Logical formalization of multimedia interpretation
Knowledge-driven multimedia information extraction and ontology evolution
Logic, probability and computation: foundations and issues of statistical relational AI
LPNMR'11 Proceedings of the 11th international conference on Logic programming and nonmonotonic reasoning
A multi-agent reinforcement learning with weighted experience sharing
ICIC'11 Proceedings of the 7th international conference on Advanced Intelligent Computing Theories and Applications: with aspects of artificial intelligence
Folkview: a multi-agent system approach to modeling folksonomies
UMAP'11 Proceedings of the 19th international conference on Advances in User Modeling
The current state of SKOS vocabularies on the web
ESWC'12 Proceedings of the 9th international conference on The Semantic Web: research and applications
Building a social multi-agent system simulation management toolbox
Proceedings of the 6th Balkan Conference in Informatics
Reducing the solution space of optimal task scheduling
Computers and Operations Research
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Recent decades have witnessed the emergence of artificial intelligence as a serious science and engineering discipline. Artificial Intelligence: Foundations of Computational Agents is a textbook aimed at junior to senior undergraduate students and first-year graduate students. It presents artificial intelligence (AI) using a coherent framework to study the design of intelligent computational agents. By showing how basic approaches fit into a multidimensional design space, readers can learn the fundamentals without losing sight of the bigger picture. The book balances theory and experiment, showing how to link them intimately together, and develops the science of AI together with its engineering applications. Although structured as a textbook, the book's straightforward, self-contained style will also appeal to a wide audience of professionals, researchers, and independent learners. AI is a rapidly developing field: this book encapsulates the latest results without being exhaustive and encyclopedic. It teaches the main principles and tools that will allow readers to explore and learn on their own. The text is supported by an online learning environment, AIspace, http://aispace.org, so that students can experiment with the main AI algorithms plus problems, animations, lecture slides, and a knowledge representation system, AIlog, for experimentation and problem solving.