A Labelling Framework for Probabilistic Argumentation

The combination of argumentation and probability paves the way to new accounts of qualitative and quantitative uncertainty, thereby offering new theoretical and applicative opportunities. Due to a variety of interests, probabilistic argumentation is approached in the literature with different frameworks, pertaining to structured and abstract argumentation, and with respect to diverse types of uncertainty, in particular the uncertainty on the credibility of the premises, the uncertainty about which arguments to consider, and the uncertainty on the acceptance status of arguments or statements. Towards a general framework for probabilistic argumentation, we investigate a labelling-oriented framework encompassing a basic setting for rule-based argumentation and its (semi-) abstract account, along with diverse types of uncertainty. Our framework provides a systematic treatment of various kinds of uncertainty and of their relationships and allows us to back or question assertions from the literature

Learning policy constraints through dialogue

Publisher PD

On the Optimized Utilization of Smart Contracts in DLTs from the Perspective of Legal Representation and Legal Reasoning

Smart contracts are computer programs stored in blockchain which open a wide range of applications but also raise some important issues. When we convert traditional legal contracts written in natural language into smart contracts written in lines of code, problems will arise. Translation errors will exist in the process of conversion since the law in natural language is ambiguous and imprecise, full of conflicts, and the emergence of new evidence may influence the processing of reasoning. This research project has three purposes: the first aims at the resolution of these problems from logic and technical perspective to develop the accuracy and human-readability of smart contracts, by exploring a more novel and advanced logic-based language to represent legal contracts, and analyzing an extended argumentation framework with rich expressiveness; the second purpose is to investigate various existing technologies like Akoma Ntoso and Legal- RuleML, making the legal knowledge and reasoning machine-readable and be linked with the real world; third, to investigate the implementation of a mature multi-agent system incorporating the software agents with sensing, inferring, learning, decision-making and social abilities that can be fitted onto DLTs

Algorithms for Dynamic Argumentation Frameworks: An Incremental SAT-Based Approach

Peer reviewe

A Concurrent Language for Argumentation: Preliminary Notes

While agent-based modelling languages naturally implement concurrency, the currently available languages for argumentation do not allow to explicitly model this type of interaction. In this paper we introduce a concurrent language for handling process arguing and communicating using a shared argumentation framework (reminding shared constraint store as in concurrent constraint). We introduce also basic expansions, contraction and revision procedures as main bricks for enforcement, debate, negotiation and persuasion

Neural-Symbolic Argumentation Mining: An Argument in Favor of Deep Learning and Reasoning

Deep learning is bringing remarkable contributions to the field of argumentation mining, but the existing approaches still need to fill the gap toward performing advanced reasoning tasks. In this position paper, we posit that neural-symbolic and statistical relational learning could play a crucial role in the integration of symbolic and sub-symbolic methods to achieve this goal