Burden of Persuasion in Argumentation

This paper provides a formal model for the burden of persuasion in dialogues, and in particular, in legal proceedings. The model shows how an allocation of the burden of persuasion may induce single outcomes in dialectical contexts in which, without such an allocation, the status of conflicting arguments would remain undecided. Our approach is based on a two-stage labelling. The first-stage labelling determines what arguments are accepted, rejected or undecided, regardless of the allocation of burden. The second-stage labelling revises the dialectical status of first-stage undecided arguments, according to burdens of persuasion. The labelling is finally extended in such a way as to obtain a complete labelling. Our model combines two ideas that have emerged in the debate on the burden of persuasion: the idea that the burden of persuasion determines the solution of conflicts between arguments, and the idea that its satisfaction depends on the dialectical status of the arguments concerned. Our approach also addresses inversions of the burden of persuasion, namely, cases in which the burden of persuasion over an argument does not extend to its subarguments.Comment: In Proceedings ICLP 2020, arXiv:2009.0915

Lazy Stream Manipulation in Prolog via Backtracking: The Case of 2P-Kt

The ability to lazily manipulate long or infinite streams of data is an essential feature in the era of data-driven artificial intelligence. Yet, logic programming technologies currently fall short when it comes to handling long or infinite streams of data. In this paper, we discuss how Prolog can be reinterpreted as a stream processing tool, and re-designed around an abstract state-machine capable of lazily manipulating streams of data via backtracking

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

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

Micro-intelligence for the IoT: logic-based models and technologies

Computing is moving towards pervasive, ubiquitous environments in which devices, software agents and services are all expected to seamlessly integrate and cooperate in support of human objectives. An important next step for pervasive computing is the integration of intelligent agents that employ knowledge and reasoning to understand the local context and share this information in support of intelligent applications and interfaces. Such scenarios, characterised by "computation everywhere around us", require on the one hand software components with intelligent behaviour in terms of objectives and context, and on the other their integration so as to produce social intelligence. Logic Programming (LP) has been recognised as a natural paradigm for addressing the needs of distributed intelligence. Yet, the development of novel architectures, in particular in the context Internet of Things (IoT), and the emergence of new domains and potential applications, are creating new research opportunities where LP could be exploited, when suitably coupled with agent technologies and methods so that it can fully develop its potential in the new context. In particular, the LP and its extensions can act as micro-intelligence sources for the IoT world, both at the individual and the social level, provided that they are reconsidered in a renewed architectural vision. Such micro-intelligence sources could deal with the local knowledge of the devices taking into account the domain specificity of each environment. The goal of this thesis is to re-contextualise LP and its extensions in these new domains as a source of micro-intelligence for the IoT world, envisioning a large number of small computational units distributed and situated in the environment, thus promoting the local exploitation of symbolic languages with inference capabilities. The topic is explored in depth and the effectiveness of novel LP models and architectures -and of the corresponding technology- expressing the concept of micro-intelligence is tested

Labelled variables in logic programming: Foundations

none4noWe define a new notion of truth for logic programs extended with labelled variables, interpreted in non-Herbrand domains. There, usual terms maintain their Herbrand interpretations, whereas diverse domain-specific computational models depending on the local situation of the computing device can be expressed via suitably-tailored labelled models. After some introductory examples, we define the theoretical model for labelled variables in logic programming (LVLP). Then, we present both the fixpoint and the operational semantics, and discuss their correctness and completeness, as well as their equivalence.openRoberta Calegari; Enrico Denti; Agostino Dovier; Andrea OmiciniRoberta Calegari; Enrico Denti; Agostino Dovier; Andrea Omicin

A Mechanism for Reasoning over Defeasible Preferences in Arg2P

This paper introduces argumentation over defeasible preferences in Arg2P, an argumentation framework based on logic programming. A computational mechanism is first implemented in Arg2P according to Dung’s defeasible preference model, then generalised to enable arbitrary preference relations over arguments

Neuro-symbolic Computation for XAI: Towards a Unified Model

The idea of integrating symbolic and sub-symbolic approaches to make intelligent systems (IS) understandable and explainable is at the core of new fields such as neuro-symbolic computing (NSC). This work lays under the umbrella of NSC, and aims at a twofold objective. First, we present a set of guidelines aimed at building explainable IS, which leverage on logic induction and constraints to integrate symbolic and sub-symbolic approaches. Then, we reify the proposed guidelines into a case study to show their effectiveness and potential, presenting a prototype built on the top of some NSC technologies

Logic Programming as a Service (LPaaS): Intelligence for the IoT

The widespread diffusion of low-cost computing devices, such as Arduino boards and Raspberry Pi, along with improvements of Cloud computing platforms, are paving the way towards a whole new set of opportunities for Internet of Things (IoT) applications and services. Varying degrees of intelligence are often required for supporting adaptation and self-management—yet, they should be provided in a light-weight, easy to use and customise, highly-interoperable way. Accordingly, in this paper we explore the idea of Logic Programming as a Service (LPaaS) as a novel and promising re-interpretation of distributed logic programming in the IoT era. After introducing the reference context and motivating scenarios of LPaaS as a key enabling technology for intelligent IoT, we define the LPaaS general system architecture. Then, we present a prototype implementation built on top of the tuProlog system, which provides the required interoperability and customisation. We showcase the LPaaS potential through a case study designed as a simplification of the motivating scenarios

Burden of persuasion in argumentation: A meta-argumentation approach

This paper examines the view of the burden of persuasion as meta argument and elaborates the meta-argumentative aspects of a burden-of-persuasion semantics in argumentation. An argumentation framework composed of a meta level (dealing with the burden) and an object level (dealing with standard arguments) is proposed and discussed, and its equivalence with the burden-of-persuasion model in argumentation is proved. Finally, a computationally-feasible implementation of the meta-argumentation approach is presented