In memoriam Douglas N. Walton: the influence of Doug Walton on AI and law

Doug Walton, who died in January 2020, was a prolific author whose work in informal logic and argumentation had a profound influence on Artificial Intelligence, including Artificial Intelligence and Law. He was also very interested in interdisciplinary work, and a frequent and generous collaborator. In this paper seven leading researchers in AI and Law, all past programme chairs of the International Conference on AI and Law who have worked with him, describe his influence on their work

PyArg for solving and explaining rgumentation in python

We introduce PyArg, a Python-based solver and explainer for both abstract argumentation and ASPIC+. A large variety of extension-based semantics allows for flexible evaluation and several explanation functions are available

Thirty years of Artificial Intelligence and Law:the second decade

The first issue of Artificial Intelligence and Law journal was published in 1992. This paper provides commentaries on nine significant papers drawn from the Journal’s second decade. Four of the papers relate to reasoning with legal cases, introducing contextual considerations, predicting outcomes on the basis of natural language descriptions of the cases, comparing different ways of representing cases, and formalising precedential reasoning. One introduces a method of analysing arguments that was to become very widely used in AI and Law, namely argumentation schemes. Two relate to ontologies for the representation of legal concepts and two take advantage of the increasing availability of legal corpora in this decade, to automate document summarisation and for the mining of arguments

Stability and Relevance in Incomplete Argumentation Frameworks

We explore the computational complexity of stability and relevance in incomplete argumentation frameworks (IAFs), abstract argumentation frameworks that encode qualitative uncertainty by distinguishing between certain and uncertain arguments and attacks. IAFs can be specified by, e.g., making uncertain arguments or attacks certain; the justification status of arguments in an IAF is determined on the basis of the certain arguments and attacks. An argument is stable if its justification status is the same in all specifications of the IAF. For arguments that are not stable in an IAF, the relevance problem is of interest: which uncertain arguments or attacks should be investigated for the argument to become stable? We redefine stability and define relevance for IAFs and study their complexity

Tsunami Evacuation Plan for the Hatfield Marine Science Center - Newport, OR

This report outlines the current evacuation plan for Oregon State University's Hatfield Marine Science Center and makes recommendations for enhancements and changes. The physical setting is described along with detail on evacuation strategies. Recommendations include reducing personnel working on the South Beach Peninsula, improving current evacuation point, improving communication and investing in cooperation. Vertical evacuation and warning systems were also examined, and not recommended at this time

Justifications derived from inconsistent case bases using authoritativeness

Post hoc analyses are used to provide interpretable explanations for machine learning predictions made by an opaque model. We modify a top-level model (AF-CBA) that uses case-based argumentation as such a post hoc analysis. AF-CBA justifies model predictions on the basis of an argument graph constructed using precedents from a case base. The effectiveness of this approach is limited when faced with an inconsistent case base, which are frequently encountered in practice. Reducing an inconsistent case base to a consistent subset is possible but undesirable. By altering the approach’s definition of best precedent to include an additional criterion based on an expression of authoritativeness, we allow AF-CBA to handle inconsistent case bases. We experiment with four different expressions of authoritativeness using three different data sets in order to evaluate their effect on the explanations generated in terms of the average number of precedents and the number of inconsistent a fortiori forcing relations

Justification in Case-Based Reasoning

The explanation and justification of decisions is an important subject in contemporary data-driven automated methods. Case-based argumentation has been proposed as the formal background for the explanation of data-driven automated decision making. In particular, a method was developed in recent work based on the theory of precedential constraint which reasons from a case base, given by the training data of the machine learning system, to produce a justification for the outcome of a focus case. An important role is played in this method by the notions of citability and compensation, and in the present work we develop these in more detail. Special attention is paid to the notion of compensation; we formally specify the notion and identify several of its desirable properties. These considerations reveal a refined formal perspective on the explanation method as an extension of the theory of precedential constraint with a formal notion of justification

Arguing about the existence of conflicts

In this paper we formalise a meta-argumentation framework as an ASPIC+ extension which enables reasoning about conflicts between formulae of the argumentation language. The result is a standard abstract argumentation framework that can be evaluated via grounded semantics

Explainable Logic-Based Argumentation

Explainable artificial intelligence (XAI) has gained increasing interest in recent years in the argumentation community. In this paper we consider this topic in the context of logic-based argumentation, showing that the latter is a particularly promising paradigm for facilitating explainable AI. In particular, we provide two representations of abductive reasoning by sequent-based argumentation frameworks and show that such frameworks successfully cope with related challenges, such as the handling of synonyms, justifications, and logical equivalences

Hybrid and Intelligent Energy Storage Systems in Standalone Photovoltaic Applications.

Remote systems such as communication relays or irrigation control installations cannot usually be powered by the electrical grid. One of the alternatives is to power these systems through solar panels, in what is known as standalone photovoltaic applications.Most of these systems need a continuous operation, but a standalone photovoltaic installation cannot be powered during the night. For this reason, they use batteries to store excess energy during the day. These storage systems have been traditionally based on Valve Regulated Lead Acid (VRLA) batteries, but some effects can alter their performance in terms of reliability, operation cost and maintenance. One of the key issues that alter the energy behavior of the photovoltaic off-grid systems is the Partial State of Charge (PSoC) effect: Batteries cannot be completely charged as manufacturers indicate due to the day-night cycle. This gets the battery into an intermediate state of charge that effectively reduces its capacity, even halving it in some cases. To mitigate the impact of these effects on the installation, batteries tend to be oversized with some security margins. These oversizing factors can be incredibly high and have a huge impact on the deployment and maintenance cost of the facility.The first part of this thesis highlights some of these key concepts, analyzing which of them are critical in specific design cases, modeling them into a simulation tool, and as an outcome, establishing optimal sizing regions for the installations. After the analysis, different ways of improving the performance of the installations are proposed. One idea to mitigate PSoC is to combine different storage technologies in a Hybrid Energy Storage Systems (HESS). HESSs have traditionally combined high energy density elements as batteries with high power density elements as ultracapacitors. An iteration of this idea is carried out throughout this thesis, where different types of batteries are combined. Each of them is best fitted to different power patterns in the application, such as daily cycles or emergency periods. It is possible to further increase the performance by using intelligent algorithms to improve the functionalities of the Battery Management Systems embedded in these applications. To this end, failure prediction and health estimation algorithms are proposed as contributions of this work. These new algorithms endow the HESS with tools to predict possible energy disruption events and to anticipate aging, and thus, act accordingly.<br /