Data Mining - Concepts and Techniques.

Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI

In the last years, Artificial Intelligence (AI) has achieved a notable momentum that may deliver the best of expectations over many application sectors across the field. For this to occur, the entire community stands in front of the barrier of explainability, an inherent problem of AI techniques brought by sub-symbolism (e.g. ensembles or Deep Neural Networks) that were not present in the last hype of AI. Paradigms underlying this problem fall within the so-called eXplainable AI (XAI) field, which is acknowledged as a crucial feature for the practical deployment of AI models. This overview examines the existing literature in the field of XAI, including a prospect toward what is yet to be reached. We summarize previous efforts to define explainability in Machine Learning, establishing a novel definition that covers prior conceptual propositions with a major focus on the audience for which explainability is sought. We then propose and discuss about a taxonomy of recent contributions related to the explainability of different Machine Learning models, including those aimed at Deep Learning methods for which a second taxonomy is built. This literature analysis serves as the background for a series of challenges faced by XAI, such as the crossroads between data fusion and explainability. Our prospects lead toward the concept of Responsible Artificial Intelligence, namely, a methodology for the large-scale implementation of AI methods in real organizations with fairness, model explainability and accountability at its core. Our ultimate goal is to provide newcomers to XAI with a reference material in order to stimulate future research advances, but also to encourage experts and professionals from other disciplines to embrace the benefits of AI in their activity sectors, without any prior bias for its lack of interpretability.

Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI.

As the title of this book identifies clearly, the authors combine the concepts of competitiveness and sustainability, which some may feel are difficult to reconcile, to provide guidance to enhance the management of tourism destinations. The contents of this book are not novel for it does not contain new insights on the topics that are covered or fresh case studies. However, it is innovative in that it introduces, within the same document, competitiveness and sustainability, embellishes these concepts and uses them to develop a conceptual framework. The explication of this framework is both the objective and content of the book. The focus is upon tourism destinations. The emphasis is on places with multiple attractions and accommodation establishments but the framework has applicability to countries and larger regions; states counties and smaller places; and there is also much of utility to the individual operator. The authors are devotees of Porter whose seminal works underpin their thinking, albeit with modifications to fit tourism.

The Competitive Destination: a Sustainable Tourism Perspective

Multiagent Systems is the title of a collection of papers dedicated to surveying specific themes of Multiagent Systems (MAS) and Distributed Artificial Intelligence (DAI). All of them authored by leading researchers of this dynamic multidisciplinary field.

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Multiagent systems: a modern approach to distributed artificial intelligence

Patterns for context-based knowledge fusion in decision support systems

Today multidisciplinary real-world problem solving requires an intensive knowledge integration and exchange between participants of the global information environment. Along with a large number of available distributed knowledge sources (KSs) this has led to the appearance of a new direction in knowledge management called knowledge logistics (KL). The paper describes a KSNet-approach, being developed to build an intelligent support system addressing problems of KL through knowledge fusion (KF). The approach is mainly based on such technologies as ontology management and intelligent agents. In the KSNet-approach, a multi-agent system architecture based on FIPA Reference Model was chosen as a technological basis for definition of agent properties and functions and FIPA ontology definition was used for knowledge representation. As a general model of ontology representation the object-oriented constraint network paradigm was proposed. The most important ideas of the proposed KSNet-approach were implemented an...

Knowledge source network configuration approach to knowledge logistics

Knowledge logistics in information grid environment

Agent-based support of mass customization for corporate knowledge management

Cyber-Physical-Social Systems (CPSSs) integrate various resources from physical, cyber, and social worlds. Efficient interaction of these resources is essential for CPSSs operation. Ontologies do not only provide for semantic operability between different resources but also provide means to create sharable ontology-based context models specified for actual settings. Usage of the context supports situation-driven behavior of CPSSs resources and thus is an enabler for their self-organisation. The present research inherits the idea of context ontologies usage for modelling context in CPSSs. In this work, an upper level context ontology for CPSSs is proposed. This ontology is applied in the domain of self-organising resource network.

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Tatiana Levashova

Papers

Patterns for context-based knowledge fusion in decision support systems

Knowledge source network configuration approach to knowledge logistics

Knowledge logistics in information grid environment

Agent-based support of mass customization for corporate knowledge management

Ontology for cyber-physical-social systems self-organisation