If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Robotics and artificial intelligence are closely related areas though their research interests and topics diverted in past. Recently, the progress in both areas brings robotics and artificial intelligence together again and higher-level deliberative functions such as action planning are being integrated into usually reactive robotics systems to increase their autonomy as well as to simplify their control. This special track addresses research results on the border between robotics (and general intelligent agents) and AI techniques with the aim of bridging the enlarging gap between the areas.
Anderson, Monica (University of Alabama) | Barták, Roman (Charles University) | Brownstein, John S. (Boston Children's Hospital, Harvard University) | Buckeridge, David L. (McGill University) | Eldardiry, Hoda (Palo Alto Research Center) | Geib, Christopher (Drexel University) | Gini, Maria (University of Minnesota) | Isaksen, Aaron (New York University) | Keren, Sarah (Technion University) | Laddaga, Robert (Vanderbilt University) | Lisy, Viliam (Czech Technical University) | Martin, Rodney (NASA Ames Research Center) | Martinez, David R. (MIT Lincoln Laboratory) | Michalowski, Martin (University of Ottawa) | Michael, Loizos (Open University of Cyprus) | Mirsky, Reuth (Ben-Gurion University) | Nguyen, Thanh (University of Michigan) | Paul, Michael J. (University of Colorado Boulder) | Pontelli, Enrico (New Mexico State University) | Sanner, Scott (University of Toronto) | Shaban-Nejad, Arash (University of Tennessee) | Sinha, Arunesh (University of Michigan) | Sohrabi, Shirin (IBM T. J. Watson Research Center) | Sricharan, Kumar (Palo Alto Research Center) | Srivastava, Biplav (IBM T. J. Watson Research Center) | Stefik, Mark (Palo Alto Research Center) | Streilein, William W. (MIT Lincoln Laboratory) | Sturtevant, Nathan (University of Denver) | Talamadupula, Kartik (IBM T. J. Watson Research Center) | Thielscher, Michael (University of New South Wales) | Togelius, Julian (New York University) | Tran, So Cao (New Mexico State University) | Tran-Thanh, Long (University of Southampton) | Wagner, Neal (MIT Lincoln Laboratory) | Wallace, Byron C. (Northeastern University) | Wilk, Szymon (Poznan University of Technology) | Zhu, Jichen (Drexel University)
Domain-independent planning requires only to specify planning problems in a standard language (e.g. PDDL) in order to utilise planning in some application. Despite a huge advancement in domain-independent planning, some relatively-easy problems are still challenging for existing planning engines. Such an issue can be mitigated by specifying Domain Control Knowledge (DCK) that can provide better guidance for planning engines. In this paper, we introduce transition-based DCK, inspired by Finite State Automata, that is efficient as demonstrated empirically, planner-independent (can be encoded within planning problems) and easy to specify.