Vaidas Giedrimas, Leonidas Sakalauskas and Anatoly Petrenko (July 19th 2017). Distributed Software Development Tools for Distributed Scientific Applications, Recent Progress in Parallel and Distributed Computing, Wen-Jyi Hwang, IntechOpen, DOI: 10.5772/intechopen.68334.

Eneko Osaba, Esther Villar and Javier Del Ser (December 4th 2019). Introductory Chapter: Swarm Intelligence - Recent Advances, New Perspectives, and Applications, Swarm Intelligence - Recent Advances, New Perspectives and Applications, Javier Del Ser, Esther Villar and Eneko Osaba, IntechOpen, DOI: 10.5772/intechopen.90066.

Austria

The growing complexity of nowadays distributed systems becomes a critical issue.  Distributed software systems are forced to integrate other software systems and components that are often not reliable, exhibit bad performance, and are sometimes unavailable. Such software is typically characterized by a huge problem size concerning number of computers, clients, requests and size of queries, autonomy and heterogeneity of participating organizations, and dynamic changes of the environment. To cope with unforeseen dynamics in the environment and vast number of unpredictable dependencies on participating components, there is a demand on self-organizing approaches.

IJCCI 2019

Swarm Intelligence in Distributed Systems&#39; Use-cases

The purpose of IJCCI is to bring together researchers, engineers and practitioners on the areas of Fuzzy Computation, Evolutionary Computation and Neural Computation. IJCCI is composed of three co-located conferences, each specialized in at least one of the aforementioned main knowledge areas.

The purpose of IJCCI is to bring together researchers, engineers and practitioners on the areas of Fuzzy Computation, Evolutionary Computation and Neural Computation.

Swarm Intelligence in Distributed Systems' Use-cases

keynote

How Theoretical Analyses Can Impact Practical Applications of Evolutionary Computation

Explainable AI is rapidly becoming extremely important. Whilst classic rule-based approaches of early AI have been comprehensible "glass-box" approaches at least in narrow domains, their weakness was in dealing with uncertainties of the real world. The introduction of statistical/probabilistic learning methods has made AI increasingly successful. Meanwhile deep learning approaches even exceed human performance in particular tasks. However, such approaches are becoming increasingly opaque, and even if we understand the underlying mathematical principles of such models, they still lack explicit declarative knowledge. For example, words are mapped to high-dimensional vectors, making them unintelligible to humans. What we need in the future are context-adaptive procedures, i.e. systems that construct contextual explanatory models for classes of real-world phenomena. This talk presents some recent approaches on how to understand why a machine decision has been reached, making results re-traceable, explainable and comprehensible on demand and paving the way for an expert in the loop to augment human intelligence with artificial intelligence.

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