving convergence speed and optimality.

Access control operates under the assumption that it is possible to correctly encode and predict all subjects' needs and rights. However, in human-centric pervasive domains, such as health care, it is hard if not impossible to encode all emergencies and exceptions, but also to imagine a priori all the permissible requests. Break-glass is an approach that em- bodies the idea that under certain conditions it is possible for a subject to break-the-glass and explicitly override the denied request. Current break-glass models make this decision without considering and investigating what the reasons for issuing the denial are, and they have a fixed decision procedure to determine whether the override is permitted. Furthermore, they do not explicitly represent and reason over conflicting and missing information about subjects and the context; which in human-centric pervasive domains is a norm rather than an anomaly. This paper presents a novel break-glass model, Rumpole, that structures a break-glass policy by establishing why the access was denied. It uses Belnap's four-valued logic to represent conflicting and missing (unknown) information, allowing the policy to make a more informed decision when faced with missing or inconsistent knowledge. The model also provides a declarative query language that is used to specify an explicit break-glass decision procedure, rather than having an implicitly hard-coded one. This allows a policy writer to further condition and restrict when and how break-glass access is permitted.

The automation of policy refinement, whilst promising great benefits for policy-based management, has hitherto received relatively little treatment in the literature, with few concrete approaches emerging. In this paper we present initial steps towards a framework for automated distributed policy refinement for both obligation and authorization policies. We present examples drawn from military scenarios, describe details of our formalism and methods for action decomposition, and discuss directions for future research.

Despite several research studies, the effective analysis of policy based systems remains a significant challenge. Policy analysis should at least (i) be expressive (ii) take account of obligations and authorizations, (iii) include a dynamic system model, and (iv) give useful diagnostic information. We present a logic-based policy analysis framework which satisfies these requirements, showing how many significant policy-related properties can be analysed, and we give details of a prototype implementation.