AIList-REQUEST@SRI-AI.ARPA (AIList Moderator Kenneth Laws) (11/15/85)
AIList Digest Friday, 15 Nov 1985 Volume 3 : Issue 169 Today's Topics: Seminars - Question-Answering Systems (UPenn) & Bill, the Othello Program (CMU) & Information-Based Complexity (CSLI) & Multilisp (MIT) & Gazing in Theorem Proving (MCC) & Deductive Design Synthesis (SRI) & Probabilistic Propositional Logic (Buffalo) ---------------------------------------------------------------------- Date: Mon, 11 Nov 85 16:04 EST From: Tim Finin <Tim%upenn.csnet@CSNET-RELAY.ARPA> Subject: Seminar - Question-Answering Systems (UPenn) Forwarded From: Bonnie Webber <Bonnie@UPenn> on Mon 11 Nov 1985 at 10:42 Subj: Seminar in Natural Language Processing CIS679 - SEMINAR IN NATURAL LANGUAGE PROCESSING SPRING 1986 The topic for this term is question-answering systems, with particular attention to the type of information included in response to a question, instead of or in addition to an answer. We will look at the role of plan recognition and planning in formulating cooperative responses, as well as considering how to circumscribe the reasoning expected of a respondent. Response components of particular interest will be information intended to explain or justify answers, information intended to point out and/or correct misconceptions, and information intended to further the questioner's goals. Instructors: Joshi/Webber Time: MW 3-4:30 ------------------------------ Date: 13 Nov 85 12:54:10 EST From: Kai-Fu.Lee@SPEECH2.CS.CMU.EDU Subject: Seminar - Bill, the Othello Program (CMU) BILL : THE OTHELLO PROGRAM THAT BEAT IAGO Kai-Fu Lee Friday November 15, 1985 Wean Hall 5409 4:30 PM - 6:00 PM BILL is an Othello program written by myself and Sanjoy Mahajan. It was entered in the Waterloo Othello Tournament on November 9, and captured first place with a 4-0 record. In two unofficial games, it defeated IAGO, the world champion othello program developed at CMU in 1980-2. Most, if not all, othello programs use one of two types of evaluation functions: (1) knowledge-intensive but slow (such as IAGO), or (2) knowledge-deficient but fast (such as most programs at Waterloo). BILL succeeds through its use of a knowledge-intensive, yet extremely efficient evaluation function. It is further enhanced by an iterative deepening zero-window alpha-beta procedure, a hash table, a linked-move killer table, a two-phase end-game search, and thinking on opponent's time. In this talk, I will first discuss othello strategies. Next, I will describe Bill, and analyze its games in Waterloo and against IAGO. Finally, we will demonstrate BILL by playing it against the audience. ------------------------------ Date: Wed 13 Nov 85 17:05:26-PST From: Emma Pease <Emma@SU-CSLI.ARPA> Subject: Seminar - Information-Based Complexity (CSLI) [Excerpted from the CSLI Newsletter by Laws@SRI-AI.] An Introduction to Information-based Complexity J. F. Traub Computer Science Department, Columbia University THURSDAY, November 21, 1985 4:15 p.m. CSLI Colloquium, Redwood Hall, Room G-19 In information-based complexity ``information'' is, informally, what we know about a problem which we wish to solve. The goal of information-based complexity is to create a general theory about problems with partial and contaminated information and to apply the results to solving specific problems in varied disciplines. Problems with partial and contaminated information occur in areas such as vision, medical imaging, prediction, geophysical exploration, signal processing, control, and scientific and engineering calculation. For problems with partial and contaminated information, very general results can be obtained at the ``information level.'' Among the general results to be discussed is the power of parallel (non-adaptive) information and the application of such information to the solution of problems on distributed systems. The methodology and results of information-based complexity will be contrasted with the study of NP-complete problems where the information is assumed to be complete, exact, and free. ------------------------------ Date: Tue 12 Nov 85 12:45:02-EST From: "Brian C. Williams" <WILLIAMS%MIT-OZ@MIT-MC.ARPA> Subject: Seminar - Multilisp (MIT) Thursday , October 14 4:00pm Room: NE43- 8th floor Playroom The Artificial Intelligence Lab Revolving Seminar Series "Multilisp: A Language for Parallel Symbolic Computing" Burt Halstead MIT, LCS Multilisp is an extension of Scheme with additional operators and additional semantics for parallel execution. These have been added without removing side effects from the language. The principal parallelism construct in Multilisp is the "future," which exhibits some features of both eager and lazy evaluation. Current work focuses on making Multilisp a more humane programming environment, and on expanding the power of Multilisp to express task scheduling policies. A skeletal Multilisp has been implemented, and has been run on the shared-memory Concert multiprocessor, using as many as eight processors, as well as on a BBN Butterfly machine with as many as 128 processors. The implementation uses interesting techniques for task scheduling and garbage collection. The task scheduler helps control excessive resource utilization by means of an unfair scheduling policy; the garbage collector uses a multiprocessor algorithm modeled after the incremental garbage collector of Baker. The talk will describe Multilisp, discuss the areas of current activity, and indicate the future direction of the project. ------------------------------ Date: Mon 11 Nov 85 15:55:02-CST From: AI.HASSAN@MCC.ARPA Subject: Seminar - Gazing in Theorem Proving (MCC) GAZING: USING THE STRUCTURE OF THE THEORY IN THEOREM PROVING Dave Plummer Department of Mathematics University of Texas at Austin Wednesday, November 20 10:00 a.m. Echelon I, Room 409 A mechanical theorem prover embodies two types of knowledge: logical and non-logical. The logical knowledge informs the prover which inferences are legal within the logic. The non-logical information, however, is specific to the theory that the prover is working in and includes definitions of concepts used in the theory, axioms, and previously proved facts. The theory is structured by relationships between these facts and these relationships may be exploited in order to provide guidance for a mechanical theorem prover. In this talk I will describe a technique, called Gazing, which exploits the structure of a theory, thus aiding a mechanical prover in determining which items of knowledge will be useful in the proof of a given goal. As concepts are defined in the theory, the system builds a graph representing the definitional order. This graph is used in two ways. First, whenever a new fact enters the theory, the ordering is used to determine an orientation of that fact creating a new rewrite rule. Secondly, the ordering is used to guide the search for a proof of a conjecture whenever the proof is known to require the use of non-logical facts. This guidance takes the form of determining which concepts are "close" in the definitional ordering, and attempting to find rewrite rules which may be used to rewrite two different concepts to a common new concept. The ordering can also be used to decide which of a number of possible common rewritings is preferable, and indeed if any common rewriting exists. ------------------------------ Date: Thu 14 Nov 85 14:31:42-PST From: LANSKY@SRI-AI.ARPA Subject: Seminar - Deductive Design Synthesis (SRI) EXPLOITATION OF CONSTRAINTS IN DEDUCTIVE DESIGN SYNTHESIS Jeff Finger Stanford University JFinger@SU-SUSHI PLANLUNCH 11:00 AM, MONDAY, November 18 SRI International, Building E, Room EJ228 (new conference room) The talk will cover two related topics in deductive design synthesis: (1) efficiency gained by reasoning forward from subgoals, and (2) advantages and disadvantages of using a declarative representation for partially completed designs. The first part of the talk gives the deductive framework for capturing the following intuition: Suppose I have decided that X and Y and to be true of my design. Perhaps I should think about what else X and Y imply about the design, say Z. Otherwise, I might waste time trying to complete the design process by making decisions that have *already* been ruled out by X and Y, for example, NOT(Z). The conditions that X and Y imply (called "necessary constraints" or "NC's") are found via reasoning forward from subgoals. We show how NC's of a subgoal can be used to prune the design space either by preventing some impossible possibilities from ever being generated or by providing a quick means of filtering bad choices. In terms of resolution, the above use of NC's corresponds to the rather counterintuitive notion of allowing OR-INTRODUCTION on clauses in the set of support. We will also discuss inheritance of NC's from goal to subgoal and the relation of finding NC's to that of checking consistency of partially completed designs. The second part of the talk deals with declarative representation of partially completed designs. Deductive design systems such as QA3 or Manna and Waldinger's reify the design as a single term in the logic. However, it is difficult to express many sorts of constraints on partially completed designs as a single term. Examples include two actions in an unspecified order, or the constraint that Action A takes place less than 3 seconds or more than 8 seconds after Action B. We present a system called RESIDUE in which we build up the design as a set of facts we are willing to assume about of the design. Using facts rather than a single term, we can make finer-grained decisions, avoiding unwitting commitments that might result in unnecessary backtracking. In addition, forward reasoning on subgoals (as in the first portion of the talk) may be done directly on the set of facts. ------------------------------ Date: Thu, 14 Nov 85 10:40:19 EST From: "William J. Rapaport" <rapaport%buffalo.csnet@CSNET-RELAY.ARPA> Subject: Seminar - Probabilistic Propositional Logic (Buffalo) UNIVERSITY AT BUFFALO STATE UNIVERSITY OF NEW YORK DEPARTMENT OF COMPUTER SCIENCE COLLOQUIUM DEXTER KOZEN Department of Computer Science Cornell University A PROBABILISTIC PROPOSITIONAL DYNAMIC LOGIC This talk concerns a probabilistic analog of Propositional Dynamic Logic, called Probabilistic Propositional Dynamic Logic (PPDL). PPDL is useful in the formal manipulation of simple pro- babilistic programs and the average-case analysis of determinis- tic programs. We describe the formal syntax and semantics of the system and its deductive calculus, and illustrate its use by cal- culating the expected running time of a simple random walk. We also describe briefly a polynomial-space decision procedure for deciding the truth of formulas involving well-structured pro- grams. Thursday, November 21, 1985 3:30 P.M. Bell 337, Amherst Campus Wine and cheese will be served at 4:30 P.M., 224 Bell Hall For further information, call (716) 636-3181. William J. Rapaport Assistant Professor Dept. of Computer Science, SUNY Buffalo, Buffalo, NY 14260 (716) 636-3193, 3180 uucp: ...{allegra,decvax,watmath}!sunybcs!rapaport ...{cmc12,hao,harpo}!seismo!rochester!rocksvax!sunybcs!rapaport cs: rapaport@buffalo arpa: rapaport%buffalo@csnet-relay bitnet: rapaport@sunybcs ------------------------------ End of AIList Digest ********************