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Modern machine learning and artificial intelligence (AI) have revolutionized many disciplines but have only minimally impacted the practice of architecture. We discuss design challenges that architects face, illustrate how AI can meet them, and describe three areas where progress is needed to ignite the AI revolution in architecture. [ABSTRACT FROM AUTHOR]

Enabling Subject Matter Experts (SMEs) to formulate knowledge without the intervention of Knowledge Engineers (KEs) requires providing SMEs with methods and tools that abstract the underlying knowledge representation and allow them to focus on modeling activities. Bridging the gap between SME-authored models and their representation is challenging, especially in the case of complex knowledge types like processes, where aspects like frame management, data, and control flow need to be addressed. In this paper, we describe how SME-authored process models can be provided with an operational semantics and grounded in a knowledge representation language like F-logic to support process-related reasoning. The main results of this work include a formalism for process representation and a mechanism for automatically translating process diagrams into executable code following such formalism. From all the process models authored by SMEs during evaluation 82 percent were well formed, all of which executed correctly. Additionally, the two optimizations applied to the code generation mechanism produced a performance improvement at reasoning time of 25 and 30 percent with respect to the base case, respectively. [ABSTRACT FROM PUBLISHER]

This editorial explains that the articles in this special issue of "IEEE Intelligent Systems" deal with large-scale distributed software systems with predictable dependability properties, and discuss an emerging and exciting approach to building these most challenging kinds of systems. Three main sources of change have converged to make the topic of dependable distributed systems of great current interest: the growing demand for dependability; a revolutionary change in our models of computation; and the appearance of a potential solution. The articles in this issue explore the interplay between the promises of flexible, agent-based computation and the desire for dependability guarantees.

Focuses on the theoretical and tool-creation efforts to answer the practicality of development of sophisticated agents for every applications. Factors to be considered in building the sophisticated agent-based systems; Appreciation of the leading-edge developments in agent theory; Agent's capability to coordinate with other agents. INSETS: Policies and exception handling: a fence and an ambulance.;Pragmatics in agent communication.;Agent-management tool..

The article discusses the 2006 International Semantic Web Conference held in Athens, Georgia, and the overlap between Web 2.0 and the Semantic Web. The panel considered the degree to which Semantic Web technical capabilities can support Web 2.0 applications' semantic requirements, Semantic Web technology can support and enhance Web 2.0 user ecosystems, and Semantic Web design and implementation practices can complement Web 2.0 business models. Web 2.0-style applications and companies include Flickr, Wikipedia, YouTube, and others. The value of Semantic Web inference in Web 2.0 contexts includes basic subsumption reasoning that is able to extend and regularize tag systems, suggest useful cross-links in blogs and wikis, and increase precision and recall in social-network search.

The article focuses on innovations in semantic technologies, such as knowledge representation and reasoning (KR) technologies. This field has been completely transformed as a result of usable Web-oriented KR languages, coupled with the availability of Web-scale data and the massive success of search engines. These technological innovations have made it possible to reasonably predict the type and degree of impact that semantic technologies might have in the near future. Also discussed is why artificial intelligence was limited for several years.