We describe CARDIAC, a prototype for an intelligent conversational assistant that provides health monitoring for chronic heart failure patients. CARDIAC supports user initiative through its ability to understand natural language and connect it to intention recognition. The natural language interface allows patients to interact with CARDIAC without special training. The system is designed to understand information that arises spontaneously in the course of the interview. If the patient gives more detail than necessary for answering a question, the system updates the user model accordingly. CARDIAC is a first step towards developing cost-effective, customizable, automated in-home conversational assistants that help patients manage their care and monitor their health using natural language.

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 benefits of an abstract AF, while keeping notions distinct which are conflated in other approaches to instantiation.

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.

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, and will be answered using an argumentative inference mechanism. We focus on a particular implementation of recommended systems that extends the integration of argumentation and recommender systems to a multi-agent setting. Our approach is based on a DeLP-server that can answer queries from agents in remote hosts. Since client agents can consult different domain specific recommender servers, then, multiple configurations of clients and servers can be defined.

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 retain some of the characteristics of the kind of reasoning that a human would perform in this situation.

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 may not have been taught an explicit model of argument.  Computational models of argument may be 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.

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 may need to communicate their arguments between one another to convince each other of the acceptability or not of a given claim.

In the The use of argumentation for decision support is not new, remainder of the paper, we motivate our approach by using a with a long history of studies such as (Amgoud and Prade group decision making setting in clinical oncology, present a 2009; Amgoud and Vesic 2009; Amgoud, Dimopoulos, and formal framework, and procedural basis for mixed initiative Moraitis 2008; Fox et al. 2007; Amgoud and Prade 2006; argumentation and finally describe a clinical group decision Atkinson, Bench-Capon, and Modgil 2006; Rehg, McBurney, support system that implements this framework.

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 this 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.

Argumentation is a form of reasoning that makes explicit the reasons for the conclusions that are drawn and how con- flicts between reasons are resolved. This provides a natural mechanism, for example, to handle inconsistent and uncer- tain information and to resolve conflicts of opinion between intelligent agents. The advantage of a mechanism based on argumentation is that considering the reasons behind the conclusions offers more than considering the conclusions alone (to adapt something Isaac Bashevis Singer once said, the approach has “more vitamins” than other approaches to reasoning). For example, in dealing with inconsistent infor- mation, an early use of argumentation, it is possible to know more than just that we have the inconsistent conclusions p and not p. We can establish exactly which pieces of infor- mation lead to these conclusions and can then prioritize one conclusion over another on the basis of this information, de- cide what information should be revised to achieve consis- tency, or even determine what additional investigation needs to be carried out (when we have reason to believe both that it is raining outside and not raining outside, and have no way of determining which is correct, going to look may be the best solution).