Probabilistic graded semantics

We propose a new graded semantics for abstract argumentation frameworks that is based on the constellations approach to probabilistic argumentation. Given an abstract argumentation framework, our approach assigns uniform probability to all arguments and then ranks arguments according to the probability of acceptance wrt. some classical semantics. Albeit relying on a simple idea this approach (1) is based on the solid theoretical foundations of probability theory, and (2) complies with many rationality postulates proposed for graded semantics. We also investigate an application of our approach for inconsistency measurement in argumentation frameworks and show that the measure induced by the probabilistic graded semantics also complies with the basic rationality postulates from that area

Probabilistic Argumentation. An Equational Approach

There is a generic way to add any new feature to a system. It involves 1) identifying the basic units which build up the system and 2) introducing the new feature to each of these basic units. In the case where the system is argumentation and the feature is probabilistic we have the following. The basic units are: a. the nature of the arguments involved; b. the membership relation in the set S of arguments; c. the attack relation; and d. the choice of extensions. Generically to add a new aspect (probabilistic, or fuzzy, or temporal, etc) to an argumentation network can be done by adding this feature to each component a-d. This is a brute-force method and may yield a non-intuitive or meaningful result. A better way is to meaningfully translate the object system into another target system which does have the aspect required and then let the target system endow the aspect on the initial system. In our case we translate argumentation into classical propositional logic and get probabilistic argumentation from the translation. Of course what we get depends on how we translate. In fact, in this paper we introduce probabilistic semantics to abstract argumentation theory based on the equational approach to argumentation networks. We then compare our semantics with existing proposals in the literature including the approaches by M. Thimm and by A. Hunter. Our methodology in general is discussed in the conclusion

A probabilistic analysis of argument cogency

This paper offers a probabilistic treatment of the conditions for argument cogency as endorsed in informal logic: acceptability, relevance, and sufficiency. Treating a natural language argument as a reason-claim-complex, our analysis identifies content features of defeasible argument on which the RSA conditions depend, namely: change in the commitment to the reason, the reason’s sensitivity and selectivity to the claim, one’s prior commitment to the claim, and the contextually determined thresholds of acceptability for reasons and for claims. Results contrast with, and may indeed serve to correct, the informal understanding and applications of the RSA criteria concerning their conceptual dependence, their function as update-thresholds, and their status as obligatory rather than permissive norms, but also show how these formal and informal normative approachs can in fact align

Belief Revision in Structured Probabilistic Argumentation

In real-world applications, knowledge bases consisting of all the information at hand for a specific domain, along with the current state of affairs, are bound to contain contradictory data coming from different sources, as well as data with varying degrees of uncertainty attached. Likewise, an important aspect of the effort associated with maintaining knowledge bases is deciding what information is no longer useful; pieces of information (such as intelligence reports) may be outdated, may come from sources that have recently been discovered to be of low quality, or abundant evidence may be available that contradicts them. In this paper, we propose a probabilistic structured argumentation framework that arises from the extension of Presumptive Defeasible Logic Programming (PreDeLP) with probabilistic models, and argue that this formalism is capable of addressing the basic issues of handling contradictory and uncertain data. Then, to address the last issue, we focus on the study of non-prioritized belief revision operations over probabilistic PreDeLP programs. We propose a set of rationality postulates -- based on well-known ones developed for classical knowledge bases -- that characterize how such operations should behave, and study a class of operators along with theoretical relationships with the proposed postulates, including a representation theorem stating the equivalence between this class and the class of operators characterized by the postulates

Probabilistic Argumentation with Epistemic Extensions and Incomplete Information

Abstract argumentation offers an appealing way of representing and evaluating arguments and counterarguments. This approach can be enhanced by a probability assignment to each argument. There are various interpretations that can be ascribed to this assignment. In this paper, we regard the assignment as denoting the belief that an agent has that an argument is justifiable, i.e., that both the premises of the argument and the derivation of the claim of the argument from its premises are valid. This leads to the notion of an epistemic extension which is the subset of the arguments in the graph that are believed to some degree (which we defined as the arguments that have a probability assignment greater than 0.5). We consider various constraints on the probability assignment. Some constraints correspond to standard notions of extensions, such as grounded or stable extensions, and some constraints give us new kinds of extensions

Preface to the Special Issue on Advances in Argumentation in Artificial Intelligence

Now at the forefront of automated reasoning, argumentation has become a key research topic within Artificial Intelligence. It involves the investigation of those activities for the production and exchange of arguments, where arguments are attempts to persuade someone of something by giving reasons for accepting a particular conclusion or claim as evident. The study of argumentation has been the focus of attention of philosophers and scholars, from Aristotle and classical rhetoric to the present day. The computational study of arguments has emerged as a field of research in AI in the last two decades, mainly fuelled by the interest from scholars in logics, non-monotonic and epistemic reasoning, and in related disciplines such as Law, Sociology and Computational Linguistics. This special issue collects a selection of five papers from the 2nd Workshop on Advances In Argumentation In Artificial Intelligence, co-located with AI*IA 2018, the 17th International Conference of the Italian Association for Artificial Intelligence held in Trento in November 2018. The workshop was organized as part of the activities of the Argumentation in Artificial Intelligence Working Group. The Argumentation Group is a working group of the Associazione Italiana per l’Intelligenza Artificiale (AI*IA) whose general goal is to promote Italian scientific activities in the field of Argumentation in Artificial Intelligence, and foster collaborations between research groups. The selected papers discuss theoretical foundations in argumentation as well as challenges and real-world problems for which argumentation may represent a viable AI-paradigm. Each submission underwent a single-blind peer-review process and the five accepted articles were reviewed by at least two independent expert reviewers. Much work in computational models of argument is centered on Dung’ seminal 1995 paper “On the acceptability of arguments and its fundamental role in nonmonotonic reasoning, logic programming and n-person games.”. On the one hand, this is reflected by the papers presented in this special issue, with four out of five papers describing works directly linked to Dung’s abstract framework or to its extensions. On the other hand, the papers also testify the variety and richness of the current state-of-the-art of argumentation studies, which extends and goes far beyond Dung’s work, proposing research combining natural language processing and probabilistic reasoning with abstract argumentation The papers by Flesca, Dondio and Longo, and Taticchi and Bistarelli are theoretical works in the area of computational argumentation. The paper by Flesca examines the problem of efficiently computing the probability of the extensions of bipolar probabilistic argumentation frameworks, proposing a set of more efficient and empirically-tested algorithms. The paper by Dondio and Longo introduces a novel abstract argumentation semantics. Inspired by the ambiguity blocking semantics of defeasible logic, the authors propose a semantics where the undecided label assigned to some arguments could be blocked instead of being propagated to attacked arguments. The paper by Taticchi and Bistarelli proposes a cooperative-game approach to share acceptability and rank arguments of an argumentation framework. The paper by Gobbo et al. proposes a new method for annotating arguments expressed in natural language, called adpositional argumentation. By doing so, they provide the guidelines for designing a gold standard corpus that could benefit studies in argumentation mining and arguments definition. The paper by Pazienza et al. proposes an interesting application of abstract argumentation to financial predictions. The authors design a framework combining natural language processing along with abstract argumentation techniques to automatically extract relevant arguments from Earning Conference Call transcripts, weight such arguments and produce a final advice aimed to anticipate and predict analysts’ recommendations. Finally, the Editors are like to acknowledge the work of the members of the Programme Committee whose invaluable expertise and efforts have led to the selection of the papers included in this special issue. Last but not least, the editors would like to thank all the authors that have contributed to this special issue