Teaching artificial intelligence (AI) is challenging. It is a fast moving field and therefore difficult to keep people updated with the state-of-the-art. Educational offerings for students are ever increasing, beyond university degree programs where AI education traditionally lay. In this paper, we present an experience report of teaching an AI course to business executives in the United Arab Emirates (UAE). Rather than focusing only on theoretical and technical aspects, we developed a course that teaches AI with a view to enabling students to understand how to incorporate it into existing business processes. We present an overview of our course, curriculum and teaching methods, and we discuss our reflections on teaching adult learners, and to students in the UAE.

In this paper we revisit the problem of generating query plans using AI automated planning with a view to leveraging significant recent advances in state-of-the-art planning techniques. Our efforts focus on the specific problem of cost-based join-order optimization for conjunctive relational queries, a critical component of production-quality query optimizers. We characterize the general query-planning problem as a delete-free planning problem, and query plan optimization as a context-sensitive cost-optimal planning problem. We propose algorithms that generate high-quality query plans, guaranteeing optimality under certain conditions. Our approach is general, supporting the use of a broad suite of domain-independent and domain-specific optimization criteria. Experimental results demonstrate the effectiveness of AI planning techniques for query plan generation and optimization.

We develop a novel absorption technique for large collections of factual assertions about individual objects. These assertions are commonly accompanied by implicit background knowledge and form a knowledge base. Both the assertions and the background knowledge are expressed in a suitable language of Description Logic and queries over such knowledge bases can be expressed as assertion retrieval queries. The proposed absorption technique significantly improves the performance of such queries, in particular in cases where a large number of object features are known for the objects represented in such a knowledge base. In addition to the absorption technique we present the results of a preliminary experimental evaluation that validates the efficacy of the proposed optimization.