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) …
The workshop focused on possible solutions to both technical and organizational challenges to mobility and manipulation research. This article presents the highlights of that discussion along with the content of the accompanying exhibits. Fortunately, these applications can be successful through simple repetitive behaviors or remote human operation. However, useful autonomy needed for operation in general situations requires advanced mobility and manipulation. Opening doors, retrieving specific items, and maneuvering in cluttered environments are required for useful deployment in anything but the most controlled environment. The mobile manipulation skills necessary to perform tasks in arbitrary environments may not result from current approaches to robotics and AI. Moving toward true robot autonomy may require new paradigms, hardware, and ways of thinking. The goal of the AAAI 2008 Workshop on Mobility and Manipulation was not only to demonstrate current research successes to the AAAI community but also to road-map future mobility and manipulation challenges that create synergies between artificial intelligence and robotics. The half-day workshop included both a session on the exhibits and a panel discussion. The panel consisted of five prominent researchers who led a discussion of future directions for mobility and manipulation research.
Robots in the Robot Host competition, part of the Eighteenth National Conference on Artificial Intelligence (AAAI-2002) Mobile Robot Competition faced two challenges: (1) a serving task that was similar to the Hors d'Oeuvres, Anyone? Both tasks required moving carefully among people, politely offering them information or hors d'oeuvres, recognizing when the people are making a request, and answering the request. Both tasks required moving carefully among people, politely offering them information or hors d'oeuvres, recognizing when the people are making a request, and answering the request. Celebrating the sixth year for the Robot Host competition, a new task, the robot information kiosk, was added. Three entries took on the challenge of creating host robots who can both offer hors d'oeuvres to attendees of the robot exhibition and can serve as a source of information to attendees during breaks in the program.
The Eighteenth National Conference on Artificial Intelligence (AAAI-2002) Robot Challenge is part of an annual series of robot challenges and competitions. It is intended to promote the development of robot systems that interact intelligently with humans in natural environments. The Challenge task calls for a robot to attend the AAAI conference, which includes registering for the conference and giving a talk about itself. In this article, we review the task requirements, introduce the robots that participated at AAAI-2002 and describe the strengths and weaknesses of their performance. The purpose of the challenge is to promote the development of robot systems that interact intelligently with humans in natural environments.
WE NEED BETTER STANDARDS FOR AI RESEARCH The state of the art in any science includes the criteria for evaluating research. Like every other aspect of the science, it has to be developed. In my previous message I grumbled about there being insufficient basic research, but one of the reasons for this is the difficulty of evaluating whether a piece of research has made basic progress. It seems that evaluation should be based on the kind of advance the research purports to be. I haven't been able to develop a complete set of criteria, but here are some considerations.
From an AI perspective, finding effective treatments for cancer is a high-dimensional search problem characterized by many molecularly distinct cancer subtypes, many potential targets and drug combinations, and a dearth of highquality data to connect molecular subtypes and treatments to responses. The broadening availability of molecular diagnostics and electronic medical records presents both opportunities and challenges to apply AI techniques to personalize and improve cancer treatment. We discuss these in the context of Cancer Commons, a "rapid learning" community where patients, physicians, and researchers collect and analyze the molecular and clinical data from every cancer patient and use these results to individualize therapies. Research opportunities include adaptively planning and executing individual treatment experiments across the whole patient population, inferring the causal mechanisms of tumors, predicting drug response in individuals, and generalizing these findings to new cases. The goal is to treat each patient in accord with the best available knowledge and to continually update that knowledge to benefit subsequent patients.
Picture tactile feedback and situated computing. That's the year when a revolving cadre of scientists began work on the problem of predicting the outcome of the spin of a roulette wheel. Although lacking the societal import of, say, predicting cancer in a patient, or even poison in a mushroom, predicting roulette seems on the face of it of even greater difficulty. The game itself is designed in every way for unpredictability. The problem is at its core a machine learning problem with a direct physical basis.
I view the World Wide Web as an information food chain. The maze of pages and hyperlinks that comprise the Web are at the very bottom of the chain. The maze of pages and hyperlinks that comprise the Web are at the very bottom of the chain. Today's Web is populated by a panoply of primitive but popular information services. Is the Web challenge a distraction from our long-term goal of understanding intelligence and building intelligent agents?
The AI field needs major breakthroughs in its thinking to achieve continuous, sensory-gathered, machine learning from the environment on unlimited subjects. The way to motivate such dramatic progress is to articulate and endorse research goals for machine behavior so ambitious that limited-domain, problemsolving knowledge representation methods are disqualified at the outset, thus forcing ourselves to produce valuable new "thoughtware". After exploring why the tendency to associate intelligence with problem-solving may be a mental roadblock to further progress in AI science, some preliminary thinking tools are introduced more suitable for sensory learning machine rcscarch These include lifelong sensorimotor data streams, representation as a symbolic recording process, knowledge transmission, and the totality of knowledge. One day, Professor Nokemoff, a distinguished AI professor of robotics at a leading institution, called in a grad student to hand out a thesis project. "Build me a robot that can sort two different parts coming down a conveyor belt.
The Robot World Cup Soccer Games and Conferences (RoboCup) are a series of competitions and events designed to promote the full integration of AI and robotics research. Following the first RoboCup, held in Nagoya, Japan, in 1997, RoboCup-98 was held in Paris from 2-9 July, overlapping with the real World Cup soccer competition. RoboCup-98 included competitions in three leagues: (1) the simulation league, (2) the real robot small-size league, and (3) the real robot middle-size league. It was organized by University of Paris-VI and the Centre National de la Recherche Scientifique and was sponsored by Sony Corporation, NAMCO Limited, and SUNX Limited, with official balls for the middle-size league supplied by Molten Corporation. Over 15,000 people watched the games, and over 120 international media (such as CNN, ABC, NHK, and TV-Aich) and prominent scientific magazines covered the competition.
Now completing its first year, the High-Performance Knowledge Bases Project promotes technology for developing very large, flexible, and reusable knowledge bases. The project is supported by the Defense Advanced Research Projects Agency and includes more than 15 contractors in universities, research laboratories, and companies. Programs lack knowledge about the world sufficient to understand and adjust to new situations as people do. Consequently, programs have been poor at interpreting and reasoning about novel and changing events, such as international crises and battlefield situations. These problems are more open ended than chess.