Search Results for author: Beishui Liao

Found 11 papers, 0 papers with code

Defense semantics of argumentation: revisit

no code implementations20 Nov 2023 Beishui Liao, Leendert van der Torre

In this paper we introduce a novel semantics, called defense semantics, for Dung's abstract argumentation frameworks in terms of a notion of (partial) defence, which is a triple encoding that one argument is (partially) defended by another argument via attacking the attacker of the first argument.

Abstract Argumentation

A Concept and Argumentation based Interpretable Model in High Risk Domains

no code implementations17 Aug 2022 Haixiao Chi, Dawei Wang, Gaojie Cui, Feng Mao, Beishui Liao

Interpretability has become an essential topic for artificial intelligence in some high-risk domains such as healthcare, bank and security.

Vocal Bursts Intensity Prediction

Value-based Practical Reasoning: Modal Logic + Argumentation

no code implementations11 Apr 2022 Jieting Luo, Beishui Liao, Dov Gabbay

Autonomous agents are supposed to be able to finish tasks or achieve goals that are assigned by their users through performing a sequence of actions.

BTPK-based interpretable method for NER tasks based on Talmudic Public Announcement Logic

no code implementations24 Jan 2022 Yulin Chen, Beishui Liao, Bruno Bentzen, Bo Yuan, Zelai Yao, Haixiao Chi, Dov Gabbay

In this paper, we propose a novel interpretable method, BTPK (Binary Talmudic Public Announcement Logic model), to help users understand the internal recognition logic of the name entity recognition tasks based on Talmudic Public Announcement Logic.

Decision Making Logical Reasoning +5

A Formal Framework for Reasoning about Agents' Independence in Self-organizing Multi-agent Systems

no code implementations17 May 2021 Jieting Luo, Beishui Liao, John-Jules Meyer

The resulting information about agents' full contributions allows us to understand the complex link between local agent behavior and system level behavior in a self-organizing multi-agent system.

A Bayesian Approach to Direct and Inverse Abstract Argumentation Problems

no code implementations10 Sep 2019 Hiroyuki Kido, Beishui Liao

It is the inverse of the direct problem corresponding to the traditional problem of the abstract argumentation that focuses on finding sets of acceptable arguments in terms of the semantics given an attack relation between the arguments.

Abstract Argumentation

Representation, Justification and Explanation in a Value Driven Agent: An Argumentation-Based Approach

no code implementations13 Dec 2018 Beishui Liao, Michael Anderson, Susan Leigh Anderson

As a result, a VDA in a given situation is mapped onto an argumentation framework in which arguments are defined by the notion of deduction.

Decision Making Epistemic Reasoning +2

The Jiminy Advisor: Moral Agreements Among Stakeholders Based on Norms and Argumentation

no code implementations11 Dec 2018 Beishui Liao, Pere Pardo, Marija Slavkovik, Leendert van der Torre

We address the challenge of how the ethical views of such stakeholders can be integrated in the behavior of an autonomous system.

Decision Making

Prioritized Norms in Formal Argumentation

no code implementations23 Sep 2017 Beishui Liao, Nir Oren, Leendert van der Torre, Serena Villata

To resolve conflicts among norms, various nonmonotonic formalisms can be used to perform prioritized normative reasoning.

Defense semantics of argumentation: encoding reasons for accepting arguments

no code implementations30 Apr 2017 Beishui Liao, Leendert van der Torre

In this paper we show how the defense relation among abstract arguments can be used to encode the reasons for accepting arguments.

Formulating Semantics of Probabilistic Argumentation by Characterizing Subgraphs: Theory and Empirical Results

no code implementations1 Aug 2016 Beishui Liao, Kang Xu, Huaxin Huang

The results show that our approach not only dramatically decreases the time for computing p(E^\sigma), but also has an attractive property, which is contrary to that of existing approaches: the denser the edges of a PrAG are or the bigger the size of a given extension E is, the more efficient our approach computes p(E^\sigma).

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