On computing explanations in argumentation

Copyright © 2015, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.Argumentation can be viewed as a process of generating explanations. However, existing argumentation semantics are developed for identifying acceptable arguments within a set, rather than giving concrete justifications for them. In this work, we propose a new argumentation semantics, related admissibility, designed for giving explanations to arguments in both Abstract Argumentation and Assumption-based Argumentation. We identify different types of explanations defined in terms of the new semantics. We also give a correct computational counterpart for explanations using dispute forests

Morphological Complexity and Conceptualization : The Human Body

In this squib, I want to argue that the morphological structure of words is, at least to some extent, motivated. As an example I have choosen the partonomic (and for the less part taxonomic) nomenclature of the human body. While important work by Brown et alii (1973), Anderson (1978) and Schladt (1997) exists on this topic, these analyses focus on the conceptualization of body-parts and their semantics, but not on their morphological representation. In the following, I want to check two predictions about the morphological complexity of lexical items denoting parts of the human body. The first assumption is that the most canonical body-parts are always expressed by mono-lexematic items. The second one consists in the assumption that body-parts of the lowest levels in the hierarchy are always morphologically complex. A set of six body-parts has been analysed in 27 languages. The set consists of two canonical (HEAD and EAR) and of one from the lowest level of the hierarchy (TOENAIL). For this I have adopted a sample from Schladt (1997) and a small one compiled by mysel

Developments in abstract and assumption-based argumentation and their application in logic programming

Logic Programming (LP) and Argumentation are two paradigms for knowledge representation and reasoning under incomplete information. Even though the two paradigms share common features, they constitute mostly separate areas of research. In this thesis, we present novel developments in Argumentation, in particular in Assumption-Based Argumentation (ABA) and Abstract Argumentation (AA), and show how they can 1) extend the understanding of the relationship between the two paradigms and 2) provide solutions to problematic reasoning outcomes in LP. More precisely, we introduce assumption labellings as a novel way to express the semantics of ABA and prove a more straightforward relationship with LP semantics than found in previous work. Building upon these correspondence results, we apply methods for argument construction and conflict detection from ABA, and for conflict resolution from AA, to construct justifications of unexpected or unexplained LP solutions under the answer set semantics. We furthermore characterise reasons for the non-existence of stable semantics in AA and apply these findings to characterise different scenarios in which the computation of meaningful solutions in LP under the answer set semantics fails.Open Acces

A semantics for probabilistic answer set programs with incomplete stochastic knowledge

Some probabilistic answer set programs (PASP) semantics assign probabilities to sets of answer sets and implicitly assume these answer sets to be equiprobable. While this is a common choice in probability theory, it leads to unnatural behaviours with PASPs. We argue that the user should have a level of control over what assumption is used to obtain a probability distribution when the stochastic knowledge is incomplete. To this end, we introduce the Incomplete Knowledge Semantics (IKS) for probabilistic answer set programs. We take inspiration from the field of decision making under ignorance. Given a cost function, represented by a user-defined ordering over answer sets through weak constraints, we use the notion of Ordered Weighted Averaging (OWA) operator to distribute the probability over a set of answer sets accordingly to the user’s level of optimism. The more optimistic (or pessimistic) a user is, the more (or less) probability is assigned to the more optimal answer sets. We present an implementation and showcase the behaviour of this semantics on simple examples. We also highlight the impact that different OWA operators have on weight learning, showing that the equiprobability assumption is not always the best option

On the Difference between Assumption-Based Argumentation and Abstract Argumentation

Acknowledgements The first author has been supported by the National Research Fund, Luxembourg (LAAMI project) and by the Engineering and Physical Sciences Research Council (EPSRC, UK), grant ref. EP/J012084/1 (SAsSy project). The second and third authors have been supported by CNPq (Universal 2012 - Proc. no. 473110/2012-1), CAPES (PROCAD 2009) and CNPq/CAPES (Casadinho/PROCAD 2011).Peer reviewedPostprin

On the Difference between Assumption-Based Argumentation and Abstract Argumentation

Preprin

Properties of ABA+ for Non-Monotonic Reasoning

We investigate properties of ABA+, a formalism that extends the well studied structured argumentation formalism Assumption-Based Argumentation (ABA) with a preference handling mechanism. In particular, we establish desirable properties that ABA+ semantics exhibit. These pave way to the satisfaction by ABA+ of some (arguably) desirable principles of preference handling in argumentation and nonmonotonic reasoning, as well as non-monotonic inference properties of ABA+ under various semantics.Comment: This is a revised version of the paper presented at the worksho

Overgeneration in the Higher Infinite

The Overgeneration Argument is a prominent objection against the model-theoretic account of logical consequence for second-order languages. In previous work we have offered a reconstruction of this argument which locates its source in the conflict between the neutrality of second-order logic and its alleged entanglement with mathematics. Some cases of this conflict concern small large cardinals. In this article, we show that in these cases the conflict can be resolved by moving from a set-theoretic implementation of the model-theoretic account to one which uses higher-order resources