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.

Multi-representational architectures exploit diversity to yield the breadth of capabilities required for intelligent behavior in the world, but in so doing can sacrifice too much of the complementary benefits of architectural uniformity. The proposal here is to couple the benefits of diversity and uniformity through establishment of a uniform graph-based implementation level for diverse architectures.

Comirit is a framework for commonsense reasoning that combines simulation, logical deduction and passive machine learning. While a passive, observation-driven approach to learning is safe and highly conservative, it is limited to inte-raction only with those objects that it has previously ob-served. In this paper we describe a preliminary exploration of methods for extending Comirit to allow safe action selection in uncertain situations, and to allow reward-maximizing selection of behaviors.

A sizable fraction of current research into human visuo-spatial knowledge processing explicitly or implicitly suggests a spatial processing of certain knowledge types and a visual processing of others. Similarly, many formal and technical approaches for representing and processing visuo-spatial information in artificial intelligence, in computational cognitive modeling, or in knowledge representation and reasoning explicitly or implicitly treat visual and spatial information as belonging to separate types. While there exists good evidence for some differences in mental processing of different visuo-spatial knowledge types, there is much less reason to maintain the currently ascribed separation between the visual and the spatial. We provide arguments on why strict dichotomies seem unwarranted with regard to descriptions of human mental spatial reasoning and disadvantageous for the formal and technical approaches. We build upon a synopsis of psychological evidence for the existence of multiple knowledge type specific representations in human visuo-spatial reasoning and discuss the notion of scalable representation structures. In absence of proof to the contrary, it seems better practice to assume that (a) many of the type differences attributed to visuo-spatial knowledge processing are gradual rather than qualitative in nature, and that (b) tasks involving visuo-spatial knowledge of several types are often mentally processed through dynamic associations of structures for processing basal knowledge types. The paper calls for more investigations of human reasoning in visuo-spatial tasks in which knowledge types dynamically change during reasoning. It outlines a research framework for systematically investigating different basal visuo-spatial knowledge types and their combinations with regard to cognitive and computational plausibility. Current research is related to the framework, including research on Casimir, our computational cognitive architecture for reasoning with visuo-spatial knowledge. We argue that a more systematic course of research along the lines of the proposed framework will not only lead to more appropriate descriptions of human cognition (regarding visuo-spatial knowledge processing) but may also spawn more integrated and versatile formal and technical approaches for dealing with visuo-spatial information.

Face recognition under variable lighting conditions is recognized as one of the most problematic are of the recognition domain by various authors. Previous work suggested that image variations caused by parameters such as illumination, can be modeled by low dimensional subspaces. In this work, we propose a new scheme for recognition under a single variation. Using a generic manifold learning technique like LPP, we are able to construct coordinate systems for the underlying subspace with the help of an optimization step. We performed experiments with face recognition under changing illumination conditions.