Attributing a cyber-operation through the use of multiple pieces of technical evidence (i.e., malware reverse-engineering and source tracking) and conventional intelligence sources (i.e., human or signals intelligence) is a difficult problem not only due to the effort required to obtain evidence, but the ease with which an adversary can plant false evidence. In this paper, we introduce a formal reasoning system called the InCA (Intelligent Cyber Attribution) framework that is designed to aid an analyst in the attribution of a cyber-operation even when the available information is conflicting and/or uncertain. Our approach combines argumentation-based reasoning, logic programming, and probabilistic models to not only attribute an operation but also explain to the analyst why the system reaches its conclusions.

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bases is deciding what information is no longer useful; pieces of information (such as intelligence reports) may be outdated, may come from sources that have recently been discovered to be of low quality, or abundant evidence may be available that contradicts them.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.

Argumentation is a natural meaning of reasoning in the daily life, and has also become a highly interested topic of knowledge representation in the past few years. In this paper, we will use the phrase "two-sided argumentation" for a type of formalization for our real world debate: an issue with a pro-side supports it and a con-side opposes it. Then, we will point out that, when we use the term "argumentation," we in fact mean a binary concept: a method of reasoning, and a type of negotiation. For both case, we will consider the semantics: argumentative models for the former, argumentation games for the latter. We will also give out some results about the relationship between them.

This article is devoted to the study of methods to change defeasible logic programs (de.l.p.s) which are the knowledge bases used by the Defeasible Logic Programming (DeLP) interpreter. DeLP is an argumentation formalism that allows to reason over potentially inconsistent de.l.p.s. Argument Theory Change (ATC) studies certain aspects of belief revision in order to make them suitable for abstract argumentation systems. In this article, abstract arguments are rendered concrete by using the particular rule-based defeasible logic adopted by DeLP. The objective of our proposal is to define prioritized argument revision operators \`a la ATC for de.l.p.s, in such a way that the newly inserted argument ends up undefeated after the revision, thus warranting its conclusion. In order to ensure this warrant, the de.l.p. has to be changed in concordance with a minimal change principle. To this end, we discuss different minimal change criteria that could be adopted. Finally, an algorithm is presented, implementing the argument revision operations.

Defeasible argumentation and default reasoning are usually perceived as two similar, but distinct approaches to commonsense reasoning. In this paper, we combine these two fields by viewing (defeasible resp. default) rules as a common crucial part in both areas. We will make use of possible worlds semantics from default reasoning to provide examples for arguments, and carry over the notion of plausibility to the argumentative framework. Moreover, we base a priority relation between arguments on the tolerance partitioning of system Z and obtain a criterion phrased in system Z terms that ensures warrancy in defeasible argumentation.

Defeasible Logic Programming (DELP) is a formalism that extends declarative programming to capture defeasible reasoning. Its inference mechanism, upon a query on a literal in a program, answers by indicating whether or not it is warranted in an argumentation process. While the properties of DELP are well known, some of its basic elements can be redefined in order to shed light on some of the subtleties of the warrant process. We will discuss these alternative definitions and the cases in which they provide a better performance.

This article falls within the field of abstract argumentation frameworks. In particular, we focus on the study of frameworks using a proof procedure based on dialectical trees. These trees rely on a marking procedure to determine the warrant status of their root argument. Thus, our objective is to formulate rationality postulates to characterize the marking criterion over dialectical trees. The behavior of the marking procedure is closely tied to the alteration of trees, which is the keystone of any model of change based on dialectical argumentation. Hence, the results achieved in this work will benefit research on dynamics in argumentation.

This paper discusses a process of argumentation. We propose an algorithm for dynamic treatment of argumentation in which all lines of argumentation are executed in succession, and the agent's knowledge base can change during argumentation. We show that there exists a case in which an agent dynamically loses argumentation that would be considered won by a static analysis. We also show that the algorithm terminates, and describe acceptable arguments that are obtained after the argumentation.