The Fourth International Conference on Knowledge Capture was held October 28-31, 2007 in Whistler, British Columbia. K-CAP 2007 included two invited talks, technical papers, posters, and demonstrations. Topics included knowledge engineering and modeling methodologies, knowledge engineering and the semantic web, mixed-initiative planning and decision-support tools, acquisition of problem-solving knowledge, knowledge-based markup techniques, knowledge extraction systems, knowledge acquisition tools, and advice taking systems.
Balduccini, Marcello (Eastman Kodak Company) | Baral, Chitta (Arizona State University) | Brodaric, Boyan (Geological Survey of Canada) | Colton, Simon (Imperial College, London) | Fox, Peter (National Center for Atmospheric Research) | Gutelius, David (SRI International) | Hinkelmann, Knut (University of Applied Sciences Northwestern Switzerland) | Horswill, Ian (Northwestern University) | Huberman, Bernardo (HP Labs) | Hudlicka, Eva (Psychometrix Associates) | Lerman, Kristina (USC Information Sciences Institute) | Lisetti, Christine (Florida International University) | McGuinness, Deborah L. (Rensselaer Polytechnic Institute) | Maher, Mary Lou (National Science Foundation) | Musen, Mark A. (Stanford University) | Sahami, Mehran (Stanford University) | Sleeman, Derek (University of Aberdeen) | Thönssen, Barbara (University of Applied Sciences Northwestern Switzerland) | Velasquez, Juan D. (MIT CSAIL) | Ventura, Dan (Brigham Young University)
The titles of the eight symposia were as follows: (1) AI Meets Business Rules and Process Management, (2) Architectures for Intelligent Theory-Based Agents, (3) Creative Intelligent Systems, (4) Emotion, Personality, and Social Behavior, (5) Semantic Scientific Knowledge Integration, (6) Social Information Processing, (7) Symbiotic Relationships between Semantic Web and Knowledge Engineering, (8) Using AI to Motivate Greater Participation in Computer Science The goal of the AI Meets Business Rules and Process Management AAAI symposium was to investigate the various approaches and standards to represent business rules, business process management and the semantic web with respect to expressiveness and reasoning capabilities. The Semantic Scientific Knowledge Symposium was interested in bringing together the semantic technologies community with the scientific information technology community in an effort to build the general semantic science information community. The Social Information Processing's goal was to investigate computational and analytic approaches that will enable users to harness the efforts of large numbers of other users to solve a variety of information processing problems, from discovering high-quality content to managing common resources. The purpose of the Using AI to Motivate Greater Participation in Computer Science symposium was to identify ways that topics in AI may be used to motivate greater student participation in computer science by highlighting fun, engaging, and intellectually challenging developments in AI-related curriculum at a number of educational levels.
Carbonell, Jaime G., Sleeman, Derek
Two closely related aspects of artificial intelligence that have received comparatively little attention in the recent literature are research methodology, and the analysis of computational techniques that span multiple application areas. We believe both issues to be increasingly significant as Artificial Intelligence matures into a science and spins off major application efforts. Similarly, awareness of research methodology issues can help plan future research buy learning from past successes and failures. We view the study of research methodology to be similar to the analysis of operational AI techniques, but at a meta-level; that is, research methodology analyzes the techniques and methods used by the researchers themselves, rather than their programs, to resolve issues of selecting interesting and tractable problems to investigate, and of deciding how to proceed with their investigations.
In this article we discuss a method for learning useful conditions on the application of operators during heuristic search. Since learning is not attempted until a complete solution path has been found for a problem, credit for correct moves and blame for incorrect moves is easily assigned. We review four learning systems that have incorporated similar techniques to learn in the domains of algebra, symbolic integration, and puzzle-solving. We conclude that the basic approach of learning from solution paths can be applied to any situation in which problems can be solved by sequential search.