l also pass along a limited number of carefully edited messages derived from Arpanet-distributed position postings and similar material. I shall take considerable liberties with the arrangement and format of the original texts without inserting [...] annotations, and shall suppress explicit solicitations (although the unofficial custom on the Arpanet has been to permit such commercialism by academic institutions). I shall also try to avoid repeating boilerplate lab descriptions that AIList has already published. Nonacademic institutions may [occasionally] submit similar promotional material so long as Arpanet standards are respected. My decision to distribute such material will be based solely on interest to the general AIList reader, not on the potential benefit of filling AI-related positions. Please don't dump all of your archived blurbs on me today or tomorrow; we have plenty of time. I should like to see the submissions dribble in over a period of >>years<<, so wait until an appropriate opportunity (e.g., when a related discussion comes up in the digest or when your dissertation goes to press). Eventually we shall reach a steady state with material being submitted as it is produced for other purposes. I anticipate that these news items will require more editing than normal submissions, particularly the lab reports derived from promotional material. You can simplify my job if you provide a meaningful "Subject:" line such as the "Seminar - ..." headers I have been distributing. Keywords such as "Abstract" and "Project" should be followed by a very short title that readers can use to screen the messages. The submissions themselves should be concise and closely related to the interests of the AIList readership. (The enthusiasm of your colleagues, bosses, and sponsors for your 200 papers on educational parapsychology may not be shared by a general audience.) Please include sufficient "Contact:" information (e.g., address and phone number) that I shall not have to help readers wanting further information. I shall be fairly strict about screening material I consider marginal, and should appreciate your consideration in minimizing this unpleasant part of my responsibilities. Rejections will be handled by "form letter", and generally will not include detailed justifications. I hope that few will interpret such a notice as an invitation to debate or the opening round in a series of negotiations. Comments to AIList-Request@SRI-AI on this new policy will be helpful in determining whether this experiment should be modified or discontinued. (Your silence will be interpreted as lack of disapproval.) I shall keep list readers informed of any significant trends in the expressed opinions. -- Dr. Kenneth I. Laws AIList Moderator ------------------------------ Date: Tue 24 Jul 84 12:36:44-PDT From: Juanita Mullen Subject: Seminar - Learning State Variables [Forwarded from the Stanford SIGLUNCH distribution by Laws@SRI-AI.] DATE: Friday, July 27, 1984 LOCATION: Chemistry Gazebo, between Physical & Organic Chemistry TIME: 12:05 SPEAKER: Tom Dietterich Heuristic Programming Project Stanford University TOPIC: Learning About Systems That Contain State Variables It is difficult to learn about systems that contain state variables when those variables are not directly observable. This talk formalizes this learning problem and presents a method called the iterative extension method for solving it. In the iterative extension method, the learner gradually constructs a partial theory of the state-containing system. At each stage, the learner applies this partial theory to interpret the I/O behavior of the system and obtain additional constraints on the structure and values of its state variables. These constraints can be applied to extend the partial theory by hypothesizing additional internal state variables. The improved theory can then be applied to interpret more complex I/O behavior. This process continues until a theory of the entire system is obtained. Several sufficient conditions for the success of