Argument Graph Research Articles

Contribution functions for quantitative bipolar argumentation graphs: A principle-based analysis

We present a principle-based analysis of contribution functions for quantitative bipolar argumentation graphs that quantify the contribution of one argument to another. The introduced principles formalise the intuitions underlying different contribution functions as well as expectations one would have regarding the behaviour of contribution functions in general. As none of the covered contribution functions satisfies all principles, our analysis can serve as a tool that enables the selection of the most suitable function based on the requirements of a given use case.

Weighted knowledge bases with typicality and defeasible reasoning in a gradual argumentation semantics

Weighted knowledge bases for description logics with typicality provide a logical interpretation of MultiLayer Perceptrons, based on a “concept-wise” multi-preferential semantics. On the one hand, in the finitely many-valued case, Answer Set Programming (ASP) has been shown to be suitable for addressing defeasible reasoning from weighted knowledge bases for the boolean fragment of ALC . On the other hand, the semantics of weighted knowledge bases with typicality, in their different variants, have suggested some new gradual argumentation semantics, as well as an approach for defeasible reasoning over a weighted argumentation graph, building on the gradual semantics and, specifically on the φ-coherent semantics. In this paper, we explore the relationships between weighted knowledge bases and weighted argumentation graphs, to develop proof methods for defeasible reasoning over an argumentation graph under the φ-coherent semantics, in the finitely-valued case. We establish a mapping from a weighted argumentation graph to a weighted knowledge base as well as a lower bound on the complexity of the problem of verifying graded implications over an argumentation graph in the φ-coherent semantics. We also consider a mapping from weighted knowledge bases to weighted argumentation graphs, and provide an ASP implementation and some experimental results.

Counting Complexity for Reasoning in Abstract Argumentation

In this paper, we consider counting and projected model counting of extensions in abstract argumentation for various semantics, including credulous reasoning. When asking for projected counts, we are interested in counting the number of extensions of a given argumentation framework, while multiple extensions that are identical when restricted to the projected arguments count as only one projected extension. We establish classical complexity results and parameterized complexity results when the problems are parameterized by the treewidth of the undirected argumentation graph. To obtain upper bounds for counting projected extensions, we introduce novel algorithms that exploit small treewidth of the undirected argumentation graph of the input instance by dynamic programming. Our algorithms run in double or triple exponential time in the treewidth, depending on the semantics under consideration. Finally, we establish lower bounds of bounded treewidth algorithms for counting extensions and projected extension under the exponential time hypothesis (ETH).

Development of an intelligent assistant for selection of goods in the process of dialogue with the user

This article is devoted to the development of methods for creating intelligent assistants. Intelligent assistants can be used in call centers to solve customer problems, to solve technical support tasks, to help people with disabilities, to help in choosing goods, etc. We consider intelligent assistants that engage in argumentative dialogue with users, aimed at finding goods and services that maximally satisfy users’ wants and needs. The development of the intelligent assistant is based on a four-level model of the subject domain and a semantic model of the user. The system under development automates the process of search and decision justification through the reuse of domain cases: accumulated knowledge about previous dialogues with users. This gives the system we developed an advantage over existing analogues, which are incapable of reusing knowledge about previous dialogues. The paper develops a case-based approach to building an intelligent system capable of reasoning about its responses. For this purpose, an argumentation graph is constructed, methods for structuring domain cases are developed, and ontological homomorphisms are used to transform the available domain cases into a finished solution. A description of model-theoretical methods for constructing intelligent assistants is presented. The cases of goods, users and dialogues of an intelligent assistant with users are formally described in the form of partial models. The transformation of domain cases and similarity of cases are formalized using ontological homomorphisms of partial models. The purpose of the developed dialogue system is not only to select a solution according to the user’s request, but also to find out the tasks that the user is going to solve, to analyze his argumentation, and then to justify the proposed solution to the user, to show that this particular product or service will be able to meet his needs.

Argument structure in explaining wave-particle duality of photons in double-slit experiment

We analyze here how pre-service teachers explicate their views about the wave-particle duality of photons and what role it plays in their arguments supporting the quantum nature of light. The data for the analysis is provided by 12 written reports about the double-slit experiment with feeble light. The analysis is based on constructing semantic networks corresponding to pre-service teachers’ written texts. Contingency-like associative correlation between word-pairs is used to differentiate between word-pairs, where associations of two terms or words is systematic. Such associations indicate connections, which are significant for key term vocabularies in construction of inferences and arguments. Based on that information of the key vocabulary we then construct the structure of pre-service teachers’ argument for the nature of the photon and its wave-particle duality, in the form of directed argument graphs (DAGs). The results show that argument structures in four to six out of 12 cases meet the goals set for pre-service teacher education. In these cases, experimental aspects and wave-particle duality play an important role in the pre-service teachers’ argument and its structure.

Analyzing Disagreements in Argumentation Annotation of Scientific Texts in Russian Language

This paper presents the analysis of inter-annotator disagreements in modeling argumentation in scientific papers. The aim of the study is to specify annotation guidelines for the typical disagreement cases. The analysis focuses on inter-annotator disagreements at three annotation levels: theses identification, links construction between theses, specification of reasoning models for these links. The dataset contains 20 argumentation annotations for 10 scientific papers from two thematic areas, where two experts have independently annotated each text. These 20 annotations include 917 theses and 773 arguments. The annotation of each text has consisted in modelling its argumentation structure in accordance with Argument Interchange Format. The use of this model results in construction of an oriented graph with two node types (information nodes for statements, scheme nodes for links between them and reasoning models in these links) for an annotated text. Identification of reasoning models follows Walton’s classification. To identify disagreements between annotators, we perform an automatic comparison of graphs that represent an argumentation structure of the same text. This comparison includes three stages: 1) identification of theses that are present in one graph and absent in another; 2) detection of links that connect the corresponding theses between graphs in a different manner; 3) identification of different reasoning models specified for the same links. Next, an expert analysis of the automatically identified discrepancies enables specification of the typical disagreement cases based on the structural properties of argumentation graphs (positioning of theses, configuration of links across statements at different distances in the text, the ratio between the overall frequency of a reasoning model in annotations and the frequency of disagreements over its identification). The study shows that the correspondence values between argumentation graphs reach on average 78 % for theses, 55 % for links, 60 % for reasoning models. Typical disagreement cases include 1) detection of theses expressed in a text without explicit justification; 2) construction of links between theses in the same paragraph or at a distance of four and more paragraphs; 3) identification of two specific reasoning models (connected respectively to the 40 % and 33 % of disagreements); 4) confusion over functionally different schemes due to the perception of links by annotators in different aspects. The study results in formulating annotation guidelines for minimizing typical disagreement cases at each level of argumentation structures.

Linked Argumentation Graphs for Multidisciplinary Decision Support.

Multidisciplinary clinical decision-making has become increasingly important for complex diseases, such as cancers, as medicine has become very specialized. Multiagent systems (MASs) provide a suitable framework to support multidisciplinary decisions. In the past years, a number of agent-oriented approaches have been developed on the basis of argumentation models. However, very limited work has focused, thus far, on systematic support for argumentation in communication among multiple agents spanning various decision sites and holding varying beliefs. There is a need for an appropriate argumentation scheme and identification of recurring styles or patterns of multiagent argument linking to enable versatile multidisciplinary decision applications. We propose, in this paper, a method of linked argumentation graphs and three types of patterns corresponding to scenarios of agents changing the minds of others (argumentation) and their own (belief revision): the collaboration pattern, the negotiation pattern, and the persuasion pattern. This approach is demonstrated using a case study of breast cancer and lifelong recommendations, as the survival rates of diagnosed cancer patients are rising and comorbidity is the norm.

Graph Convolutional Networks for Link Prediction in Argument Structure Extraction

Argument structure extraction is a fundamental problem in argumentation mining which aims at automatically constructing arguments from unstructured textual documents. Re-garding each argument as a node, then predicting relationships of those nodes is a key task for argument structure extraction, which is called link prediction. Graph neural networks (GNN), as an efficient approach to cope with graphical data, have shown good performances on link prediction tasks. However, they usually focus on predicting whether parts of links really exist in one argument graph, rather than predicting existence of the links between any two nodes, though it is more practical in real applications of argument mining. Thus, in this paper our goal is to predict links between any two nodes given only nodes information. This task is more difficult than the traditional link prediction tasks since the prediction scale exponentially increases with node number. We propose a GNN based model for link predic-tion which outputs the embedding of all the nodes. Then, the probability of link existence between any two nodes is calculated by their embedding results. Finally, we use a dataset of essay comments to perform evaluations and the results confirm the effectiveness of our proposed method.

Neighborhood Preserving Kernels for Attributed Graphs.

We describe the design of a reproducing kernel suitable for attributed graphs, in which the similarity between two graphs is defined based on the neighborhood information of the graph nodes with the aid of a product graph formulation. Attributed graphs are those which contain a piece of vector information and a discrete label over the nodes and edges. We represent the proposed kernel as the weighted sum of two other kernels of which one is an R-convolution kernel that processes the attribute information of the graph and the other is an optimal assignment kernel that processes label information. They are formulated in such a way that the edges processed as part of the kernel computation have the same neighborhood properties and hence the kernel proposed makes a well-defined correspondence between regions processed in graphs. These concepts are also extended to the case of the shortest paths. We identified the state-of-the-art kernels that can be mapped to such a neighborhood preserving framework. We found that the kernel value of the argument graphs in each iteration of the Weisfeiler-Lehman color refinement algorithm can be obtained recursively from the product graph formulated in our method. By incorporating the proposed kernel on support vector machines we analyzed the real-world data sets and it showed superior performance in comparison with that of the other state-of-the-art graph kernels.

Explaining Semantics and Extension Membership in Abstract Argumentation

This paper explores the computation of explanations in the specific context of abstract argumentation. The explanations that we define are designed to be visual, in the sense that they take the form of subgraphs of the argumentation graph. Moreover, these explanations rely on the modular aspects of abstract argumentation semantics and can consequently be either aggregated or decomposed. We investigate graph properties of these explanations, and their adequacy to desirable explanatory criteria.

Transparent assessment of information quality of online reviews using formal argumentation theory

Review scores collect users’ opinions in a simple and intuitive manner. However, review scores are also easily manipulable, hence they are often accompanied by explanations. A substantial amount of research has been devoted to ascertaining the quality of reviews, to identify the most useful and authentic scores through explanation analysis. In this paper, we advance the state of the art in review quality analysis. We introduce a rating system to identify review arguments and to define an appropriate weighted semantics through formal argumentation theory. We introduce an algorithm to construct a corresponding graph, based on a selection of weighted arguments, their semantic distance, and the supported ratings. We also provide an algorithm to identify the model of such an argumentation graph, maximizing the overall weight of the admitted nodes and edges. We evaluate these contributions on the Amazon review dataset by McAuley et al. (2015), by comparing the results of our argumentation assessment with the upvotes received by the reviews. Also, we deepen the evaluation by crowdsourcing a multidimensional assessment of reviews and comparing it to the argumentation assessment. Lastly, we perform a user study to evaluate the explainability of our method, i.e., to test whether the automated method we use to assess reviews is understandable by humans. Our method achieves two goals: (1) it identifies reviews that are considered useful, comprehensible, and complete by online users, and does so in an unsupervised manner, and (2) it provides an explanation of quality assessments.

Linking Scholarly Contents: The Design and Construction of an Argumentation Graph

In this study, we propose a way to link the scholarly contents of scientific papers by constructing a knowledge graph based on the semantic organization of argumentation units and relations in scientific papers. We carried out an argumentation graph data model aimed at linking multiple discourses, and also developed a semantic annotation platform for scientific papers and an argumentation graph visualization system. A construction experiment was performed using 12 articles. The final argumentation graph has 1,262 nodes and 1,628 edges, including 1,628 intra-article relations and 190 inter-article relations. Knowledge evolution representation, strategic reading, and automatic abstracting use cases are presented to demonstrate the application of the argumentation graph. In contrast to existing knowledge graphs used in academic fields, the argumentation graph better supports the organization and representation of scientific paper content and can be used as data infrastructure in scientific knowledge retrieval, reorganization, reasoning, and evolution. Moreover, it supports automatic abstract and strategic reading.

Meaning of the derivative as a rate of change through a graphic argumentation. A case with Chilean students

From the socio-epistemology perspective, the teaching of the derivative is problematized due to the lack of reference frameworks that give variational meanings to the derivative and allow its construction by means of graphical arguments. The objective of this research was to analyze the construction of meanings of the derivative, as a rate of change, that Chilean students developed through a graphic argumentation in a specific situation of variation. In order to respond to the stated objective, a qualitative methodology was used, consisting of a case study, where a situation of variation was designed with the purpose of promoting the analysis of temperature changes in specific everyday life phenomena. This design was applied to ten Chilean high school students interested in studying engineering. As a result of the research, it was obtained that the participants, through a graphic argumentation, understood the slope of a straight line as a rate of change of temperature regarding time. It was concluded that this research provides elements for the construction of frames of reference that signify the derivative, as a rate of change, in situations of variation, valuing the graphic arguments and functional justifications that emerged from the students.

Averaging Solution of Differential Equations as Value for Argumentation Graph with Similarity

In this paper, we present a new method to get the value of claims and relations of an argumentation graph. By the method, the values of claims and relations are determined by the average of the solutions of the differential equation derived from the argumentation graph. We show that the overhead of obtaining multiple solutions can be reduced by parallel execution and the values obtained by the method agree with our intuition. We can get a confidence interval of each value and a rough grouping of claims and relations by the method. We show the results for toy graphs, large artificial graphs, and a natural language argumentation graph.

Ensuring reference independence and cautious monotony in abstract argumentation

In the symbolic artificial intelligence community, abstract argumentation with its semantics, i.e. approaches for defining sets of valid conclusions (extensions) that can be derived from argumentation graphs, is considered a promising method for non-monotonic reasoning. However, from a sequential perspective, abstract argumentation-based decision-making processes typically do not guarantee an alignment with common formal notions to assess consistency; in particular, abstract argumentation can, in itself, not enforce the satisfaction of relational principles such as reference independence (based on a key principle of microeconomic theory) and cautious monotony. In this paper, we address this issue by introducing different approaches to ensuring reference independence and cautious monotony in sequential argumentation: a reductionist, an expansionist, and an extension-selecting approach. The first two approaches are generically applicable, but may require comprehensive changes to the corresponding argumentation framework. In contrast, the latter approach guarantees that an extension of the corresponding argumentation framework can be selected to satisfy the relational principle by requiring that the used argumentation semantics is weakly reference independent or weakly cautiously monotonous, respectively, and also satisfies some additional straightforward principles. To highlight the relevance of the approach, we illustrate how the extension-selecting approach to reference independent argumentation can be applied to model (boundedly) rational economic decision-making.

On probabilistic argumentation and subargument-completeness

Abstract Probabilistic argumentation combines probability theory and formal models of argumentation. Given an argumentation graph where vertices are arguments and edges are attacks or supports between arguments, the approach of probabilistic labellings relies on a probability space where the sample space is any specific set of argument labellings of the graph, so that any labelling outcome can be associated with a probability value. Argument labellings can feature a label indicating that an argument is not expressed, and in previous work these labellings were constructed by exploiting the subargument-completeness postulate according to which if an argument is expressed then its subarguments are expressed and through the use of the concept of ‘subargument-complete subgraphs’. While the use of such subgraphs is interesting to compare probabilistic labellings with other works in the literature, it may also hinder the comprehension of a relatively simple framework. In this short communication, we revisit the construction of probabilistic labellings and demonstrate how labellings can be specified without reference to the concept of subargument-complete subgraphs. By doing so, the framework is simplified and yields a more natural model of argumentation.

TransforMED: End-to-Εnd Transformers for Evidence-Based Medicine and Argument Mining in medical literature

Argument Mining (AM) refers to the task of automatically identifying arguments in a text and finding their relations. In medical literature this is done by identifying Claims and Premises and classifying their relations as either Support or Attack. Evidence-Based Medicine (EBM) refers to the task of identifying all related evidence in medical literature to allow medical practitioners to make informed choices and form accurate treatment plans. This is achieved through the automatic identification of Population, Intervention, Comparator and Outcome entities (PICO) in the literature to limit the collection to only the most relevant documents. In this work, we combine EBM with AM in medical literature to increase the performance of the individual models and create high quality argument graphs, annotated with PICO entities. To that end, we introduce a state-of-the-art EBM model, used to predict the PICO entities and two novel Argument Identification and Argument Relation classification models that utilize the PICO entities to enhance their performance. Our final system works in a pipeline and is able to identify all PICO entities in a medical publication, the arguments presented in them and their relations.

Promethean Economics: The Exploration and Exploitation of the Space of Technological Possibilities

Graphical argument and game theory can elucidate, more effectively than mathematical general equilibrium theory, the Promethean economics of how market capitalist economies explore and exploit the space of technological possibilities. The first step is to understand why supply curves can slope down, which can result in the non-production of goods and services that are technologically available. The consumer goods space, for example, consists of active and potential industries, largely depending on whether there is a critical mass of demand. Invention can add goods to the consumer goods space, but goods can become newly available without invention, simply due to increased market size or intensification of and division of labor. Goods can be classified as (a) natural vs. man-made and (b) consumable vs. higher-order, yielding a typology of (a) foraging goods that are natural and consumable, (b) raw materials, (c) consumer products, and (d) intermediate goods (man-made and non-consumable). Adding roundaboutness and higher-order goods to the picture makes the technology space a graph-theoretic landscape of active and potential industries, with production functions linking nodes in the network in complex ways. Market size and/or division of labor can fuel productivity growth through activating upstream industries without fundamental invention. Effective market size depends in part on institutions, which may fail to achieve efficiency in basic product and factor markets, but also in the less well-recognized specialization problem that arises when specializing is in the social but not the individual interest, due to the way specializing damages an individual's or organization's bargaining position and terms of trade. Contracts, reputation, habit and moral virtue are among the factors that might solve prisoner's dilemma games to facilitate and trade, driving a more complete exploitation of the technology space, and raising productivity. A wide range of policy dilemmas, e.g., relating to IP and competition policy, arise at the economic frontier, which don't lend themselves to one-size-fits-all solutions, and finding the efficient policy frequently depends on understanding the technologies involved and the resulting economic possibilities and incentives. This gives rise to a valuable role for technocratic planning if it can be done well.

Introducing the Argumentation Framework Within Agent-Based Models to Better Simulate Agents' Cognition in Opinion Dynamics: Application to Vegetarian Diet Diffusion

This paper introduces a generic agent-based model simulating the exchange and the di usion of pro and con arguments. It is applied to the case of the di usion of vegetarian diets in the context of a potential emergence of a second nutrition transition. To this day, agent-based simulation has been extensively used to study opinion dynamics. However, the vast majority of existing models have been limited to extremely abstract and simplified representations of the di usion process. These simplifications impairs the realism of the simulations and disables the understanding of the reasons for the shi of an actor's opinion. The generic model presented here explicitly represents exchanges of arguments between actors in the context of an opinion dynamic model. In particular, the inner attitude towards an opinion of each agent is formalized as an argumentation graph and each agent can share arguments with other agents. Simulation experiments show that introducing attacks between arguments and a limitation of the number of arguments mobilized by agents has a strong impact on the evolution of the agents' opinion. We also highlight that when a new argument is introduced into the system, the quantity and the profile of the agents receiving the new argument will impact the evolution of the overall opinion. Finally, the application of this model to vegetarian diet adoption seems consistent with historical food behaviour dynamics observed during crises.

Two constructive proofs on d-majorization and thermo-majorization

Two constructive proofs on d-majorization and thermo-majorization are provided. In the first part, we present a diagrammatic proof of the equivalence between d-majorization and the existence of a proper stochastic matrix. We explicitly construct the desired stochastic matrix by using a graphical argument. In the second part, we present a constructive proof of the equivalence between the Gibbs-preserving map and thermal operation in classical systems. We construct the desired thermal operation and a heat bath which emulates any Gibbs-preserving map with arbitrary accuracy.