Argumentation and Logic Programming for Explainable and Ethical AI

In this paper we sketch a vision of explainability of intelligent systems as a logic approach suitable to be injected into and exploited by the system actors once integrated with sub-symbolic techniques. In particular, we show how argumentation could be combined with different extensions of logic programming – namely, abduction, inductive logic programming, and probabilistic logic programming – to address the issues of explainable AI as well as to address some ethical concerns about AI

Explainable and Ethical AI: A Perspective on Argumentation and Logic Programming

In this paper we sketch a vision of explainability of intelligent systems as a logic approach suitable to be injected into and exploited by the system actors once integrated with sub-symbolic techniques. In particular, we show how argumentation could be combined with different extensions of logic programming – namely, abduction, inductive logic programming, and probabilistic logic programming – to address the issues of explainable AI as well as some ethical concerns about AI

A Model for an Intelligent Support Decision System in Aquaculture

The paper purpose an intelligent software system agents–based to support decision in aquculture and the approach of fish diagnosis with informatics methods, techniques and solutions. A major purpose is to develop new methods and techniques for quick fish diagnosis, treatment and prophyilaxis at infectious and parasite-based known disorders, that may occur at fishes raised in high density in intensive raising systems. But, the goal of this paper is to presents a model of an intelligent agents-based diagnosis method will be developed for a support decision system.support decision system, diagnosis, multi-agent system, fish diseases

An Argumentation-Based Reasoner to Assist Digital Investigation and Attribution of Cyber-Attacks

We expect an increase in the frequency and severity of cyber-attacks that comes along with the need for efficient security countermeasures. The process of attributing a cyber-attack helps to construct efficient and targeted mitigating and preventive security measures. In this work, we propose an argumentation-based reasoner (ABR) as a proof-of-concept tool that can help a forensics analyst during the analysis of forensic evidence and the attribution process. Given the evidence collected from a cyber-attack, our reasoner can assist the analyst during the investigation process, by helping him/her to analyze the evidence and identify who performed the attack. Furthermore, it suggests to the analyst where to focus further analyses by giving hints of the missing evidence or new investigation paths to follow. ABR is the first automatic reasoner that can combine both technical and social evidence in the analysis of a cyber-attack, and that can also cope with incomplete and conflicting information. To illustrate how ABR can assist in the analysis and attribution of cyber-attacks we have used examples of cyber-attacks and their analyses as reported in publicly available reports and online literature. We do not mean to either agree or disagree with the analyses presented therein or reach attribution conclusions

Machine ethics via logic programming

Machine ethics is an interdisciplinary field of inquiry that emerges from the need of imbuing autonomous agents with the capacity of moral decision-making. While some approaches provide implementations in Logic Programming (LP) systems, they have not exploited LP-based reasoning features that appear essential for moral reasoning. This PhD thesis aims at investigating further the appropriateness of LP, notably a combination of LP-based reasoning features, including techniques available in LP systems, to machine ethics. Moral facets, as studied in moral philosophy and psychology, that are amenable to computational modeling are identified, and mapped to appropriate LP concepts for representing and reasoning about them. The main contributions of the thesis are twofold. First, novel approaches are proposed for employing tabling in contextual abduction and updating – individually and combined – plus a LP approach of counterfactual reasoning; the latter being implemented on top of the aforementioned combined abduction and updating technique with tabling. They are all important to model various issues of the aforementioned moral facets. Second, a variety of LP-based reasoning features are applied to model the identified moral facets, through moral examples taken off-the-shelf from the morality literature. These applications include: (1) Modeling moral permissibility according to the Doctrines of Double Effect (DDE) and Triple Effect (DTE), demonstrating deontological and utilitarian judgments via integrity constraints (in abduction) and preferences over abductive scenarios; (2) Modeling moral reasoning under uncertainty of actions, via abduction and probabilistic LP; (3) Modeling moral updating (that allows other – possibly overriding – moral rules to be adopted by an agent, on top of those it currently follows) via the integration of tabling in contextual abduction and updating; and (4) Modeling moral permissibility and its justification via counterfactuals, where counterfactuals are used for formulating DDE.Fundação para a Ciência e a Tecnologia (FCT)-grant SFRH/BD/72795/2010 ; CENTRIA and DI/FCT/UNL for the supplementary fundin