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.

The increase of connectivity and the impact it has in every day life is raising new and existing security problems that are becoming important for social good. We introduce two particular problems: cyber attack attribution and regulatory data sharing. For both problems, decisions about which rules to apply, should be taken under incomplete and context dependent information. The solution we propose is based on argumentation reasoning, that is a well suited technique for implementing decision making mechanisms under conflicting and incomplete information. Our proposal permits us to identify the attacker of a cyber attack and decide the regulation rule that should be used while using and sharing data. We illustrate our solution through concrete examples.

This paper presents the STAR system, a system for automated narrative comprehension, developed on top of an argumentation-theoretic formulation of defeasible reasoning, and strongly following guidelines from the psychology of comprehension. We discuss the system's use in psychological experiments on story comprehension, and our plans for its broader use in empirical studies concerning wider issues of commonsense reasoning.

Motivated by the requirements of many real-life applications, recent research in AI planning has shown a growing interest in tackling problems that involve numeric constraints and complex optimization objectives. Applying Integer Programming (IP) to such domains seems to have a significant potential, since it can naturally accommodate their representational requirements. In this paper we explore the area of applying IP to AI planning in two different directions. First, we improve the domain-independent IP formulation of Vossen et al., by an extended exploitation of mutual exclusion relations between the operators, and other information derivable by state of the art domain analysis tools. This information may reduce the number of variables of an IP model and tighten its constraints. Second, we link IP methods to recent work in heuristic search for planning, by introducing a variant of {\tt FF}'s enforced hill-climbing algorithm that uses IP models as its underlying representation. In addition to extending the delete lists heuristic to parallel planning and the more expressive language of IP, we also introduce a new heuristic based on the linear relaxation.

Among the issues faced by an intelligent agent, central is that of reconciling the, often contradictory, pieces of knowledge — be those given, learned, or sensed — at its disposal. This problem, known as knowledge qualification, requires that pieces of knowledge deemed reliable in some context be given preference over the others. These preferences are typically viewed as encodings of reasoning patterns; so, the frame axiom can be encoded as a preference of persistence over spontaneous change. Qualification, then, results by the principled application of these preferences. We illustrate how this can be naturally done through argumentation, by uniformly treating object-level knowledge and reasoning patterns alike as arguments that can be defeated by other stronger ones. We formulate an argumentation framework for Reasoning about Actions and Change that gives a semantics for Action Theories that include a State Default Theory. Due to their explicit encoding as preferences, reasoning patterns can be adapted, when and if needed, by a domain designer to suit a specific application domain. Furthermore, the reasoning patterns can be defeated in lieu of stronger external evidence, allowing, for instance, the frame axiom to be overridden when unexpected sensory information suggests that spontaneous change may have broken persistence in a particular situation.