Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Artificial intelligence --- Artificial intelligence.

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Artificial intelligence --- Artificial intelligence.

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

The 18th conference of the Canadian Society for the Computational Study of Intelligence (CSCSI) continued the success of its predecessors. This set of - pers re?ects the diversity of the Canadian AI community and its international partners. AI 2005 attracted 135 high-quality submissions: 64 from Canada and 71 from around the world. Of these, eight were written in French. All submitted papers were thoroughly reviewed by at least three members of the Program Committee. A total of 30 contributions, accepted as long papers, and 19 as short papers are included in this volume. We invited three distinguished researchers to give talks about their current research interests: Eric Brill from Microsoft Research, Craig Boutilier from the University of Toronto, and Henry Krautz from the University of Washington. The organization of such a successful conference bene?ted from the coll- oration of many individuals. Foremost, we would like to express our apprec- tion to the Program Committee members and external referees, who provided timely and signi?cant reviews. To manage the submission and reviewing process we used the Paperdyne system, which was developed by Dirk Peters. We owe special thanks to Kellogg Booth and Tricia d'Entremont for handling the local arrangementsandregistration.WealsothankBruceSpencerandmembersofthe CSCSI executive for all their e?orts in making AI 2005 a successful conference.

Artificial intelligence. --- Artificial Intelligence.

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

As the number of connected devices rapidly increases, largely thanks to uptake of IoT technologies, there is significant stimulus to enable opportunistic interactions between different systems that encounter each other at run time. However, this is complicated by diversity in IoT technologies and implementation details that are not known in advance. To achieve such unplanned interactions, we use the concept of a holon to represent a system's services and requirements at a high level. A holon is a self-describing system that appears as a whole when viewed from above whilst potentially comprising multiple sub-systems when viewed from below. In order to realise this world view and facilitate opportunistic system interactions, we propose the idea of using ontologies to define and program a holon. Ontologies offer the ability to classify the concepts of a domain, and use this formalised knowledge to infer new knowledge through reasoning. In this paper, we design a holon ontology and associated code generation tools. We also explore a case study of how programming holons using this approach can aid an IoT system to self-describe and reason about other systems it encounters. As such, developers can develop system composition logic at a high-level without any preconceived notions about low-level implementation details. © 2020 IEEE.

Artificial intelligence. --- Artificial intelligence

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Artificial intelligence. --- Artificial intelligence