In his 'On Sophistical Refutations', Aristotle comments: an argument which denied that it was better to take a walk after dinner, because of Zeno's argument, would not be a proper argument for a doctor, because Zeno's argument is of general application. Aristotle's criticism is of the impropriety of the argument, not a failure of cogency. But what kind of failing is it for an argument to be "of general application"? This talk is not an exercise in historical scholarship, and I abstract a great deal from the context of Aristotle's remark. Once Aristotle's criticism is understood in less restricted terms than he offers, I attempt to account for when and why a challenge to an assertion is too general, using Zeno's Argument and everyday examples as illustrations. The main ingredients of the account I develop is an assertion-challenge-default model of dialogue and pragmatic notions of presupposition, contrast classes, and selective relevance.

This paper describes a preliminary proposal of an argumentation-based approach to modeling articulated decision support contexts. The proposed approach encompasses a variety of argument and attack schemes aimed at representing basic knowledge and reasoning patterns for decision support. Some of the defined attack schemes involve attacks directed towards other attacks, which are not allowed in traditional argumentation frameworks but turn out to be useful as a knowledge and reasoning modeling tool: in particular, we demonstrate their use to support what-if reasoning capabilities, which are of primary importance in decision support. Formal backing to the proposed approach is provided by the AFRA formalism, a recently proposed extension of Dung’s argumentation framework. A literature example concerning a decision problem about medical treatments is adopted to illustrate the approach.

There are two aspects of practical reasoning which present particular difficulties for current approaches to modelling practical reasoning through argumentation: temporal aspects, and the intrinsic worth of actions. Time is important because actions change the state of the world, we need to consider future states as well as past and present ones. Equally, it is often not what we do but the way that we do it that matters: the same future state may be reachable either through desirable or undesirable actions, and often also actions are done for their own sake rather than for the sake of their consequences. In this paper we will present a semantics for practical reasoning, based on a formalisation developed originally for reasoning about commands, in which actions and states are treated as of equal status. We will show how using these semantics facilitates the handling of the temporal aspects of practical reasoning, and enables, where appropriate, justification of actions without reference to their consequences.

In this work we will present an integration of a query answering argumentation approach with an abstract agent programming language. Agents will use argumentatively reason via queries, using information of their mental components. Special context-based queries will be used to model the interaction between mental components. Deliberation and execution semantics of the proposed integration are presented

Assumption-Based Argumentation (ABA), and to a large extent argumentation in general, up to now has been considered in a single-agent setting. ABA, in particular, is such that an agent engages in a dispute (dialectic proof procedure) with itself (an imaginary opponent) to decide whether a claim is acceptable according to some acceptability criteria. We present in this paper a generalised proof procedure for the admissibility semantics of ABA, which is still a dispute by an agent with itself but such that the outcome can be readily communicated to other agents. This is important for applications in multi-agent systems wherein agents may differ in the knowledge they have and wherein agents may need to communicate their arguments between one another to convince each other of the acceptability or not of a given claim.

Formal and computational models of argument are ideally suited for education in ill-defined domains such as law, public policy, and science. Open-ended arguments play a central role in these areas but students of the domains are rarely taught an explicit model of argument. Computational models of argument are ideally suited to act as argument tutors guiding students in the formation of arguments and argument analysis according to an explicit model. In order to achieve this it is important to establish that the models can be understood and evaluated reliably, an empirical question. In this paper we report ongoing work on the diagnostic utility of argument diagrams produced in the LARGO tutoring system.

Argumentation is now a very fertile area of research in Artificial Intelligence. Yet, most approaches to reasoning with arguments in AI are based on a normative perspective, relying on intuition as to what constitutes correct reasoning, sometimes aided by purpose-built hypothetical examples. For these models to be useful in agent-human argumentation, they can benefit from an alternative, positivist perspective that takes into account the empirical reality of human reasoning. To give a flavour of the kinds of lessons that this methodology can provide, we report on a psychological study exploring simple reinstatement in argumentation semantics. Empirical results show that while reinstatement is cognitively plausible in principle, it does not yield full recovery of the argument status, a notion not captured in Dung’s classical model. This result suggests some possible avenues for research relevant to making formal models of argument more useful.

Inconsistency management policies allow a relational database user to express customized ways for managing inconsistency according to his need. For each functional dependency, a user has a library of applicable policies, each of them with constraints, requirements, and preferences for their application that can contradict each other. The problem that we address in this work is that of determining a subset of these policies that are suitable for application w.r.t. the set of constraints and user preferences. We propose a classical logic argumentation-based solution, which is a natural approach given that integrity constraints in databases and data instances are, in general, expressed in first order logic (FOL). An automatic argumentation-based selection process allows to retains some of the characteristics of the kind of reasoning that a human would perform in this situation.

Verification that agent communication protocols have desirable properties or do not have undesirable properties is an important issue for agents intending to communicate with such protocols. In this paper we explore the use of model checkers to verify properties of agent communication protocols, with these properties expressed as formulae in temporal logic. We illustrate our approach using a recently-proposed protocol for agent dialogs over commands, a protocol that permits the agents to present questions, challenges and arguments for or against compliance with a command.

In this work we introduce a defeasible logic programming recommender server that accepts  different types of queries from client agents that can be distributed in remote hosts. We formalize new ways of querying recommender servers containing specific information or preferences, and creating a particular context for the queries. This special type of queries (called contextual queries)  allows recommender servers to compute recommendations for any client using its preferences. These contextual queries will be answered using a defeasible logic programming inference mechanism.

Defeasible Logic Programming (DELP) is a formalism that extends declarative programming to capture defeasible reasoning. Its inference mechanism, upon a query on a literal in a program, answers by indicating whether or not it is warranted in an argumentation process. While the properties of DELP are well known, some of its basic elements can be redefined in order to shed light on some of the subtleties of the warrant process. We will discuss these alternative definitions and the cases in which they provide a better performance.

An understanding of the policy and resource availability constraints under which others operate is important for effectively developing and resourcing plans in a multi-agent context. Such constraints (or norms) are not necessarily public knowledge, even within a team of collaborating agents. What is required are mechanisms to enable agents to keep track of who might have and be willing to provide the resources required for enacting a plan by modeling the policies of others regarding resource use, information provision, etc. We propose a technique that combines machine learning and argumentation for identifying and modeling the policies of others. Furthermore, we demonstrate the utility of this novel combination of techniques through empirical evaluation.

This paper identifies ways in which traditional approaches to argumentation can be modified to meet the needs of practical group decision support. A framework for outcome-driven decision rationale management is proposed that permits a novel conception of mixed-initiative argumentation. The framework is evaluated in the context of group decision support in medicine.

Abstract argumentation frameworks (AFs) provide a fruitful basis for exploring issues of defeasible reasoning. Their power largely derives from the abstract nature of the arguments within the framework, where arguments are atomic nodes in an undifferentiated relation of attack. This abstraction conceals different conceptions of argument, and concrete instantiations encounter difficulties as a result of conflating these conceptions. We distinguish three distinct senses of the term. We provide an approach to instantiating AFs in which the nodes are restricted to literals and rules, encoding the underlying theory directly. Arguments, in each of the three senses, then emerge from this framework as distinctive structures of nodes and paths. Our framework retains the theoretical and computational beneftis of an abstract AF, while keeping notions distinct which are conflated in other approaches to instantiation.