Probabilistic epistemic argumentation allows for reasoning about argumentation problems in a way that is well founded by probability theory. Epistemic states are represented by probability functions over possible worlds and can be adjusted to new beliefs using update operators. While the use of probability functions puts this approach on a solid foundational basis, it also causes computational challenges as the amount of data to process depends exponentially on the number of arguments. This leads to bottlenecks in applications such as modelling opponent's beliefs for persuasion dialogues. We show how update operators over probability functions can be related to update operators over much more compact representations that allow polynomial-time updates. We discuss the cognitive and probabilistic-logical plausibility of this approach and demonstrate its applicability in computational persuasion.
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. Combining computational models of argumentation with probability theory has recently gained increasing attention, in particular with respect to abstract argumentation frameworks. Approaches following this idea can be categorised into the constellations and the epistemic approach. While the former considers probability functions on the subgraphs of abstract argumentation frameworks, the latter uses probability theory to represent degrees of belief in arguments, given a fixed framework. In this paper, we investigate the case where probability functions are given on the extensions of abstract argumentation frameworks. This generalises classical semantics in a straightforward fashion and we show that our approach also complies with many postulates for epistemic probabilistic argumentation. Although Dung's frameworks are widely approved tools for abstract argumentation, their abstractness makes expressing notions such as support or uncertainty very difficult. Thus, many of their generalizations were created, including the probabilistic argumentation frameworks (PrAFs) and the abstract dialectical frameworks (ADFs). While the first allow modeling uncertain arguments and attacks, the latter can handle various dependencies between arguments. Although the actual probability layer in PrAFs is independent of the chosen semantics, new relations pose new challenges and new interpretations of what is the probability of a relation. Thus, the methodology for handling uncertainties cannot be shifted to more general structures without any further thought. In this paper we show how ADFs are extended with probabilities. An argument graph is a graph where each node denotes an argument, and each arc denotes an attack by one argument on another. It offers a valuable starting point for theoretical analysis of argumentation following the proposals by Dung. However, the definition of an argument graph does not take into account the belief in the attacks. In particular, when constructing an argument graph from informal arguments, where each argument is described in free text, it is often evident that there is uncertainty about whether some of the attacks hold. This might be because there is some expressed doubt that an attack holds or because there is some imprecision in the language used in the arguments. In this paper, we use the set of spanning subgraphs of an argument graph as a sample space. A spanning subgraph contains all the arguments, and a subset of the attacks, of the argument graph. We assign a probability value to each spanning subgraph such that the sum of the assignments is 1. This means we can reflect the uncertainty over which is the actual subgraph using this probability distribution. Using the probability distribution over subgraphs, we can then determine the probability that a set of arguments is admissible or an extension. We can also obtain the probability of an attack relationship in the original argument graph as a marginal distribution (i.e. it is the sum of the probability assigned to each subgraph containing that attack relationship). We investigate some of the features of this proposal, and we consider the utility of our framework for capturing some practical argumentation scenarios. In this paper we present a generalization of the ASPIC ar- gumentation system, where a rule can be associated with a probability. This probability expresses uncertainty about whether or not the partic- ular rule is active. We call this system p-ASPIC. The uncertainty about rules carries over to uncertainty about whether or not a particular argu- ment is active. We generalize the usual Dung-style abstract argumenta- tion semantics, in order to deal with this uncertainty. Our work should be understood as an initial step in the direction of probabilistic, instan- tiated, Dung-style argumentation, and we discuss a number of directions for future work.  Abstract argumentation is nowadays a vivid field within artificial intelligence and has seen different developments recently. In particular, enrichments of the standard Dung frameworks have been proposed in order to model scenarios where probabilities or uncertain information have to be expressed. As for standard approaches of abstract argumentation, a uniform logical formalization for such frameworks is of great help in order to understand and compare different approaches. In this paper, we take a first step in this direction and characterize different semantics from the approach of in terms of probabilistic logic. This not only provides a uniform logical formalization but also might pave the way for future implementations.   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. We propose an argumentation framework for modelling jury-based dispute resolution where the dispute parties present their arguments before a judge and a jury. While the judge as the arbiter of law determines the legal permissibility of the presented arguments the jurors as triers of facts determine their probable weights. Such a framework is based on two key components: classical argumentation frameworks containing legally permissible arguments and probabilistic spaces assigning probable weights to arguments. A juror's probability space is represented by a set of possible worlds coupled with a probabilistic measure computed by assumption-based argumentation framework using grounded semantics.