Automated negotiation aims at finding agreements between agents with conflicting goals. Existing utility-based approaches guarantee agents satisfaction with negotiation outcomes, especially in multi-issue negotiations where concession mechanisms lead to win-win results. However, they lack explainability and do not consider agents’ beliefs. On the other hand, argument-based approaches provide reasons for accepting or rejecting offers but do not include utility modeling for offers or enable concession mechanisms in multi-issue settings. We propose a novel hybrid approach combining both types of approaches. The utility-based component enables agents to make concessions on complex negotiation objects to achieve win-win outcomes, while the argumentation component ensures that accepted offers align with the agents' personal argumentation theories. These theories represent their beliefs, encoding various profiles, ethical considerations, social norms, or legal principles.

Recently, Strength-based Argumentation Frameworks (StrAFs) have been proposed to model situations where some quantitative strength is associated with arguments. In this setting, the notion of accrual corresponds to sets of arguments that collectively attack an argument. Some semantics have already been defined, which are sensitive to the existence of accruals that collectively defeat their target, while their individual elements cannot. However, until now, only the surface of this framework and semantics have been studied. Indeed, the existing literature focuses on the adaptation of the stable semantics to StrAFs. In this paper, we push forward the study and investigate the adaptation of admissibility-based semantics. Especially, we show that the strong admissibility defined in the literature does not satisfy a desirable property, namely Dung’s fundamental lemma. We therefore propose an alternative definition that induces semantics that behave as expected. We then study computational issues for these new semantics, in particular we show that complexity of reasoning is similar to the complexity of the corresponding decision problems for standard argumentation frameworks in almost all cases. We then propose a translation in pseudo-Boolean constraints for computing (strong and weak) extensions. We conclude with an experimental evaluation of our approach which shows in particular that it scales up well for solving the problem of providing one extension as well as enumerating them all.

This paper addresses the problem of task allocation among multiple autonomous agents that must accomplish a complex global task. Solutions to the problem have real-world applications in defense, space, disaster management, etc. We solve this problem via agent coalition formation. Multiple coalition formation mechanisms were introduced in prior art, seldom accounting for interdependent tasks. We address this challenge. We introduce an anytime decentralized coalition formation mechanism that enables agents with complementary capabilities to form, autonomously and dynamically, feasible coalition structures that accomplish a global, composite task. The formed structures are incrementally improved via agent replacements to optimize a global utility. We analyze the complexity and show that, although the general problem is NP-hard, our mechanism provides a solution within acceptable time. We present extensive experimental results that illustrate the added value of our approach.

Dynamics of argumentation is the family of techniques concerned with the evolution of an argumentation framework (AF), for instance to guarantee that a given set of arguments is accepted. This work proposes Control Argumentation Frameworks (CAFs), a new approach that generalizes existing techniques, namely normal extension enforcement, by accommodating the possibility of uncertainty in dynamic scenarios. A CAF is able to deal with situations where the exact set of arguments is unknown and subject to evolution, and the existence (or direction) of some attacks is also unknown. It can be used by an agent to ensure that a set of arguments is part of one (or every) extension whatever the actual set of arguments and attacks. A QBF encoding of reasoning with CAFs provides a computational mechanism for determining whether and how this goal can be reached. We also provide some results concerning soundness and completeness of the proposed encoding as well as complexity issues.

Gorgias-B is a new tool that supports a methodology for the development of real life applications. It can be used by non-argumentation experts generating and testing automatically the target argumentation theory in Gorgias.

In debates, agents are faced with the problem of deciding how to best contribute to the current state of the debate in order to satisfy their own goals. Target sets specify minimal changes on the current state of the debate that are required to achieve such goals, where changes are the addition and/or deletion of attacks among arguments. However, agents may not have the ability to implement all the actions prescribed by a target set, nor to rely on others to help them to do so. In this short paper we provide evidence that this notion is still a useful criterion for move selection.

Argumentation-based negotiation has gained increasing prominence in the multi-agent field over the last years. There is currently a long literature on the use of argumentation in negotiation and especially the modeling of negotiation protocols or decision making mechanisms. However the study of strategic issues that define the behavior of an agent during the negotiation has been largely neglected. This work fills this gap by providing, profile, behavior and time constraints based tactics that can be combined together to implement complex strategies similar to those studied in game-theoretic negotiation. These different tactics lead to different types of concessions. An experimental evaluation shows how tactics and concessions may influence the negotiation length and outcome, under the assumptions of deadlines and the availability of information on the opponent.

Agents contributing to (online) debate systems often have different areas of expertise. This must be considered if we want to define a decision making process based on the output of such a system. Distinguishing agents on the basis of their areas of expertise also opens an interesting perspective: when a debate is deemed “controversial”, calling an additional expert may be a natural way to make the decision easier. We introduce possible definitions that capture these notions and we provide a preliminary analysis with the objective to help a designer find the “right” expert.

Many works have identified the potential benefits of using argumentation to address a large variety of multiagent problems. In this paper we take this idea one step further and develop the concept of a fully integrated argumentation-based agent architecture that allows us to develop agents that are coherently designed on an underlying argumentation based foundation. Under this architecture, an agent is composed of a collection of modules each of which is equipped with a local argumentation theory. Similarly, the intra-agent control of the agent is governed by local argumentation theories that are sensitive to the current situation of the agent through dynamically enabled feasibility arguments.

During the last years, argumentation has been gaining increasing interest in modeling different reasoning tasks of an agent. Many recent works have acknowledged the importance of incorporating preferences or priorities in argumentation. However, relatively little is known about the theoretical and computational implications of preferences in argumentation.

This paper presents an overall framework for carrying out different types of dialogues between intelligent and autonomous agents acting in an electronic marketplace. Such dialogues take place during various commercial transactions concerning requests and offers of products and services. The proposed dialogue framework has been adopted in the communication and collaboration protocols of an already implemented system, which enables buyers and sellers delegate a variety of tasks to their personal agents. Much attention has been paid to the personalization of collaborative agents, which may permanently live and interact in the market representing their owners' interests. Our overall approach builds on a modular decomposition of the agents involved, and a formal and operational modeling of the associated dialogues. Features of our framework are demonstrated through an illustrative example of dialogues deployed during interagent transactions on the establishment of a combined reservation for dinner and a movie. The main contribution of this work is that the proposed framework is capable to represent disparate dialogues taking place among agents having adopted diverse strategies for carrying out e‐commerce transactions.