Combining counterpropagation neural networks and defeasible logic programming for text classification

The increasing growth of documents available in the World Wide Web has resulted in a difficult situation for those end-users who search for a particular piece of information. A common approach to facilitate search is to perform document classification first, learning the topology of a document base as a set of clusters. Clusters will be labeled as relevant or irrelevant, and determining whether a new document belongs to a given cluster can help determine whether such document corresponds to the user information needs. We contend that the above clustering technique can be enriched by additional filtering criteria specified in terms of Defeasible Logic Programming (DeLP). In this paper we discuss a combination of Counterpropagation Neural Networks for clustering and DeLP to solve the problem of classifying documents according to user-specified criteria. We present an example of how the proposed approach works.Eje: Sistemas de información y MetaheurísticaRed de Universidades con Carreras en Informática (RedUNCI

Combining argumentation and clustering techniques in pattern classification problems

Clustering techniques can be used as a basis for classification systems in which clusters can be classified into two categories: positive and negative. Given a new instance enew, the classification algorithm is applied to determine to which cluster ci it belongs and the label of the cluster is checked. In such a setting clusters can overlap, and a new instance (or example) can be assigned to more than one cluster. In many cases, determining to which cluster this new instance actually belongs requires a qualitative analysis rather than a numerical one. In this paper we present a novel approach to solve this problem by combining defeasible argumentation and a clustering algorithm based on the Fuzzy Adaptive Resonance Theory neural network model. The proposed approach takes as input a clustering algorithm and a background theory. Given a previously unseen instance enew, it will be classified using the clustering algorithm. If a conflicting situation arises, argumentation will be used in order to consider the user’s preference criteria for classifying examples.Eje: Agentes y Sistemas Inteligentes (ASI)Red de Universidades con Carreras en Informática (RedUNCI

Modelling derivation in defeasible logic programming with perceptron-based neural networks

A solution of problems in multiagent systems involves representing beliefs of agents immersed in dynamic environments. Observation-based Defeasible Logic Programming (ODeLP) is an argument-based logic programming language that is used to represent an agent’s knowledge in the context of a multiagent system. The beliefs of the agent depends on a warrant procedure performed on its knowledge base contents. New perceptions result in changes in the agent’s beliefs. In the context of real time constraints, this belief change procedure should be done efficiently. This paper introduces an algorithm for translating an agent’s knowledge base, expressed as an ODeLP rule base, into a Perceptron-based neural network. Observations in an ODeLP program can then be codified as an input pattern. The input pattern is then fed to the neural network whose propagation results in an output pattern. This output pattern contains information regarding which beliefs can be hold by the agent as well as if there exists contradiction among them. The proposal is attractive as the massivelly parallel processing intrinsic to neural networks make them appropiate for implementing parts of the aforementioned warrant procedure.Eje: V - Workshop de agentes y sistemas inteligentesRed de Universidades con Carreras en Informática (RedUNCI

Argumentation for machine learning: a survey

Existing approaches using argumentation to aid or improve machine learning differ in the type of machine learning technique they consider, in their use of argumentation and in their choice of argumentation framework and semantics. This paper presents a survey of this relatively young field highlighting, in particular, its achievements to date, the applications it has been used for as well as the benefits brought about by the use of argumentation, with an eye towards its future

Integrating defeasible argumentation with fuzzy ART neural networks for pattern classification

Many classification systems rely on clustering techniques in which a collection of training examples is provided as an input, and a number of clusters c1,...cm modelling some concept C results as an output, such that every cluster ci is labelled as positive or negative. Given a new, unlabelled instance enew, the above classification is used to determine to which particular cluster ci this new instance belongs. In such a setting clusters can overlap, and a new unlabelled instance can be assigned to more than one cluster with conflicting labels. In the literature, such a case is usually solved non-deterministically by making a random choice. This paper presents a novel, hybrid approach to solve this situation by combining a neural network for classification along with a defeasible argumentation framework which models preference criteria for performing clustering.Facultad de Informátic

Logic-based Technologies for Intelligent Systems: State of the Art and Perspectives

Together with the disruptive development of modern sub-symbolic approaches to artificial intelligence (AI), symbolic approaches to classical AI are re-gaining momentum, as more and more researchers exploit their potential to make AI more comprehensible, explainable, and therefore trustworthy. Since logic-based approaches lay at the core of symbolic AI, summarizing their state of the art is of paramount importance now more than ever, in order to identify trends, benefits, key features, gaps, and limitations of the techniques proposed so far, as well as to identify promising research perspectives. Along this line, this paper provides an overview of logic-based approaches and technologies by sketching their evolution and pointing out their main application areas. Future perspectives for exploitation of logic-based technologies are discussed as well, in order to identify those research fields that deserve more attention, considering the areas that already exploit logic-based approaches as well as those that are more likely to adopt logic-based approaches in the future

Combining counterpropagation neural networks and defeasible logic programming for text classification

The increasing growth of documents available in the World Wide Web has resulted in a difficult situation for those end-users who search for a particular piece of information. A common approach to facilitate search is to perform document classification first, learning the topology of a document base as a set of clusters. Clusters will be labeled as relevant or irrelevant, and determining whether a new document belongs to a given cluster can help determine whether such document corresponds to the user information needs. We contend that the above clustering technique can be enriched by additional filtering criteria specified in terms of Defeasible Logic Programming (DeLP). In this paper we discuss a combination of Counterpropagation Neural Networks for clustering and DeLP to solve the problem of classifying documents according to user-specified criteria. We present an example of how the proposed approach works.Eje: Sistemas de información y MetaheurísticaRed de Universidades con Carreras en Informática (RedUNCI

Combining argumentation and clustering techniques in pattern classification problems

Clustering techniques can be used as a basis for classification systems in which clusters can be classified into two categories: positive and negative. Given a new instance enew, the classification algorithm is applied to determine to which cluster ci it belongs and the label of the cluster is checked. In such a setting clusters can overlap, and a new instance (or example) can be assigned to more than one cluster. In many cases, determining to which cluster this new instance actually belongs requires a qualitative analysis rather than a numerical one. In this paper we present a novel approach to solve this problem by combining defeasible argumentation and a clustering algorithm based on the Fuzzy Adaptive Resonance Theory neural network model. The proposed approach takes as input a clustering algorithm and a background theory. Given a previously unseen instance enew, it will be classified using the clustering algorithm. If a conflicting situation arises, argumentation will be used in order to consider the user’s preference criteria for classifying examples.Eje: Agentes y Sistemas Inteligentes (ASI)Red de Universidades con Carreras en Informática (RedUNCI

Empowering Qualitative Research Methods in Education with Artificial Intelligence

Artificial Intelligence is one of the fastest growing disciplines, disrupting many sectors. Originally mainly for computer scientists and engineers, it has been expanding its horizons and empowering many other disciplines contributing to the development of many novel applications in many sectors. These include medicine and health care, business and finance, psychology and neuroscience, physics and biology to mention a few. However, one of the disciplines in which artificial intelligence has not been fully explored and exploited yet is education. In this discipline, many research methods are employed by scholars, lecturers and practitioners to investigate the impact of different instructional approaches on learning and to understand the ways skills and knowledge are acquired by learners. One of these is qualitative research, a scientific method grounded in observations that manipulates and analyses non-numerical data. It focuses on seeking answers to why and how a particular observed phenomenon occurs rather than on its occurrences. This study aims to explore and discuss the impact of artificial intelligence on qualitative research methods. In particular, it focuses on how artificial intelligence have empowered qualitative research methods so far, and how it can be used in education for enhancing teaching and learning