An Infrastructure for Argumentative Agents

Multiagent systems are suitable for providing a framework that allows agents to perform collaborative processes in a social context. Furthermore, argumentation is a natural way of reaching agreements between several parties. However, it is difficult to find infrastructures of argumentation offering support for agent societies and their social context. Offering support for agent societies allows representation of more realistic environments to have argumentation dialogues. We propose an infrastructure to develop and execute argumentative agents in an open multiagent system. It offers tools to develop agents with argumentation capabilities. It also offers support for agent societies and their social context. The infrastructure is publicly available. Also, it has been implemented in an application scenario where argumentative agents try to reach an agreement about the best solution to solve a problem reported to the system.     

Hybrid job offer recommender system in a social network

Recommender systems (RSs) play a very important role in web navigation, ensuring that the users easily find the information they are looking for. Today's social networks contain a large amount of information and it is necessary that they employ a mechanism that will guide users to the information they are interested in. However, to be able to recommend content according to user preferences, it is necessary to analyse their profiles and determine their preferences. The present work proposes a job offer RS for a career‐oriented social network. The recommendation system is a hybrid, it consists of a case‐based reasoning (CBR) system and an argumentation framework, based on a multi‐agent system (MAS) architecture. The CBR system uses a series of metrics and similar cases to decide whether a job offer is likely to be recommended to a user. Besides, the argumentation framework extends the system with an argumentation CBR, through which old and similar cases can be obtained from the CBR system. Finally, a discussion process is established amongst the agents who debate using their experience from past cases to take a final decision.     

Research opportunities for argumentation in social networks

Nowadays, many websites allow social networking between their users in an explicit or implicit way. In this work, we show how argumentation schemes theory can provide a valuable help to formalize and structure on-line discussions and user opinions in decision support and business oriented websites that held social networks between their users. Two real case studies are studied and analysed. Then, guidelines to enhance social decision support and recommendations with argumentation are provided.     

Extending abstract argumentation systems theory

In this paper, we extend the theory of abstract argumentation systems proposed by Vreeswijk (1997). This framework stands at a high abstraction level and provides a general model for argumentation activity. However, the theory reveals an inherent limitation in that the premises of the argumentation process are assumed to be indefeasible, and this introduces the need of an implicit constraint on the strength of the arguments, in order to preserve correctness. In many application contexts the information available to start reasoning is not guaranteed to be completely reliable, therefore it is natural to assume that premises can be discarded during the argumentation process. We extend the theory by admitting that premises can be defeated and relaxing the implicit assumption about their strength.Besides fixing the technical problems related to this hidden assumption (e.g., ensuring that warranted arguments are compatible), our proposal provides an integrated model for belief revision and defeasible reasoning, confirming the suitability of argumentation as a general model for the activity of intelligent reasoning in presence of various kinds of uncertainty.     

Performance evaluation of an online argumentation learning assistance agent

Recent research indicated that students’ ability to construct evidence-based explanations in classrooms through scientific inquiry is critical to successful science education. Structured argumentation support environments have been built and used in scientific discourse in the literature. To the best of our knowledge, no research work in the literature addressed the issue of automatically assessing the student’s argumentation quality, and the teaching load of the teacher that used the online argumentation support environments is not alleviated. In this work, an intelligent argumentation assessment system based on machine learning techniques for computer supported cooperative learning is proposed. Learners’ arguments on discussion board were examined by using argumentation element sequence to detect whether the learners address the expected discussion issues and to determine the argumentation skill level achieved by the learner. Learners are first assigned to heterogeneous groups based on their responses to the learning styles questionnaire given right before the beginning of learning activities on the e-learning platform. A feedback rule construction mechanism is used to issue feedback messages to the learners in case the argumentation assessment system detects that the learners go in a biased direction. The Moodle, an open source software e-learning platform, was used to establish the cooperative learning environment for this study. The experimental results exhibit that the proposed work is effective in classifying and improving student’s argumentation level and assisting the students in learning the core concepts taught at a natural science course on the elementary school level.     

基于论证关系判别的议论文句子排序研究

议论文自动生成是自然语言生成中一项极具挑战性的任务,与诗歌、故事等生成任务不同,所生成的文章需要句子语义明确、论证结构清晰并合理地表达出核心论点。上述特点使得现有的预训练模型难以准确地建模并自动生成,因此传统的检索式方法成为解决该问题的主要方式。但前人方法在句子检索和排序过程中只考虑了语义相关度,忽视了对逻辑论证关系的判别,导致语义不连贯、论证逻辑倒置等问题。针对上述问题,该文将自然语言推理应用于论证关系逻辑判别任务,提出了基于显式语义结构的论证关系逻辑判别方法,新模型在论证判别数据集上取得优于以往自然语言推理模型的效果。同时将论文判别结果作为显式特征应用于议论文句子排序模型,在议论文生成数据集中有效改善了排序模型的逻辑不一致问题并进一步提升了议论文生成系统的总体性能。     

Dialogue games that agents play within a society

Human societies have long used the capability of argumentation and dialogue to overcome and resolve conflicts that may arise within their communities. Today, there is an increasing level of interest in the application of such dialogue games within artificial agent societies. In particular, within the field of multi-agent systems, this theory of argumentation and dialogue games has become instrumental in designing rich interaction protocols and in providing agents with a means to manage and resolve conflicts. However, to date, much of the existing literature focuses on formulating theoretically sound and complete models for multi-agent systems. Nonetheless, in so doing, it has tended to overlook the computational implications of applying such models in agent societies, especially ones with complex social structures. Furthermore, the systemic impact of using argumentation in multi-agent societies and its interplay with other forms of social influences (such as those that emanate from the roles and relationships of a society) within such contexts has also received comparatively little attention. To this end, this paper presents a significant step towards bridging these gaps for one of the most important dialogue game types; namely argumentation-based negotiation (ABN). The contributions are three fold. First, we present a both theoretically grounded and computationally tractable ABN framework that allows agents to argue, negotiate, and resolve conflicts relating to their social influences within a multi-agent society. In particular, the model encapsulates four fundamental elements: (i) a scheme that captures the stereotypical pattern of reasoning about rights and obligations in an agent society, (ii) a mechanism to use this scheme to systematically identify social arguments to use in such contexts, (iii) a language and a protocol to govern the agent interactions, and (iv) a set of decision functions to enable agents to participate in such dialogues. Second, we use this framework to devise a series of concrete algorithms that give agents a set of ABN strategies to argue and resolve conflicts in a multi-agent task allocation scenario. In so doing, we exemplify the versatility of our framework and its ability to facilitate complex argumentation dialogues within artificial agent societies. Finally, we carry out a series of experiments to identify how and when argumentation can be useful for agent societies. In particular, our results show: a clear inverse correlation between the benefit of arguing and the resources available within the context; that when agents operate with imperfect knowledge, an arguing approach allows them to perform more effectively than a non-arguing one; that arguing earlier in an ABN interaction presents a more efficient method than arguing later in the interaction; and that allowing agents to negotiate their social influences presents both an effective and an efficient method that enhances their performance within a society.     

Computing ideal sceptical argumentation

We present two dialectic procedures for the sceptical ideal semantics for argumentation. The first procedure is defined in terms of dispute trees, for abstract argumentation frameworks. The second procedure is defined in dialectical terms, for assumption-based argumentation frameworks. The procedures are adapted from (variants of) corresponding procedures for computing the credulous admissible semantics for assumption-based argumentation, proposed in [P.M. Dung, R.A. Kowalski, F. Toni, Dialectic proof procedures for assumption-based, admissible argumentation, Artificial Intelligence 170 (2006) 114–159]. We prove that the first procedure is sound and complete, and the second procedure is sound in general and complete for a special but natural class of assumption-based argumentation frameworks, that we refer to as p-acyclic. We also prove that in the case of p-acyclic assumption-based argumentation frameworks (a variant of) the procedure of [P.M. Dung, R.A. Kowalski, F. Toni, Dialectic proof procedures for assumption-based, admissible argumentation, Artificial Intelligence 170 (2006) 114–159] for the admissible semantics is complete. Finally, we present a variant of the procedure of [P.M. Dung, R.A. Kowalski, F. Toni, Dialectic proof procedures for assumption-based, admissible argumentation, Artificial Intelligence 170 (2006) 114–159] that is sound for the sceptical grounded semantics.     

一种新的辩论推理模式及其应用

文中定义了一个新的辩论推理模式,建立了一个形式化的知识表示框架,并把它应用于研究扩展逻辑程序类的说明语义,结果表明,新语义克服了择优语义的不足。作者还根据上述研究结果实现了逻辑程序设计风格下的知识框架。     

Argumentation mining

Argumentation mining aims to automatically detect, classify and structure argumentation in text. Therefore, argumentation mining is an important part of a complete argumentation analyisis, i.e. understanding the content of serial arguments, their linguistic structure, the relationship between the preceding and following arguments, recognizing the underlying conceptual beliefs, and understanding within the comprehensive coherence of the specific topic. We present different methods to aid argumentation mining, starting with plain argumentation detection and moving forward to a more structural analysis of the detected argumentation. Different state-of-the-art techniques on machine learning and context free grammars are applied to solve the challenges of argumentation mining. We also highlight fundamental questions found during our research and analyse different issues for future research on argumentation mining.     

Computer decision-support systems for public argumentation: assessing deliberative legitimacy

Recent proposals for computer-assisted argumentation have drawn on dialectical models of argumentation. When used to assist public policy planning, such systems also raise questions of political legitimacy. Drawing on deliberative democratic theory, we elaborate normative criteria for deliberative legitimacy and illustrate their use for assessing two argumentation systems. Full assessment of such systems requires experiments in which system designers draw on expertise from the social sciences and enter into the policy deliberation itself at the level of participants.

Towards a Formal and Implemented Model of Argumentation Schemes in Agent Communication

Argumentation schemes are patterns of non-deductive reasoning that have been the focus of extended study in argumentation theory. They have also been identified in computational domains including multi-agent systems as holding the potential for significant improvements in reasoning and communication abilities. By focusing on models of natural language argumentation schemes, and then building formal systems from them, direct implementation in multi-agent environments becomes a possibility. The formal, representational and implementational details are presented here, along with results that demonstrate not only advantages of flexibility, scope, and knowledge sharing, but also of computational efficiency.     

On Construction and Evaluation of Analogical Arguments for Persuasive Reasoning

ABSTRACT

Analogical reasoning is a complex process based on a comparison between two pairs of concepts or states of affairs (aka. the source and the target) for characterizing certain features from one to another. Arguments which employ this process to support their claims are called analogical arguments. Our goals are to study the structure and the computation for their defeasibility in light of the argumentation theory. Our proposed assumption-based argumentation with predicate similarity ABA^(p) framework can be seen as an extension of assumption-based argumentation framework (ABA), in which not only assumptions can be used but also similarity of predicates is used to support a claim. ABA ^(p) labels each argument tree with an analogical degree and different ways to aggregate numerical values are studied toward gullible/skeptical characteristics in agent reasoning. The acceptability of analogical arguments is evaluated w.r.t. the semantics of abstract argumentation. Finally, we demonstrate that ABA ^(p) captures the argumentation scheme for argument from analogy and provides an explanation when it is used for persuasion.     

Computational dialectic and rhetorical invention

This paper has three dimensions, historical, theoretical and social. The historical dimension is to show how the Ciceronian system of dialectical argumentation served as a precursor to computational models of argumentation schemes such as Araucaria and Carneades. The theoretical dimension is to show concretely how these argumentation schemes reveal the interdependency of rhetoric and logic, and so the interdependency of the normative with the empirical. It does this by identifying points of disagreement in a dialectical format through using argumentation schemes and critical questions. The social dimension is to show how the Ciceronian dialectical viewpoint integrates with the use of computational tools that can be used to support the principle of reason-based deliberation and facilitate deliberative democracy.     

Where is the evidence? A meta-analysis on the role of argumentation for the acquisition of domain-specific knowledge in computer-supported collaborative learning

This meta-analysis investigated the role of the quality of argumentation for domain-specific knowledge gains in computer-supported collaborative learning settings. Given the scarcity of primary studies that report correlations between these two variables, a meta-regression approach was employed that uses interventions' effects on argumentation to predict their effects on domain-specific knowledge. Effect sizes for 17 comparisons extracted from 12 studies were included in the analysis using a random-effects model. Moderator analyses concerning type of argumentation measure, type of knowledge test, and type of intervention were conducted. The interventions had a statistically significant small to moderate mean effect (d = 0.39) on argumentation, which varied as a function of the type of argumentation measure employed. The mean effect of the interventions on domain-specific knowledge (d = 0.22) appeared to be non-existent (d = 0.00) on the basis of the whole sample of studies, and small at best after the exclusion of three outlying effect sizes from one study. With respect to the relation of the studies' effects on argumentation to their effects on domain-specific knowledge, no unequivocal picture emerges: After the exclusion of the three outliers, the regression coefficient for predicting the studies' effects on domain-specific knowledge on the basis of their effects on argumentation was b = −0.08 and statistically not significant. These findings constitute a challenge for the broadly shared theoretical assumption that argumentation mediates the effects of interventions on domain-specific knowledge. A set of recommendations for strengthening future research on the topic is presented.     

分级论辩系统的逻辑研究

近年来,形式论证已逐渐成为人工智能领域的研究热点之一。自Dung于1995年提出抽象辩论框架起,学术界普遍认为论辩的核心任务是在各种基于外延的语义下对论点集进行评估,以确定其辩护状态。分级论辩系统(Graded Argumentation System,GAS)是对经典Dung型论辩系统(Dung-style Argumentation System,DAS)的推广,通过一般化DAS语义的两个核心性质,即无冲突性和可接受性,来提供更细化的论点状态概念。当前的论辩系统语义等效性研究主要集中在框架和论点层次上,可为其结构约简提供有力的保证。针对两个不同分级论辩系统中论点的语义等效问题,首先运用分级模态逻辑(Graded Modal Logic,GML)形式化分级论辩系统的片段,然后建立并证明了分级论辩系统基于外延的语义和GML公式之间的一一对应关系,最后定义分级互模拟关系并证明其蕴含分级论辩系统的4个重要的语义等价性。