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) …
Moving to the right, credit card fraud detection and spam filtering have higher levels of predictability, but current-day systems still generate significant numbers of false positives and false negatives. Consider two of the relatively higher predictability problems mentioned earlier--spam filtering and driverless cars. In contrast, above the frontier, we find that even the best current diabetes prediction systems still generate too many false positives and negatives, each with a cost that is too high to justify purely automated use. On the other hand, the availability of genomic and other personal data could improve prediction accuracy dramatically (long orange horizontal arrow) and create trustworthy robotic healthcare professionals in the future.
The Fourth International Conference on Informatics in Control, Automation, and Robotics (ICINCO 2007) was held at the University of Angers from 9 through 12 May 2007. This conference sought to bring together researchers, engineers, and practitioners interested in the application of informatics to control, automation, and robotics, with an emphasis on intelligent systems and various AI technologies, such as expert systems, evolutionary computing, neural networks, and others, in connection to signal processing, systems modeling, and control. Beside the presentation of papers addressing these general topics, several specific themes were discussed during the conference in specialized forums, including special sessions, panels, and workshops, as described in this report.
Robotics is a remarkable domain that may be successfully employed in the classroom both to motivate students to tackle hard AI topics and to provide students experience applying AI representations and algorithms to real-world problems. We show how the robot obstacle-detection problem can motivate learning neural networks and Bayesian networks. We also show how the robot-localization problem can motivate learning how to build complete solutions based on particle filtering. We believe that expanding handson active learning to additional AI classrooms provides value both to the students and to the future of the field itself.
In this article, I describe agent-centered search (also called real-time search or local search) and illustrate this planning paradigm with examples. Agent-centered search methods interleave planning and plan execution and restrict planning to the part of the domain around the current state of the agent, for example, the current location of a mobile robot or the current board position of a game. These methods can execute actions in the presence of time constraints and often have a small sum of planning and execution cost, both because they trade off planning and execution cost and because they allow agents to gather information early in nondeterministic domains, which reduces the amount of planning they have to perform for unencountered situations. Agent-centered search methods have been applied to a variety of domains, including traditional search, strips-type planning, moving-target search, planning with totally and partially observable Markov decision process models, reinforcement learning, constraint satisfaction, and robot navigation.
This article describes a milestone in our research efforts toward the real robot competition in RoboCup. We participated in the middle-size league at RoboCup-97, held in conjunction with the Fifteenth International Joint Conference on Artificial Intelligence in Nagoya, Japan. The most significant features of our team, TRACKIES, are the application of a reinforcement learning method enhanced for real robot applications and the use of an omnidirectional vision system for our goalie that can capture a 360-degree view at any instant in time. The method and the system used are shown with competition results.
Giving robots the ability to operate in the real world has been, and continues to be, one of the most difficult tasks in AI research. Their research has been focused on using adaptive, vision-based systems to increase the driving performance of the Navlab line of on-road mobile robots. This research has led to the development of a neural network system that can learn to drive on many road types simply by watching a human teacher. This article describes the evolution of this system from a research project in machine learning to a robust driving system capable of executing tactical driving maneuvers such as lane changing and intersection navigation.