Assumption-based argumentation (ABA) is a powerful defeasible reasoning formalism which is based on the interplay of assumptions, their contraries, and inference rules. ABA with preferences ( ABA + ) generalizes the basic model by allowing a qualitative comparison of assumptions. The integration of preferences however comes with a cost. In ABA + , the evaluation under two central and well-established semantics—grounded and complete semantics—is not guaranteed to yield an outcome. Moreover, while ABA frameworks without preferences allow for a graph-based representation in Dung-style frameworks, an according instantiation for general ABA + frameworks has not been established so far. In this work, we tackle both issues: First, we develop a novel abstract argumentation formalism based on set-to-set attacks. We show that our so-called Hyper Argumentation Frameworks (HYPAFs) capture the attack relation between assumptions in ABA + . Second, we exploit this correspondence between ABA + and HYPAFs to obtain relaxed variants of complete and grounded semantics for HYPAFs that yield an extension for all frameworks by design, while still faithfully generalizing the established semantics of Dung-style Argumentation Frameworks. Finally, we discuss basic properties and provide a thorough complexity analysis for both the abstract HYPAFs as well as ABA + .

Assumption-based argumentation (ABA) is a powerful defeasible reasoning formalism which is based on the interplay of assumptions, their contraries, and inference rules. ABA with preferences (ABA+) generalizes the basic model by allowing qualitative comparison between assumptions. The integration of preferences however comes with a cost. In ABA+, the evaluation under two central and well-established semantics---grounded and complete semantics---is not guaranteed to yield an outcome. Moreover, while ABA frameworks without preferences allow for a graph-based representation in Dung-style frameworks, an according instantiation for general ABA+ frameworks has not been established so far. In this work, we tackle both issues: First, we develop a novel abstract argumentation formalism based on set-to-set attacks. We show that our so-called Hyper Argumentation Frameworks (HYPAFs) capture ABA+. Second, we propose relaxed variants of complete and grounded semantics for HYPAFs that yield an extension for all frameworks by design, while still faithfully generalizing the established semantics of Dung-style Argumentation Frameworks. We exploit the newly established correspondence between ABA+ and HYPAFs to obtain variants for grounded and complete ABA+ semantics that are guaranteed to yield an outcome. Finally, we discuss basic properties and provide a complexity analysis. Along the way, we settle the computational complexity of several ABA+ semantics.

Already in Dung’s seminal paper introducing Abstract Argumentation Frameworks (AFs), several connections to seemingly unrelated reasoning formalisms have been illustrated. In this work, we continue this trend and establish a connection between abstract argumentation frameworks and boolean networks (BNs). BNs, in a nutshell, mimic simple binary-valued systems, where for each point in time, the value of each bit (component) depends only on the other components’ values of the previous point in time of the network. This formalism is widely used to formally analyze biological processes, where from simple rules complex behavior emerges. We show that stable extensions of an arbitrary AF correspond to single state attractors of its canonically corresponding BN, the complete extensions correspond to a distinctive 2-state attractor, and the admissible sets correspond to the seeds of the BN. We thereby lay the groundwork for a fruitful exchange of ideas between the two research areas.

Abstract We describe the new version of the Planning Domain Definition Language (PDDL)-to-Answer Set Programming (ASP) translator plasp . First, it widens the range of accepted PDDL features. Second, it contains novel planning encodings, some inspired by Satisfiability Testing (SAT) planning and others exploiting ASP features such as well-foundedness. All of them are designed for handling multivalued fluents in order to capture both PDDL as well as SAS planning formats. Third, enabled by multishot ASP solving, it offers advanced planning algorithms also borrowed from SAT planning. As a result, plasp provides us with an ASP-based framework for studying a variety of planning techniques in a uniform setting. Finally, we demonstrate in an empirical analysis that these techniques have a significant impact on the performance of ASP planning.

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.

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.

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.

Preference elicitation is a serious bottleneck in many decision support applications and agent specification tasks. Ceteris paribus (CP)‐nets were designed to make the process of preference elicitation simpler and more intuitive for lay users by graphically structuring a set of CP preference statements—preference statements that most people find natural and intuitive. Beside their usefulness in the process of preference elicitation, CP‐nets support efficient optimization algorithms that are crucial in most applications (e.g., the selection of the best action to execute or the best product configuration). In various contexts, CP‐nets with an underlying cyclic structure emerge naturally. Often, they are inconsistent according to the current semantics, and the user is required to revise them. In this paper, we show how optimization queries can be meaningfully answered in many “inconsistent” networks without troubling the user with requests for revisions. In addition, we describe a method for focusing the user's revision process when revisions are truly needed. In the process, we provide a formal semantics that justifies our approach and new techniques for computing optimal outcomes. Some of the methods we use are based on a reduction to the problem of computing stable models for nonmonotonic logic programs, and we explore this relationship closely.

A semantics for inheritance reasoning is presented which allows both strict and defeasible knowledge to be represented. The approach proposed considers the semantics as consisting of two parts: the content theory which describes the knowledge about the world and a ‘process model’ which explains how the knowledge in the network is processed. Each process model is expressed by an algorithm which, given two sets of properties for each class or individual, computes a new set of properties. It is argued that the ‘clash of intuitions’ which appears in the literature on inheritance reasoning, is a clash of process models, as, in certain situations, different process models give different meaning to the links of a network. An attempt is also made to explain the instability of an inheritance reasoning system. It is argued that instability should not be surprising if the extra meaning that the process model assigns to a network is taken into account. This is because, in certain process models, no link in a network is considered as redundant. Some general questions on inheritance reasoning are finally raised.