General Issues What is AI all about? In general, I see two possible answers to this question. First, AI can be seen as a modern methodological tool now being used in the ancient enterprise of the study of mind. It also usually means getting a machine to do what previously only humans have done before (rather than simply improving existing techniques). There are really only three reasons to "do" izI From the scientific point of view, you should do 2I because you are interested in the mind From the technological point of view, you should do AI because you The dispute between these formalists, and more intuitive researchers, has been referred to by me (elsewhere) as the neat/scruffy distinction.

To coordinate, intelligent agents might need to know something about themselves, about each other, about how others view themselves and others, about how others think others view themselves and others, and so on. Taken to an extreme, the amount of knowledge an agent might possess to coordinate its interactions with others might outstrip the agent's limited reasoning capacity (its available time, memory, and so on). Much of the work in studying and building multiagent systems has thus been devoted to developing practical techniques for achieving coordination, typically by limiting the knowledge available to, or necessary for, agents. This article categorizes techniques for keeping agents suitably ignorant so that they can practically coordinate and gives a selective survey of examples of these techniques for illustration. Certainly, people who know much (or think they know much) are sometimes subject to cockiness, confusion, paralysis, resignation, or other unpleasant states.

Among the difficulties in evaluating AItype medical diagnosis systems are: the intermediate conclusions of the AI system need to be looked at in addition to the "final" answer; the "superhuman human" fallacy must be resisted; both pro-and anti-computer biases during evaluation must be guarded against; and methods for estimating how the approach will scale upwards to larger domains are needed We propose a type of Turing test for the evaluation problem, designed to provide some protection against the problems listed above We propose to measure both the accuracy of diagnosis and the structure of reasoning, the latter with a view to gauging how well the system will scale up A staple of many of the evaluations of AI systems that have so far been conducted (Colby, Hilf, Weber, 81 Kraemer, 1972; Yu et al, 1979) is a central idea from a well-known proposal to evaluate AI systems: The Turing Test (Turing, 1963) The meat of the idea is to see if a neutral observer, given a set of performances on a task, some by a machine and others by humans, but unlabelled as to authorship, could identify, better than chance, which were machine and which were human-produced. Note that this really attempts to answer the question, "DO we know how to design a machine to perform a task which until now required human intelligence?", The latter question subsumes the former in a sense: because the machine not performing well in comparison to a human would presumably increase the cost significantly. In this paper I follow tradition and consider the evaluation of AI systems for medical diagnosis from the viewpoint of the first question above. The proposed procedure is also a variant of Turing's Test.

Introduction This report summarizes our experience in building PIES, a knowledge-based system that diagnoses problems in semiconductor fabrication processes by analyzing parametric test data. Parametric measurement, which is performed on test circuits at the end of a complicated semiconductor fabrication process, provides semiconductor engineers with early information to monitor the "health' ' of the overall fabrication process. Typically, hundreds of measurements are made on each wafer. The problem is to reduce the resulting ream of data to a concise summary of the process status: whether the process is functioning correctly and, if not, what the nature and cause of the abnormality is. Currently, this interpretation taskis performed by a group of semiconductor specialists known as failure-analysis or yield-enhancement engineers and routinely consumes a large portion of their time. It is critical that problems be identified quickly to avoid a major operational loss.

The thirteenth AAAI Mobile Robot Competition and Exhibition was once again collocated with AAAI-2204, in San Jose, California. As in previous years, the robot events drew competitors from both academia and industry to showcase state-ofthe-art mobile robot software and systems in four organized events. The primary purpose of the Mobile Robot Competition and Exhibition is to bring together researchers and students from academe and industry to showcase the latest state-of-the-art mobile robot capabilities. This year saw the return of the Rescue Robot Competition, the Mobile Robot Exhibition, and the Robot Challenge, and the addition of a new event, the Open Interaction Event. For the fifth time, the Rescue Robot Competition was run at AAAI, helping raise awareness of the unique challenges involved in urban search and rescue (USAR) operations.

To construct a perpetual self-aware cognitive agent that can continuously operate with independence, an introspective machine must be produced. To assemble such an agent, it is necessary to perform a full integration of cognition (planning, understanding, and learning) and metacognition (control and monitoring of cognition) with intelligent behaviors. The failure to do this completely is why similar, more limited efforts have not succeeded in the past. I outline some key computational requirements of metacognition by describing a multistrategy learning system called Meta-AQUA and then discuss an integration of Meta-AQUA with a nonlinear state-space planning agent. I show how the resultant system, INTRO, can independently generate its own goals, and I relate this work to the general issue of self-awareness by machine.

The eighteenth annual International Workshop on Principles of Diagnosis was held in Nashville, Tennessee, May 29-31, 2007. Papers presented at the workshop covered a variety of theories, principles, and computational techniques for diagnosis, monitoring, testing, reconfiguration, fault-adaptive control, and repair of complex systems. Before deployment they are subjected to strict testing and validation. Although these procedures reduce the likelihood of initial system failures, degradation and faults in system components still occur because of wear and tear from sustained operations. Industry sources, service agencies, and the military report that down time, due to maintenance and repairs of equipment, is still a significant cost of daily operations.

A collaborative effort between the White House Office of Media Affairs, the Artificial Intelligence Laboratory at the Massachusetts Institute of Technology (MIT), and others quickly created a workable framework for wide-scale distribution of a stream of daily documents originating from the Executive Office of the President. The document stream includes daily press briefings, speeches by the President and other officials, backgrounders, and proclamations. In addition, the stream of released information includes special documents such as the National Performance Review's reports on reinventing government, the proposed healthcare reform legislation, and the yearly budgets. The Intelligent Information Infrastructure Project at the MIT Artificial Intelligence Laboratory created an information distribution server that functions as the focal point of the distribution chain. Documents are released from the Executive Office of the President through this system; they are sent from this system to a variety of archiving and retrieval systems around the country, most online services (for example, Compuserve, America Online), about 4000 direct subscribers to the MIT server, and a variety of other servers that further redistribute the documents.

This is a summary of the Workshop on Planning that was sponsored by the Defense Advanced Research Project Agency and held in Santa Cruz, California, on October 21-23, 1987. The purpose of this workshop was to identify and explore new directions for research in planning. For the purposes of this article, a planner is a program that controls one or more devices capable of carrying out actions in the real world in order to achieve some definite purpose. A strategic planner is capable of anticihe workshop was organized into five sessions. Each session was intended to examine some aspect of planning research or point directions toward future work.

With the exception of plan repair, important topics related to the use of plans (robust execution, reactivity, monitoring, evaluation) have received significantly less consideration. In realistic domains, however, plan generation is only a small component of the overall package. In particular, plans must be updated in response to new information and requirements in a timely fashion to ensure that they remain viable and relevant. Plan execution involves more than blind adherence to previously generated plans. Rather, run-time decisions are made to adapt, initiate, or abandon plans and activities in response to current considerations within the operating environment.