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Dimitar Kazakov
None
Dimitar Kazakov
None
Adaptive Agents and Multi-Agent Systems II
Adaptive agents and multi-agent systems is an emerging and exciting interdisciplinary area of research and development involving artificial intelligence, software engineering, and developmental biology, as well as cognitive and social science. This book presents 17 revised and carefully reviewed papers taken from two workshops on the topic as well as 2 invited papers by leading researchers in the area. The papers deal with various aspects of machine learning, adaptation, and evolution in the context of agent systems and autonomous agents.
Adaptive Agents and Multi-Agent Systems II
Adaptive agents and multi-agent systems is an emerging and exciting interdisciplinary area of research and development involving artificial intelligence, software engineering, and developmental biology, as well as cognitive and social science. This book presents 17 revised and carefully reviewed papers taken from two workshops on the topic as well as 2 invited papers by leading researchers in the area. The papers deal with various aspects of machine learning, adaptation, and evolution in the context of agent systems and autonomous agents.
Computer Safety, Reliability, and Security
This book constitutes the refereed proceedings of the 25th International Conference on Computer Safety, Reliability, and Security, SAFECOMP 2006. The 32 revised full papers were carefully reviewed and selected from 101 submissions. Topical sections include systems of systems, security and survivability analysis, nuclear safety and application of standards, formal approaches, networks dependability, coping with change and mobility, safety analysis and assessment, 6th FP integrated project DECOS, and modelling.
Adaptive Agents and Multi-Agent Systems
Adaptive Agents and Multi-Agent Systems is an emerging and exciting interdisciplinary area of research and development involving artificial intelligence, computer science, software engineering, and developmental biology, as well as cognitive and social science. This book surveys the state of the art in this emerging field by drawing together thoroughly selected reviewed papers from two related workshops; as well as papers by leading researchers specifically solicited for this book. The articles are organized into topical sections on - learning, cooperation, and communication - emergence and evolution in multi-agent systems - theoretical foundations of adaptive agents
The Evolution of Language
This volume comprises refereed papers and abstracts of the 10th International Conference on the Evolution of Language (EVOLANGX), held in Vienna on 14–17th April 2014. As the leading international conference in the field, the biennial EVOLANG meeting is characterised by an invigorating, multidisciplinary approach to the origins and evolution of human language, and brings together researchers from many subject areas, including anthropology, archaeology, biology, cognitive science, computer science, genetics, linguistics, neuroscience, palaeontology, primatology and psychology. For this 10th conference, the proceedings will include a special perspectives section featuring prominent researche...
Inductive Logic Programming
This book constitutes the refereed conference proceedings of the 29th International Conference on Inductive Logic Programming, ILP 2019, held in Plovdiv, Bulgaria, in September 2019. The 11 papers presented were carefully reviewed and selected from numerous submissions. Inductive Logic Programming (ILP) is a subfield of machine learning, which originally relied on logic programming as a uniform representation language for expressing examples, background knowledge and hypotheses. Due to its strong representation formalism, based on first-order logic, ILP provides an excellent means for multi-relational learning and data mining, and more generally for learning from structured data.
Inductive Logic Programming
This book constitutes the refereed conference proceedings of the 30th International Conference on Inductive Logic Programming, ILP 2021, held in October 2021. Due to COVID-19 pandemic the conference was held virtually. The 16 papers and 3 short papers presented were carefully reviewed and selected from 19 submissions. Inductive Logic Programming (ILP) is a subfield of machine learning, which originally relied on logic programming as a uniform representation language for expressing examples, background knowledge and hypotheses. Due to its strong representation formalism, based on first-order logic, ILP provides an excellent means for multi-relational learning and data mining, and more generally for learning from structured data.
