Agent defense in abstract argumentation: Semantics and principle-based analysis

In future cyber-physical systems, such as smart factories and energy grids, ontologies can serve as the enabler for semantically precise communication as well as for knowledge representation and reasoning. Multi-agent systems have shown to be a suitable software development paradigm for cyber-physical systems and may well profit from harnessing ontologies in terms of reduced engineering effort and better interoperability. This contribution presents a development methodology for ontologies that enable communication and reasoning in Multi-Agent Systems for cyber-physical systems. The methodology is unique in addressing a set of requirements specific to this application domain.

This chapter is the second chapter in Part II of the book. It is also the basis for Chapter 9, as shown in the shaded area of Fig. 7.1. This chapter first examines the relationship between case-based reasoning (CBR) systems and multiagent systems (MASs), and proposes knowledge-based models of multiagent CBR systems from both logical and knowledge-based viewpoints. Then this chapter investigates the case base and case retrieval in a distributed setting and examines the integration of case-based reasoning capabilities in a BDI architecture. This chapter also discusses CBR for agent team cooperation. Finally this chapter proposes an agent architecture using CBR to model an agent negotiation strategy.

A commonsense knowledge base supported multi-agent architecture

Overview. There is now a growing body of research on formal algorithmic models of social procedures and interactions between rational agents. These models attempt to identify logical elements in our day-to-day social activities. When interactions are modeled as games, reasoning involves analysis of agents’ long-term powers for influencing outcomes. Agents devise their respective strategies on how to interact so as to ensure maximal gain. In recent years, researchers have tried to devise logics and models in which strategies are “first class citizens”, rather than unspecified means to ensure outcomes. Yet, these cover only basic models, leaving open a range of interesting issues, e.g. communication and coordination between players, especially in games of imperfect information. Game models are also relevant in the context of system design and verification. In this article we will discuss research on logic and automata-theoretic models of games and strategic reasoning in multi-agent systems. We will get acquainted with the basic tools and techniques for this emerging area, and provide pointers to the exciting questions it offers.

Current methods to capture, analyse and present the audience participation of broadcast events are increasingly carried out using social media. Uptake of such technology tools has so far been poor amongst older adults, and it has the worrying effect of excluding the demographic from participation. Our work explores whether a common desire to interact with debates can be tapped with technology with a very low barrier to entry, to both support better engagement with broadcast debates and encourage greater use of social media. This paper describes experiments where older adults interact with a BBC radio debate programme: The Moral Maze. As a result, we obtained common interaction patterns which then are used to define recommendations for software-supported interaction with debates based on theories of argumentation. Our goal is to combine research on computational models of argument and user-driven research on human-centred computing in a project with the potential for high-profile impact in addressing older adults inclusion in the digital economy.

The theory of argumentation [52] is a rich, interdisciplinary area of research lying across philosophy, communication studies, linguistics, and psychology. Its techniques and results have found a wide range of applications in both theoretical and practical branches of artificial intelligence and computer science [9, 45]. These applications range from specifying semantics for logic programs [13], to natural language text generation [14], to supporting legal reasoning [7], to decision-support for multi-party human decision-making [22] and conflict resolution [51]. In recent years, argumentation theory has been gaining increasing interest in the multi-agent systems (MAS) research community. On one hand, argumentation-based techniques can be used to specify autonomous agent reasoning, such as belief revision and decision-making under uncertainty and non-standard preference policies. On the other hand, argumentation can also be used as a vehicle for facilitatingmulti-agent interaction, because argumentation naturally provides tools for designing, implementing and analysing sophisticated forms of interaction among rational agents. Argumentation has made solid contributions to the theory and practice of multi-agent dialogues. In this introduction to the special issue, I first briefly introduce some key notions in argumentation theory. I then outline two major applications of argumentation in MAS, namely in autonomous agent reasoning (Section 3) and multi-agent communication (Section 4). Throughout the discussion, I introduce the four papers in this special issue, which are revised and expanded versions of papers selected from the proceedings of the First International Workshop on Argumentation in Multi-Agent Systems (ArgMAS), which was held in New York during July 2004 in conjunction with the International Conference on Autonomous Agents and Multiagent Systems (AAMAS).

This paper proposes a canonical ordering of arguments within abstract argumentation labellings and two new types of efficient representations of these labellings for use in applications involving the computation of argumentation semantics. The space requirements of the representations are analysed, benchmarked on a class of hard enumeration problems taken from the International Competition on Computational Models of Argumentation (ICCMA), and compared for efficiency. We found that they both offer significant reductions of the memory representation requirements of large labellings, sometimes of up to 75%. We argue that the new way of looking at labellings provided by one of the representations, i.e., by considering repetitions of segment assignments within labellings, paves the way for investigations of new applications in argumentation theory.

Researchers have long been interested in the role that norms can play in governing agent actions in multi-agent systems. Norms have been shown to facilitate social order [2] and improve cooperation and coordination among agents [9], and an active research community has investigated many theoretical and practical aspects of normative reasoning in multi-agent systems [1]. Much of this work has focused on formalising normative concepts from human society and adapting them for the government of open software systems, and on the simulation of normative processes in human and artificial societies. However, there has been comparatively little work on applying normative MAS mechanisms to understanding the norms in human society.

This paper presents a multi-agent AI framework for legal aid, designed to support real-world case fact management through interactive dialogue. Our agent framework emulates the iterative questioning, clarification, and synthesis processes of legal professionals, not solely on the fragmented information initially provided by litigants. By engaging in multi-turn interactions, the system incrementally supplements missing details and mitigates risks of misinterpretation, thereby aligning more closely with the dynamics of real legal consultations. A key contribution of this work is the use of real-world legal aid case records as training and evaluation material, ensuring that the framework is grounded in authentic data rather than synthetic simulations. The system is implemented as a collaboration among specialized agents: (1) litigantTwins, which maintains factual integrity and guards against hallucinations; (2) legalAider, which leverages legal knowledge to generate context-sensitive follow-up questions and update case narratives; and (3) Evaluator, which compares AI-generated case records against ground truth facts, assessing factual correctness through qualitative and quantitative measures. Technically, the framework can be built on LLMs, with multi-agent system and prompt design enabling robust coordination. This architecture enhances the quality of fact construction and reasoning, while also offering a scalable solution for online public legal aid services. Beyond its technical contributions, this research highlights its public value, accessibility, and commitment to fairness in the distribution of legal resources. By integrating multi-agent AI with real-world case data, the framework addresses both the technological and socio-legal dimensions of legal information systems, advancing on legal knowledge management, deployment of conversational agents, and normative reasoning in multi-agent systems.

Multi-agent systems are increasingly deployed in complex, dynamic environments requiring sophisticated coordination, rapid decision-making, and extensive knowledge sharing. However, as the number of agents and the complexity of tasks grow, inter-agent communication and memory constraints become critical bottlenecks. This paper proposes a novel framework integrating communication compression memory management with cooperative reasoning to address these challenges. We introduce an adaptive latent-space compression algorithm that dynamically reduces communication overhead while preserving mission-critical semantic information. Furthermore, we design a hierarchical memory management system that efficiently allocates episodic and semantic memories, facilitating rapid retrieval and bounded storage requirements. Building upon this optimized infrastructure, our cooperative reasoning module enables agents to align their belief states and execute joint logical inferences without requiring full state broadcasting. Extensive empirical evaluations across diverse multi-agent scenarios demonstrate that our proposed architecture reduces communication bandwidth consumption by significant margins while maintaining or improving collective task performance compared to state-of-the-art baselines. We provide a rigorous theoretical analysis of the compression bounds and reasoning convergence. Our findings indicate that integrating memory pruning with selective communication is essential for the scalable deployment of autonomous multi-agent networks in bandwidth-constrained environments.

Multi-agent systems (MAS) are systems in which many intelligent agents interact with each other to accomplish a common goal, e.g. solving a complicated problem in a distributed environment. Domain specific knowledge can no longer solely support reasoning in MAS, whereas common sense knowledge becomes more critical to the reasoning quality, especially when e-commerce has become more popular and more merchants are getting excited to the worldwide market. Incorporating common sense knowledge to the MAS is on edge. However, the use of common sense knowledge induces implementation dilemmas. For example, OpenCyc is not only a common sense knowledge base, but also has its own inference engine. Developers have to determine whether to use the existing inference engine or to use the one that OpenCyc provided. In this paper, four approaches to incorporate common sense knowledge to MAS are proposed and evaluated, and we finally advocate our favorite.

We review the First International Competition on Computational Models of Argumentation (ICCMA'15). The competition evaluated submitted solvers' performance on four different computational tasks related to solving abstract argumentation frameworks. Each task evaluated solvers in ways that pushed the edge of existing performance by introducing new challenges. Despite being the first competition in this area, the high number of competitors entered, and differences in results, suggest that the competition will help shape the landscape of ongoing developments in argumentation theory solvers.

Design and results of the Second International Competition on Computational Models of Argumentation

Formal Argumentation is emerging as a key reasoning paradigm building bridges among knowledge representation and reasoning in artificial intelligence, informal argumentation in philosophy and linguistics, legal and ethical argumentation, mathematical and logical reasoning, and graph-theoretic reasoning. It aims to capture diverse kinds of reasoning and dialogue activities in the presence of uncertainty and conflicting information in a formal and intuitive way, with potential applications ranging from argumentation mining, via LegalTech and machine ethics, to therapy in clinical psychology. The turning point for the modern stage of formal argumentation theory, much similar to the introduction of possible worlds semantics for the theory of modality, is the framework and language of Dung's abstract argumentation theory introduced in 1995. This means that nothing could remain the same as before 1995 - it should be a focal point of reference for any study of argumentation, even if it is critical about it. Now, in modal logic, the introduction of the possible worlds semantics has led to a complete paradigm shift, both in tools and new subjects of studies. This is still not fully true for what is going on in argumentation theory. The Dagstuhl workshop led to the first volume of a handbook series in formal argumentation, reflecting the new stage of the development of argumentation theory.

We introduce a novel comprehensive framework for epistemic reasoning in multi-agent systems where agents may behave asynchronously and may be byzantine faulty. Extending Fagin et al.’s classic runs-and-systems framework to agents who may arbitrarily deviate from their protocols, it combines epistemic and temporal logic and incorporates fine-grained mechanisms for specifying distributed protocols and their behaviors. Besides our framework’s ability to express any type of faulty behavior, from fully byzantine to fully benign, it allows to specify arbitrary timing and synchronization properties. As a consequence, it can be adapted to any message-passing distributed computing model we are aware of, including synchronous processes and communication, (un-)reliable uni-/multi-/broadcast communication, and even coordinated action. The utility of our framework is demonstrated by formalizing the brain-in-a-vat scenario, which exposes the substantial limitations of what can be known by asynchronous agents in fault-tolerant distributed systems. Given the knowledge of preconditions principle, this restricts preconditions that error-prone agents can use in their protocols. In particular, it is usually necessary to relativize preconditions with respect to the correctness of the acting agent.