Login: ian Password: Last login: Fri Aug 28 13:48:03 from vaxb This login: Fri Aug 28 14:18:21 from vaxb 4.3 BSD+NFS UNIX #25: Fri Aug 28 08:08:07 MDT 1987 There is a New version of the Verdix Ada compiler installed on Vaxc. All persons using this compiler please contact Terry as soon as possible. Also, if there is anyone using the nyu ada or telesoft ada compilers please let Terry know, we would like to remove them from the system, if they are not being used. (we need the disk space!) Thanks. The imagen is once again operational but with reduced capacity (one of the memory boards is being repaired). Large documents may not work. vaxb!ian ttyp4 Aug 28 14:18 (vaxb) Welcome to Freedman's Input Line Editor (FILE). Version: 87-01-11 Please wait until your shell prompt appears (ESC-? for help). hi, terminal is amb-xl You are already logged in somewhere Harold as adjunct??? -- and reply to his mail rlogin vaxb -8 | tee transcript? VAXB cd ~/courses/551cd ~/courses/551 VAXB lsls assignments core info.sheet~ topics~ assignments~ info.sheet topics VAXB roff -a2 assignments; lpq -Palw2&roff -a2 assignments; lpq -Palw2& [1] 6894 VAXB cd ../670cd ../670 VAXB lsls 601.synopsis 601.synopsis~ synopsis synopsis~ temp VAXB cp ../533/info.sheet .cp ../533/info.sheet . VAXB e info.sheete info.sheet [>52;54h[>30;37;38;39l[>52;54h[>30;37;38;39lLoading time...doneP`&~X4R\.pn 0 .ls1 .ce2 THE UNIVERSITY OF CALGARY DEPARTMENT OF COMPUTER SCIENCE .sp2 .ps+2 .ce CPSC 533 \(em Artificial Intelligence .ps-2 .sp3 .LB .NP .ul Instructor. Ian H Witten, MA\ 786, 220-6780 .sp .NP .ul Office hours. Mondays and Wednesdays 15.00-16.00 .sp .NP .ul Tutorial instructor. Dave Maulsby, MA\ 358, 220-7683 .sp .NP .ul Lectures. ST 130, Tuesdays and Thursdays 9.30-10.45 .sp .NP .ul Labs. L01: SS\ 202, Mondays and Wednesdays 9.00-9.50; L02: SB\ 144A, Tuesdays and Thursdays 11.00-11.50 .sp .NP .ul Course texts .sp Winston, P.H. \fIArtificial Intelligence\fR. Addison-Wesley, Reading, Massachusetts, 1984 (second edition). .br Winston, P.H. & Horn, B.K.P. \fIL\s-2ISP\s+2\fR. Addison-Wesley, Reading, Massachusetts, 1984 (second edition). .sp .NP .ul Assessment .ta \w'Midterm exam (in class time) 'u .sp Midterm exam (in class time) \015% .br Final exam \030% -----Emacs: info.sheet time and load (Text Fill)----Top-------------2:20pm 3.04[1] (Text Fill)----Topno entries vaxb: sending to vaxa Rank Owner Job Files Total Size 1st ian 286 (standard input) 25202 bytes [>52;54h[>30;37;38;39l.pn 0 .ls1 .ce2 THE UNIVERSITY OF CALGARY DEPARTMENT OF COMPUTER SCIENCE .sp2 .ps+2 .ce CPSC 533 \(em Artificial Intelligence .ps-2 .sp3 .LB .NP .ul Instructor. Ian H Witten, MA\ 786, 220-6780 .sp .NP .ul Office hours. Mondays and Wednesdays 15.00-16.00 .sp .NP .ul Tutorial instructor. Dave Maulsby, MA\ 358, 220-7683 .sp .NP .ul Lectures. ST 130, Tuesdays and Thursdays 9.30-10.45 .sp .NP .ul Labs. L01: SS\ 202, Mondays and Wednesdays 9.00-9.50; L02: SB\ 144A, Tuesdays and Thursdays 11.00-11.50 .sp .NP .ul Course texts .sp Winston, P.H. \fIArtificial Intelligence\fR. Addison-Wesley, Reading, Massachusetts, 1984 (second edition). .br Winston, P.H. & Horn, B.K.P. \fIL\s-2ISP\s+2\fR. Addison-Wesley, Reading, Massachusetts, 1984 (second edition). .sp .NP .ul Assessment .ta \w'Midterm exam (in class time) 'u .sp Midterm exam (in class time) \015% .br Final exam \030% -----Emacs: info.sheet 2:20pm 3.04[1] (Text Fill)----Top------------ [5`[2@67**[1@0    M[12`John H. Andreae[8`[15`, MA\ 7--**-Emacs: info.sheet 2:20pm 3.04[1] (Text Fill)----Top------------ pn 0 .ls1 .ce2 THE UNIVERSITY OF CALGARY DEPARTMENT OF COMPUTER SCIENCE sp2 .ps+2 .ce CPSC 670 \(em Artificial Intelligence ps-2 sp3 LB .NP ul Instructor. John H Andreae, MA\ 7 Ian H Witten, MA\ 786, 220-6780 sp .NP .ul Office hours. Mondays and Wednesdays 15.00-16.00 sp NP .ul Tutorial instructor. Dave Maulsby, MA\ 358, 220-7683 .sp NPFind file: ~/courses/670/1pm 2.65[31pm 2.65[3~/tempo[14`temporary/andreae3 The Department welcomes John Andreae, on sabbatical from the University of Canterbury, Christchurch, New Zealand. John is a Visiting Research Associate and will be with us until the end of November. He is New Zealand's foremost researcher in Artificial Intelligence, and has a long-standing international reputation for his seminal work on the foundations of learning and the simulation of learning mechanisms. He will be teaching the first part of CPSC 670, and working with John Cleary and Bruce MacDonald (whose PhD theses he supervised in New Zealand), Brian Gaines, David Hill, Ian Witten, and anyone else who is interested!  John's office is room 782, phone 7299. He and Molly Andreae are presently staying at the MacDonald/Stodart residence.                ---Emacs: andreae3 2:21pm 2.65[3] (Text Fill)----All82, [21` [1@ [27` 220-7299  Tuesdays and Thursdays 14.00-15.00 (both 2pm 2.36[12pm 2.36[1instructors)[36`[54`Mondays and Wednesdays 15.\.NP Mark set .ul Lectures. ST 130, Tuesdays and Thursdays 9.30-10.45 .sp .NP.ul Labs. L01: SS\ 202, Mondays and Wednesdays 9.00-9.50; L02: SB\ 144A, Tuesdays and Thursdays 11.00-11.50 .sp .NP .ul Course texts .sp Winston, P.H. \fIArtificial Intelligence\fR. Addison-Wesley, Reading, Massachusetts, 1984 (second edition). .br Winston, P.H. & Horn, B.K.P. \fIL\s-2ISP\s+2\fR. Addison-Wesley, Reading, Massachusetts, 1984 (second edition). .sp .NP .ul Assessment .ta \w'Midterm exam (in class time) 'u .sp Midterm exam (in class time) \015% .br Final exam \030% .br Assignment 1 \010% .br Assignment 2 \010% [3` [2@??[1@?[14` [28`[1@1[1@1[1@.[2@00[1@-[1@1[2@2.[2@15 Mark set .br Project: .br \0\0\0\0interim report \010% .br \0\0\0\0final report \025%3pm 3.10[3 NP ul Office hours. uesdays and Thursdays 14.00-15.00 (both instructors) .sp NP ul Lectures. ???, Tuesdays and Thursdays 11.00-12.15 sp NP ul Course texts sp Winston, P.H. \fIArtificial Intelligence\fR. Addison-Wesley, Reading, Massachusetts, 1984 (second edition). brFind file: ~/courses/670/Winston, P.H. & Horn, B.K.P. \fIL\s-2ISP\s+2\fR. Addison-Wesley, Reading, Massachusetts, 1984 (second edition). .sp .NP .ul Assessment .ta \w'Midterm exam (in class time) 'u .sp Midterm exam (in class time) \015% --**-Emacs: info.sheet 2:23pm 3.10[3] (Text Fill)---- 2%------------ 2%synopsis pn 0 ls1 .ce2 HE UNIVERSITY OF CALGARY DEPARTMENT OF COMPUTER SCIENCE sp2 ps+2 .ce CPSC 670 \(em Artificial Intelligence ps-2 sp3 For 1987-88, this course will take place in two parts: .LB ta \w'Winter 1988 'u +\w'Dr J.A. Andreae 'u Fall 1987 Dr J.H. Andreae Machine Learning .br Winter 1988 Dr I.H. Witten Artificial Intelligence LE The parts will be assessed separately, and the first will be available as a 601 course for students wishing to take it alone. (This organization reflects a current proposal to replace CPSC 670 by several half courses in future, including CPSC 675 ``Machine Learning'' and CPSC 671 ``Artificial Intelligence''.) \c Lectures will take place on Tuesdays and Thursdays, 9.30-10.45 am. .rh "Machine Learning." This will include the following topics: LB .NP ---Emacs: synopsis 2:23pm 3.10[3] (Text Fill)----TopLB NP Dimensions of classification of machine learning problems and systems .NP Theoretical foundations of inductive inference NP Similarity-based learning: the generalization-as-search paradigm and its implementations .NP Explanation-based learning and systems which embody it NP Exploration and discovery mechanisms, functional induction and theory formation NP Learning procedural knowledge .NP Cognitive aspects of human learning. .LE The text for this part will be .LB R.S. Michalski, J.G. Carbonell, and T.M. Mitchell (Editors) (1986) \fIMachine learning Volume 2\fP. Morgan Kaufmann, Los Altos, CA. .LE Other relevant books include LB Forsyth, R. and Rada, R. (1986) ul Machine learning: applications in expert systems and information retrieval.23%4pm 2.57[14pm 2.57[1 MMMMMMark set Final exam \030% .br Assignment 1 \010% .br Assignment 2 \010% .br Project: .br \0\0\0\0interim report \010% .br \0\0\0\0final report \025% .br [4`[1@ [1@\[1@w[1@'[1@T[1@e[1@r[1@m[1@ [6`[1@w[1@'[1@W[3@int[2@er[1@ [2@Te[1@r[1@m[1@ [1@ [2@ [2@'u[1@ [1@+Auto-saving...done5pm 2.14[35pm 2.14[3Fall Term Assignment 7\(12%\7\(12%[34`07\(12%[35` Pro Mark set\0\0\0\0final report \025%.br TFinal exam[2`aFinal exam[3`keFinal exam[5`-hFinal ex[8@am [7`oFinal exam[8`meFinal exam[10` exam[10` 230%230%6pm 3.05[46pm 3.05[4\30%(30%130%230%0%%M  [27` [1@\[1@0  ____.br\h'-\w'total 'u'total\ \ 100%.spMM.sp.ta \w'Demonstrations 'uMark set.br Take-home exam \022\(12% ____  .br ____ MMM.spMMMMMM[8@  [8@   pinterim report[18`rointerim report \010%[20`ginterim report[21`reinterim report[23`ssinterim report report \010%  [8@ 7pm 3.01 Mail[2] (Text Fill)----23%7pm 3.01 Mail[2] (Text Fill)---- 2%[26`  \10%010%510%0%%%%1%2%\%(%1%2% .sp.ta \w'Demonstrations 'uMark set\0\0\0\0final report \012\(12% .br \0\0\0\0final report \012\(12% .br [11`  ifinal report[18`nfinal report[19`-final report[20`cfinal repor[8@t [21`lafinal report[23`ssfinal report final report[26`prfinal report[28`efinal repor[8@t [29`sfinal report[30`efinal report[31`ntatfinal report[35`ionfinal rep[8@ort [38` report[38` [1@\[1@0[1@2[1@\[1@( 12\(12%[50`2\(12%[50`\(12%[50`(12%12%2%% .spMMMM MM 8pm 2.878pm 2.87.br \h'-\w'total 'u'total\ \ 100% .sp .sp .ta \w'Demonstrations 'u .NP .ul Guest lectures. There may be guest lectures on topics such as: .LB .NP .ul L\s-2ISP\s+2 ``Flavors'' .NP .ul an advanced AI programming environment (eg KnowledgeCraft) .NP .ul an implementation of the ``version space'' concept learning technique .NP .ul the Japanese initiatives in new generation computing. .LE .NP .ul Demonstrations. It is hoped to arrange demonstrations of .LB .NP Mark set [8@  .ul NEXPERT expert system shellMark setFall Term Assignment \0\07\(12% .br Project: .br \0\0\0\0progress report \0\05% .br \0\0\0\0final report \012\(12% .br \0\0\0\0in-class presentation \0\02\(12% .br Take-home exam \022\(12% Fall Term Assignment \0\07\(12% .br Project: .br \0\0\0\0progress report \0\05% .br \0\0\0\0final report \012\(12% .br \0\0\0\0in-class presentation \0\02\(12% .br Take-home exam \022\(12% .sp MMMMMMMMMM.sp WFall Term[2`inFall Term[4`Auto-saving...doneterFall Term[7` Term [7`[12` CAssignment[18`lAssignment[19`aAssignment[20`sAssignment[21`sAssignment[22` Assignmen[8@t [23`pAssignment[24`reAssignment[26`sAssignment[27`eAssignment[28`nAssignment[29`tAssignment[30`aAssignmen[8@t [31`tioAssignment[34`nAssignment[35` s[43`  -home exam T-home exam[10`e-home exam[11`rm-home exam[13` -home exam[14`pa-home ex[8@am [16`per-home exam[19` exam[19` [19`9pm 2.12 Mail[1.ul Mark set an implementation of the ``version space'' concept learning technique .NP ul the Japanese initiatives in new generation computing. LE .NP .ul Demonstrations. It is hoped to arrange demonstrations of LB .NP ul NEXPERT expert system shell .NP ul Xerox L\s-2ISP\s+2 Machine NP .ul Loops programming environment (film). .LE NP .ul Facilities. VAXA LE .bp sh "About the course" .sp Winston's textbook is excellent and has been widely acclaimed. I think that in the main it is quite understandable, probably too easy. You should \fIstudy it carefully\fR (not just read it casually). Set aside some time each week. Do some of the exercises. The main function of the lectures is to take you through the text, providing additional explanation where necessary. There will not be time, however, to cover everything in lectures. p This is not a course on programming, and we will not be talking much about programming in lectures. However, by the end of the course all students are expected to have a working knowledge of the L\s-2ISP\s+2 language, and assignments and projects are to be done in L\s-2ISP\s+2. People well-educated in AI need to know the basics of \fIboth\fR P\s-2ROLOG\s+2 and L\s-2ISP\s+2. Each has strengths and weaknesses that it is important to understand, and can only be appreciated properly through experience. .pp Many students will have had some prior exposure to L\s-2ISP\s+2, and all are well versed in P\s-2ROLOG\s+2. You are expected to pick up L\s-2ISP\s+2 programming by yourself. You do \fInot\fR need to become expert in L\s-2ISP\s+2. There will be no exam questions involving tricky language details \(em at most you might have to read some code or discuss pros and cons of AI programming in L\s-2ISP\s+2 vs P\s-2ROLOG\s+2 vs P\s-2ASCAL\s+2. bp sh "Planned timetable"8bp sh "Planned timetable" .sp nf ta \w'beginning 'u +3i +\w'L\s-2ISP\s+2 Ch 0-0, 00 'u +\w'Mon 'u .ul Week Material covered Reading Deadlines ul beginning .sp \05 Jan Introduction; L\s-2ISP\s+2 AI Ch 1 Tue Assignment 1 set L\s-2ISP\s+2 Ch 1-4 sp 12 Jan Representations; blocks worlds AI Ch 2 Tue Assignment 2 set L\s-2ISP\s+2 Ch 4-7, 13 sp 19 Jan Constraints AI Ch 3 Tue Assignment 1 due .sp 26 Jan Searching AI Ch 4 Tue Assignment 2 due L\s-2ISP\s+2 Ch 11 .sp \02 Feb Control strategies; objects, Flavors AI Ch 5 Tue Project proposa\l due L\s-2ISP\s+2 Ch 16 .sp \09 Feb More on OOP (Prolog, Smalltalk) AI Ch 6 Rule-based systems L\s-2ISP\s+2 Ch 18 s 16 Feb \fI(reading week \(em no classes)\fR 23 Feb \(em Tue Mid-term exam More on rule-based systems .sp \02 Mar Logic and planning AI Ch 7 Tue Project interim report due .sp \09 Mar Knowledge representation; AI Ch 8 using frames; CD theory L\s-2ISP\s+2 Ch 22 .sp 16 Mar Understanding natural language AI Ch 9 L\s-2ISP\s+2 Ch 19-21 .sp 23 Mar Vision??? AI Ch 10 (L\s-2ISP\s+2 Ch 10?) .sp 30 Mar Concept learning AI Ch 11 Version-space technique .sp \06 Apr Learning rules AI Ch 12 Summary Fri Project final report due .sp ??? Apr Final exam (scheduled by Registrar) .fi .bp .sh "Bibliography" .sp Here is a representative selection of recent books on AI, with a bias towards practical aspects.20Assessment .ta \w'Winter Term 'u +\w'Midterm exam (in class time) 'u .sp Fall Term Assignment \0\07\(12% .br Project: .br \0\0\0\0progress report \0\05% .br \0\0\0\0final report \012\(12% .br \0\0\0\0in-class presentation \0\02\(12% .br Take-home exam \022\(12% .sp Winter Term Class presentations \0\07\(12% .br Project: .br \0\0\0\0progress report \0\05% .br \0\0\0\0final report \012\(12% .br \0\0\0\0in-class presentation \0\02\(12% .br Term paper \022\(12% .sp ____ .br \h'-\w'total 'u'total\ \ 100% 430pm 1.44Wrote /vaxb.userb/profs/ian/courses/670/info.sheetWinter Term Class prese[8@ntationserm paper ____ .br \h'-\w'total 'u'total\ \ 100% .bp .sh "Planned timetable" .sp .nf .ta \w'beginning 'u +3i +\w'L\s-2ISP\s+2 Ch 0-0, 00 'u +\w'Mon 'u .ul Week Material covered Reading Deadlines .ul beginning .sp \05 Jan Introduction; L\s-2ISP\s+2 AI Ch 1 Tue Assignment 1 set L\s-2ISP\s+2 Ch 1-4 .sp -----Emacs: info.sheet 2:30pm 1.44 Mail[1] (Text Fill)---- 6%----------Emacs: info.sheet 2:30pm 1.44 Mail[1] (Text Fill)---- 6Find file: ~/courses/670/../551/info.sheet .pn 0 ls1 .ce2 THE UNIVERSITY OF CALGARY DEPARTMENT OF COMPUTER SCIENCE sp2 .ps+2 ce CPSC 551 \(em Computer Graphics I ps-2 .sp3 LB .NP ul Instructor. Ian H Witten, MA\ 786, 220-6780p NP ul Office hours. Tuesdays and Thursdays 14.00-15.00 .sp NP .ul Tutorial assistants. Jeff Allan, MA\ 324, 220-7686, and Dave Maulsby, MA\ 358, 220-7683 .sp NP<2> 2:30pm 1.44 Mail[1] (Text Fill)----TopI-search: P ...[3`l ...a ...n ...n ...e ...d ... This is not a course on programming, and we will not be talking much about programming in lectures. However, by the end of the course all students are expected to have a working knowledge of the C programing language, and assignments and projects are to be coded in C. The text gives all examples in Pascal for pedagogical reasons only; part of the assignment work involves demonstrating your understanding of algorithms by recoding them.  is the principal modern graphics programming language in use today, and anyone well-educated in computer graphics need to know how to use it and to interface with programs written in it. You are expected to pick up C programming by yourself; the TA will be more than happy to help you with language constructs and points of syntax. bp .sh "Planned timetable" .sp nf ta \w'beginning 'u +2.5i +\w'L\s-2ISP\s+2 Ch 0-0, 00 'u +\w'Mon 'u Week Material covered Reading Deadlines .ul beginning sp \07 Sep Thu All assignments set .sp 14 Sep 21 Sep66%Mark set Mark set.sp 21 Sep .sp 28 Sep Tue Assignment 1 due .sp \05 Oct .sp 12 Oct Thu Assignment 2 due .sp 19 Oct Thu Test 1 .sp 26 Oct .sp \02 Nov Tue Assignment 3 duep \09 Nov Thu Reading days; no class sp 16 Nov Tue Test 2 .sp 23 Nov .sp 30 Nov .sp \07 Dec Tue Test 3 Thu Assignment 4 due fi Botfi                           L\s-2ISP\s+2 Ch 4-7, 13 .sp 19 Jan Constraints AI Ch 3 Tue Assignment 1 due .sp 26 Jan Searching AI Ch 4 Tue Assignment 2 due L\s-2ISP\s+2 Ch 11 .sp \02 Feb Control strategies; objects, Flavors AI Ch 5 Tue Project proposa\l due L\s-2ISP\s+2 Ch 16 .sp \09 Feb More on OOP (Prolog, Smalltalk) AI Ch 6 Rule-based systems L\s-2ISP\s+2 Ch 18 .sp 16 Feb \fI(reading week \(em no classes)\fR .sp 23 Feb \(em Tue Mid-term exam More on rule-based systems .sp \02 Mar Logic and planning AI Ch 7 Tue Project interim report due .sp \09 Mar Knowledge representation; AI Ch 8 using frames; CD theory L\s-2ISP\s+2 Ch 22 .sp 16 Mar Understanding natural language AI Ch 9 L\s-2ISP\s+2 Ch 19-21 .sp 23 Mar Vision??? AI Ch 10 (L\s-2ISP\s+2 Ch 10?) Mark set.sp .nf .ta \w'beginning 'u +2.5i +\w'L\s-2ISP\s+2 Ch 0-0, 00 'u +\w'Mon 'u .ul Week Material covered Reading Deadlines .ul beginning .sp \07 Sep Thu All assignments set .sp 14 Sep .sp 21 Sep .sp 28 Sep Tue Assignment 1 due .sp \05 Oct .sp 12 Oct Thu Assignment 2 due .sp 19 Oct Thu Test 1 .sp 26 Oct .sp \02 Nov Tue Assignment 3 due .sp \09 Nov Thu Reading days; no class .sp 16 Nov Tue Test 2 .sp 23 Nov .sp 30 Nov .sp \07 Dec Tue Test 3 Thu Assignment 4 due .fi .sh "Planned timetable"**-Emacs: info.sheet 2:30pm 1.44 Mail[1] (Text Fill)---- 5 .sp nf .ta \w'beginning 'u +3i +\w'L\s-2ISP\s+2 Ch 0-0, 00 'u +\w'Mon 'u ul Week Material covered Reading Deadlines ul beginning sp \05 Jan Introduction; L\s-2ISP\s+2 AI Ch 1 Tue Assignment 1 set L\s-2ISP\s+2 Ch 1-4 .sp 12 Jan Representations; blocks worlds AI Ch 2 Tue Assignment 2 set L\s-2ISP\s+2 Ch 4-7, 13 sp 19 Jan Constraints AI Ch 3 Tue Assignment 1 due s 26 Jan Searching AI Ch 4 Tue Assignment 2 due L\s-2ISP\s+2 Ch 11 sp \02 Feb Control strategies; objects, Flavors AI Ch 5 Tue Project proposa\l due L\s-2ISP\s+2 Ch 16 sp \09 Feb More on OOP (Prolog, Smalltalk) AI Ch 6 Rule-based systems L\s-2ISP\s+2 Ch 18 .sp 16 Feb \fI(reading week \(em no classes)\fR .sp 23 Feb \(em Tue Mid-term exam More on rule-based systems \02 Mar Logic and planning AI Ch 7 Tue Project interim report due9 Mar Knowledge representation; AI Ch 8 using frames; CD theory L\s-2ISP\s+2 Ch 22 .sp 16 Mar Understanding natural language AI Ch 9 L\s-2ISP\s+2 Ch 19-21 3 Mar Vision??? AI Ch 10 (L\s-2ISP\s+2 Ch 10?) .sp 30 Mar Concept learning AI Ch 11 Version-space technique .sp \06 Apr Learning rules AI Ch 12 Summary Fri Project final report due .sp ??? Apr Final exam (scheduled by Registrar) .fi b .sh "Bibliography" .sp Here is a representative selection of recent books on AI, with a bias towards practical aspects. LB ti-4n11LB ti-4n Barr, A. and Feigenbaum, E.A. (Editors) \c \fIThe handbook of Artificial Intelligence\fR. Kaufmann, Los Altos, CA, \fIVolume I\fR, 1981, and \fIVolume II\fR, 1982. .sp ti-4n Bratko, I. \fIP\s-2ROLOG\s+2 programming for artificial intelligence\fR. Addison-Wesley, Wokingham, England, 1986. .sp .ti-4n Bundy, A. \fIThe computer modelling of mathematical reasoning\fR. Academic Press, London, 1983. .sp .ti-4n Bundy, A. (Editor) fICatalogue of Artificial Intelligence tools\fR. Springer-Verlag, Berlin, 1984. .sp ti-4n Cohen, P.R. and Feigenbaum, E.A. (Editors) \c \fIThe handbook of Artificial Intelligence Volume III\fR. Kaufmann, Los Altos, CA, 1982. .sp ti-4n Charniak E., Riesbeck, C.K. and McDermott, D.V. \fIArtificial intelligence programming\fR. Lawrence Erlbaum Associates, Hillsdale, New Jersey, 1980. .sp ti-4n Gevarter, W.B. \fIIntelligent machines\fR. Prentice-Hall, Englewood Cliffs, NJ, 1985. .sp .ti-4n Michalski, R.S., Carbonnell, J.G., and Mitchell, T.M. (Editors) \c \fIMachine Learning\fR, Tioga, Palo Alto, CA, 1983, and \fIMachine Learning Volume II\fR, Kaufmann, Los Altos, CA, 1986. .sp .ti-4n Nilsson, N.J. fIPrinciples of artificial intelligence\fR. Tioga, Palo Alto, CA, 1980. .sp ti-4n Schank, R.C. and Riesbeck, C.K. \fIInside computer understanding: five programs plus miniatures\fR. Lawrence Erlbaum Associates, Hillsdale, New Jersey, 1981.p ti-4n Sowa, J.F. \fIConceptual structures\fR. Addison-Wesley, Reading, Massachusetts, 1984. sp221pm 1.42 Mail[2Addison-Wesley, Reading, Massachusetts, 1984. sp .ti-4n Waterman, D.A. \fIA guide to expert systems\fR. Addison-Wesley, Reading, MA, 1985. Wilensky, R.L\s-2ISP\s+2craft\fR. W.W.Norton, New York, 1984. .sp ti-4n Winograd, T. \fILanguage as a cognitive process. Volume I \(em Syntax\fR. Addison-Wesley, Reading, Massachusetts, 1984. .sp ti-4n Winston, P.H. \fIArtificial Intelligence\fR. Addison-Wesley, Reading, Massachusetts, 1984 (second edition). .sp ti-4n Winston, P.H. & Horn, B.K.P. \fIL\s-2ISP\s+2\fR. Addison-Wesley, Reading, Massachusetts, 1984 (second edition). .LE bp 0 ce2 THE UNIVERSITY OF CALGARY DEPARTMENT OF COMPUTER SCIENCE .sp2 ps+2 ce CPSC 533 \(em Artificial Intelligence .ps-2 .sh "Assignment 1. Introduction to \s-2LISP\s+2" (10% of course marks) sp Fully commented code, together with answers to the questions posed in Q.3, must be handed in on Tuesday January 20th. There will be .ul no extension of this deadline, except for good, documented, medical reasons. LB "1. " .NI "1. " 1.\ \ \c Write a recursive function \s-2POWER-OF-TWO\s+2 that computes the \fIn\fRth power of two, eg (\s-2POWER-OF-TWO\s+2 100) returns 1267650600228229401496703205376. [A simple test of function-definition and recursion.] .sp .NI "1. " 2.\ \ \c Write your own version of the Lisp function \s-2ASSOC\s+2. \s-2ASSOC\s+2 ``looks up'' a value in a list of lists, and returns the first list it finds whose first element matches the desired value. For example, (\s-2ASSOC\s+2 '\s-2Y\s+2 '((\s-2X\s+2 \s-2A\s+2) (\s-2Y\s+2 \s-2B\s+2) (\s-2Z\\[70`3.sp nf a \w'beginning 'u +3i +\w'L\s-2ISP\s+2 Ch 0-0, 00 'u +\w'Mon 'u .ul Week Material covered Reading Deadlines .ul beginning sp \05 Jan Introduction; L\s-2ISP\s+2 AI Ch 1 Tue Assignment 1 set L\s-2ISP\s+2 Ch 1-4 12 Jan Representations; blocks worlds AI Ch 2 Tue Assignment 2 set L\s-2ISP\s+2 Ch 4-7, 13 .sp 19 Jan Constraints AI Ch 3 Tue Assignment 1 due 26 Jan Searching AI Ch 4 Tue Assignment 2 due L\s-2ISP\s+2 Ch 11 .sp \02 Feb Control strategies; objects, Flavors AI Ch 5 Tue Project proposa\l due L\s-2ISP\s+2 Ch 16 .sp 09 Feb More on OOP (Prolog, Smalltalk) AI Ch 6 Rule-based systems L\s-2ISP\s+2 Ch 18 sp 16 Feb \fI(reading week \(em no classes)\fR sp 23 Feb \(em Tue Mid-term exam More on rule-based systems \02 Mar Logic and planning AI Ch 7 Tue Project interim report due sp \09 Mar Knowledge representation; AI Ch 8 using frames; CD theory L\s-2ISP\s+2 Ch 22p 16 Mar Understanding natural language AI Ch 9 L\s-2ISP\s+2 Ch 19-21 .sp 23 Mar Vision??? AI Ch 10 (L\s-2ISP\s+2 Ch 10?) sp 30 Mar Concept learning AI Ch 11 Version-space technique sp \06 Apr Learning rules AI Ch 12 Summary Fri Project final report due .sp ??? Apr Final exam (scheduled by Registrar) fi bp .sh "Bibliography" .sp Here is a representative selection of recent books on AI, with a bias towards practical aspects. .LB .ti-4n11 MLB ti-4n Barr, A. and Feigenbaum, E.A. (Editors) \c \fIThe handbook of Artificial Intelligence\fR. Kaufmann, Los Altos, CA, \fIVolume I\fR, 1981, and \fIVolume II\fR, 1982. .sp ti-4n Bratko, I. \fIP\s-2ROLOG\s+2 programming for artificial intelligence\fR. Addison-Wesley, Wokingham, England, 1986. .sp .ti-4n Bundy, A. \fIThe computer modelling of mathematical reasoning\fR. Academic Press, London, 1983. .sp .ti-4n Bundy, A. (Editor) fICatalogue of Artificial Intelligence tools\fR. Springer-Verlag, Berlin, 1984. .sp ti-4n Cohen, P.R. and Feigenbaum, E.A. (Editors) \c \fIThe handbook of Artificial Intelligence Volume III\fR. Kaufmann, Los Altos, CA, 1982. .sp ti-4n Charniak E., Riesbeck, C.K. and McDermott, D.V. \fIArtificial intelligence programming\fR. Lawrence Erlbaum Associates, Hillsdale, New Jersey, 1980. .sp ti-4n Gevarter, W.B. \fIIntelligent machines\fR. Prentice-Hall, Englewood Cliffs, NJ, 1985. .sp .ti-4n Michalski, R.S., Carbonnell, J.G., and Mitchell, T.M. (Editors) \c \fIMachine Learning\fR, Tioga, Palo Alto, CA, 1983, and \fIMachine Learning Volume II\fR, Kaufmann, Los Altos, CA, 1986. .sp .ti-4n Nilsson, N.J. fIPrinciples of artificial intelligence\fR. Tioga, Palo Alto, CA, 1980. .sp ti-4n Schank, R.C. and Riesbeck, C.K. \fIInside computer understanding: five programs plus miniatures\fR. Lawrence Erlbaum Associates, Hillsdale, New Jersey, 1981.p ti-4n Sowa, J.F. \fIConceptual structures\fR. Addison-Wesley, Reading, Massachusetts, 1984. sp22 Msp nf .ta \w'beginning 'u +3i +\w'L\s-2ISP\s+2 Ch 0-0, 00 'u +\w'Mon 'u .ul Week Material covered Reading Deadlines .ul beginning sp \05 Jan Introduction; L\s-2ISP\s+2 AI Ch 1 Tue Assignment 1 set L\s-2ISP\s+2 Ch 1-4 .sp 12 Jan Representations; blocks worlds AI Ch 2 Tue Assignment 2 set L\s-2ISP\s+2 Ch 4-7, 13 .sp 19 Jan Constraints AI Ch 3 Tue Assignment 1 due .sp 26 Jan Searching AI Ch 4 Tue Assignment 2 due L\s-2ISP\s+2 Ch 11 .sp 02 Feb Control strategies; objects, Flavors AI Ch 5 Tue Project proposa\l due L\s-2ISP\s+2 Ch 16 sp \09 Feb More on OOP (Prolog, Smalltalk) AI Ch 6 Rule-based systems L\s-2ISP\s+2 Ch 18 .sp 16 Feb \fI(reading week \(em no classes)\fR sp 23 Feb \(em Tue Mid-term exam More on rule-based systems .sp \02 Mar Logic and planning AI Ch 7 Tue Project interim report due sp \09 Mar Knowledge representation; AI Ch 8 using frames; CD theory L\s-2ISP\s+2 Ch 22 .sp 16 Mar Understanding natural language AI Ch 9 L\s-2ISP\s+2 Ch 19-21 .sp 23 Mar Vision??? AI Ch 10 (L\s-2ISP\s+2 Ch 10?) .sp 30 Mar Concept learning AI Ch 11 Version-space technique .sp 06 Apr Learning rules AI Ch 12 Summary Fri Project final report due sp ??? Apr Final exam (scheduled by Registrar) .fi .bp h "Bibliography" sp Here is a representative selection of recent books on AI, with a bias towards practical aspects. .LB ti-4n11 MMMMMMMBarr, A. and Feigenbaum, E.A. (Editors) \c \fIThe handbook of Artificial Intelligence\fR. Kaufmann, Los Altos, CA, \fIVolume I\fR, 1981, and \fIVolume II\fR, 1982. .sp .ti-4n Bratko, I. \fIP\s-2ROLOG\s+2 programming for artificial intelligence\fR. Addison-Wesley, Wokingham, England, 1986. .sp .ti-4n Bundy, A. \fIThe computer modelling of mathematical reasoning\fR. Academic Press, London, 1983. .sp .ti-4n Bundy, A. (Editor) \fICatalogue of Artificial Intelligence tools\fR. Springer-Verlag, Berlin, 1984. .sp .ti-4n Cohen, P.R. and Feigenbaum, E.A. (Editors) \c \fIThe handbook of Artificial Intelligence Volume III\fR. Kaufmann, Los Altos, CA, 1982. .sp .ti-4n Charniak E., Riesbeck, C.K. and McDermott, D.V. \fIArtificial intelligence programming\fR. Lawrence Erlbaum Associates, Hillsdale, New Jersey, 1980. .sp .ti-4n Gevarter, W.B. \fIIntelligent machines\fR. Prentice-Hall, Englewood Cliffs, NJ, 1985. .sp .ti-4n Michalski, R.S., Carbonnell, J.G., and Mitchell, T.M. (Editors) \c \fIMachine Learning\fR, Tioga, Palo Alto, CA, 1983, and \fIMachine Learning Volume II\fR, Kaufmann, Los Altos, CA, 1986. .sp .ti-4n Nilsson, N.J. \fIPrinciples of artificial intelligence\fR. Tioga, Palo Alto, CA, 1980. .sp .ti-4n Schank, R.C. and Riesbeck, C.K. \fIInside computer understanding: five programs plus miniatures\fR. Lawrence Erlbaum Associates, Hillsdale, New Jersey, 1981. .sps Winter Term Class presentations \0\07\(12% br Project: .br \0\0\0\0progress report \0\05% br \0\0\0\0final report \012\(12% .br \0\0\0\0in-class presentation \0\02\(12% .br Term paper \022\(12% sp ____ .br \h'-\w'total 'u'total\ \ 100% b sh "Planned timetable" .sp .nf .ta \w'beginning 'u +2.5i +\w'L\s-2ISP\s+2 Ch 0-0, 00 'u +\w'Mon 'u ul Week Material covered Reading Deadlines .ul beginning .sp \07 Sep Thu All assignments set sp 14 Sep .sp 21 Sep 28 Sep Tue Assignment 1 due .sp 05 Oct .sp 12 Oct Thu Assignment 2 due sp 19 Oct Thu Test 1 .sp 26 Oct \02 Nov Tue Assignment 3 due .sp 09 Nov Thu Reading days; no class .sp 16 Nov Tue Test 2 sp 23 Nov .sp 30 Nov \07 Dec Tue Test 3 Thu Assignment 4 due .fi .bp h "Bibliography" 6 MMMMMMMMM[4`       [3`[7`    [7`2pm 1.13 Mail[1         .sp [7`   Thu Assignment 4 due Here is a representative selection of recent books on AI, with a bias towards   21 Sep sp 28 Sep sp \05 Oct sp 12 Oct sp 19 Oct sp 26 Oct \02 Nov sp \09 Nov s 16 Nov 23 Nov sp 30 Nov .sp \07 Dec .fi b .sh "Bibliography" Here is a representative selection of recent books on AI, with a bias towards practical aspects. .LB ti-4n Barr, A. and Feigenbaum, E.A. (Editors) \c \fIThe handbook of Artificial Intelligence\fR. Kaufmann, Los Altos, CA, \fIVolume I\fR, 1981, and \fIVolume II\fR, 1982. .sp ti-4n Bratko, I. \fIP\s-2ROLOG\s+2 programming for artificial intelligence\fR. Addison-Wesley, Wokingham, England, 1986. .ti-4n Bundy, A. fIThe computer modelling of mathematical reasoning\fR. Academic Press, London, 1983. .sp ti-4n Bundy, A. (Editor) \fICatalogue of Artificial Intelligence tools\fR. Springer-Verlag, Berlin, 1984. .ti-4n Cohen, P.R. and Feigenbaum, E.A. (Editors) \c \fIThe handbook of Artificial Intelligence Volume III\fR. Kaufmann, Los Altos, CA, 1982. .ti-4n10  [38`[45`[51`[54` practical aspects.Charniak E., Riesbeck, C.K. and McDermott, D.V..Wrote /vaxb.userb/profs/ian/courses/670/info.sheet--\09 Nov 16 Nov 23 Nov 30 Nov \07 Dec fi .bp h "Bibliography" .sp Here is a representative selection of recent books on AI. .LB .ti-4n Barr, A. and Feigenbaum, E.A. (Editors) \c \fIThe handbook of Artificial Intelligence\fR. Kaufmann, Los Altos, CA, \fIVolume I\fR, 1981, and \fIVolume II\fR, 1982. .sp .ti-4n Bratko, I. \fIP\s-2ROLOG\s+2 programming for artificial intelligence\fR. Addison-Wesley, Wokingham, England, 1986. .spFind file: ~/courses/670/.ti-4n -----Emacs: info.sheet 2:32pm 1.13 Mail[1] (Text Fill)----10%-------synopsis LB .NP Dimensions of classification of machine learning problems and systems .NP Theoretical foundations of inductive inference .NP Similarity-based learning: the generalization-as-search paradigm and its implementations NP Explanation-based learning and systems which embody it NP Exploration and discovery mechanisms, functional induction and theory formation NP Learning procedural knowledge .NP Cognitive aspects of human learning. LE The text for this part will be .LB R.S. Michalski, J.G. Carbonell, and T.M. Mitchell (Editors) (1986) \fIMachine learning Volume 2\fP. Morgan Kaufmann, Los Altos, CA. LE Other relevant books include .LB Forsyth, R. and Rada, R. (1986) ul Machine learning: applications in expert systems and information retrieval.synopsis 2:32pm 1.13 Mail[1] (Text Fill)----233pm 1.583pm 1.58ti-4n Bundy, A. \fIThe computer modelling of mathematical reasoning\fR. Academic Press, London, 1983. .ti-4n Bundy, A. (Editor) \fICatalogue of Artificial Intelligence tools\fR. Springer-Verlag, Berlin, 1984. .sp ti-4n Cohen, P.R. and Feigenbaum, E.A. (Editors) \c \fIThe handbook of Artificial Intelligence Volume III\fR. Kaufmann, Los Altos, CA, 1982. .sp ti-4n Charniak E., Riesbeck, C.K. and McDermott, D.V. \fIArtificial intelligence programming\fR. Lawrence Erlbaum Associates, Hillsdale, New Jersey, 1980. .sp .ti-4n Gevarter, W.B. \fIIntelligent machines\fR. Prentice-Hall, Englewood Cliffs, NJ, 1985. .sp .ti-4n Michalski, R.S., Carbonnell, J.G., and Mitchell, T.M. (Editors) \c \fIMachine Learning\fR, Tioga, Palo Alto, CA, 1983, and \fIMachine Learning Volume II\fR,4 Mark set Ellis Horwood, Chichester, England. .sp R.S. Michalski, J.G. Carbonell, and T.M. Mitchell (Editors) (1983) .ul Machine learning -- an artificial intelligence approach. Tioga, Palo Alto, CA. .sp T.M. Mitchell, J.G. Carbonell, and R.S. Michalski (Editors) (1986) .ul Machine learning -- a guide to current research. Kluwer, Boston, MA. .sp Osherson, D.N., Stob, D. and Weinstein, S. (1986)34   Mark setForsyth, R. and Rada, R. (1986) .ul Machine learning: applications in expert systems and information retrieval. Ellis Horwood, Chichester, England.** [1@.[1@s[1@p Gevarter, W.B. [1@.[1@t[1@i[1@-[1@4[1@n Gevarter, W.B. \fIIntelligent machines\fR. Prentice-Hall, Englewood Cliffs, NJ, 1985. .sp .ti-4n Michalski, R.S., Carbonnell, J.G., and Mitchell, T.M. (Editors) \c \fIMachine Learning\fR, Tioga, Palo Alto, CA, 1983, and \fIMachine Learning Volume II\fR, Kaufmann, Los Altos, CA, 1986. .sp .ti-4n Nilsson, N.J. \fIPrinciples of artificial intelligence\fR. Tioga, Palo Alto, CA, 1980. .sp .ti-4n7[4` Mark set 4pm 1.84pm 1.8  Mark setR.S. Michalski, J.G. Carbonell, and T.M. Mitchell (Editors) (1983) .ul Machine learning -- an artificial intelligence approach. Tioga, Palo Alto, CA. .sp T.M. Mitchell, J.G. Carbonell, and R.S. Michalski (Editors) (1986) .ul Machine learning -- a guide to current research. Kluwer, Boston, MA. MMM\fIMachine Learning Volume II\fR, MKaufmann, Los Altos, CA, 1986. M.sp M.ti-4n MMMMM[1@.[1@t[1@i[1@-[1@4[1@n T.M. Mitchell, J.G. Carbonell, and R.S. Michalski (Editors) (1986) [1@.[1@s[1@p T.M. Mitchell, J.G. Carbonell, and R.S. Michalski (Editors) (1986) [1@.[1@t[1@i[1@-[1@4[1@n T.M. Mitchell, J.G. Carbonell, and R.S. Michalski (Editors) (1986) MMMMMMMMMMMMMMMMMMMMMMMMark set  Mark setR.S. Michalski, J.G. Carbonell, and T.M. Mitchell (Editors) (1986) \fIMachine learning Volume 2\fP. Morgan Kaufmann, Los Altos, CA. sp .ti-4n M MMT.M. Mitchell, J.G. Carbonell, and R.S. Michalski (Editors) (1986) .ul Machine learning -- a guide to current research. Kluwer, Boston, MA. .sp .ti-4n Nilsson, N.J. \fIPrinciples of artificial intelligence\fR. Tioga, Palo Alto, CA, 1980. .sp .ti-4n Schank, R.C. and Riesbeck, C.K. \fIInside computer understanding: five programs plus miniatures\fR. Lawrence Erlbaum Associates, Hillsdale, New Jersey, 1981. .sp20 .ti-4n Sowa, J.F. Mark set.sp .ti-4n .sp .ti-4n Msp Osherson, D.N., Stob, D. and Weinstein, S. (1986) ul Systems that learn. MIT Press, Cambridge, MA. .sp .ul Proceedings of the Fourth International Workshop on Machine Learning (June, 1987). Morgan Kaufmann, Los Altos, CA.E .rh "Artificial Intelligence." This part will review the foundations of artificial intelligence and illustrate the problem of relating formal systems to human intelligence by examining some key aspects of knowledge representation. In particular, the recently proposed theory of situational semantics will be studied in some detail to bring into focus the problems involved in representing meaning. LB .NP he AI hypothesis: that human thinking and machine computing are radically the same .NP Knowledge representation case study 1: inheritance systems .NP nowledge representation case study 2: uncertain knowledge NP A theory of meaning: situational semantics. Abstract situations,55pm 1.27 Mail[25pm 1.27 Mail[2 Mark set   Mark set Osherson, D.N., Stob, D. and Weinstein, S. (1986) .ul Systems that learn. MIT Press, Cambridge, MA. .sp .ul Proceedings of the Fourth International Workshop on Machine Learning (June, 1987). Morgan Kaufmann, Los Altos, CA. Lawrence Erlbaum Associates, Hillsdale, New Jersey, 1981. .sp .ti-4n Sowa, J.F. \fIConceptual structures\fR. Addison-Wesley, Reading, Massachusetts, 1984. .sp .ti-4n Waterman, D.A. \fIA guide to expert systems\fR. Addison-Wesley, Reading, MA, 1985.4MMM.ult.uli.ul-.ul4.uln.ul .ul NP A theory of meaning: situational semantics. Abstract situations, events, event-types, roles, constraints, constraint-types. Linguistic meaning, attitudes. Representing mental states and events. LE The text for this part will be .LB Barwise, J. and Perry, J. (1983) .ul Situations and attitudes. MIT Press, Cambridge, MA. .LE Other relevant books include .LB Cohen, P.R. (1985) .ul Heuristic reasoning about uncertainty: an artificial intelligence approach. Pitmans Advanced Program, London, England. sp Haugeland, J. (1985) .ul Artificial intelligence: the very idea. MIT Press, Cambridge, MA. sp anal, L.N. and Lemmer, J.F.\0(Editors) (1986) ul Uncertainty in artificial intelligence.78 North Holland, Amsterdam. .sp Touretzky, D. (1986) .ul The mathematics of inheritance systems. Pitman, London. .LE **-Emacs: synopsis 2:35pm 1.27 Mail[2] (Text Fill)----BotUndo!Osherson, D.N., Stob, D. and Weinstein, S. (1986)ystems that learn.sp .ul Proceedings of the Fourth International Workshop on Machine Learning (June, 1987). Morgan Kaufmann, Los Altos, CA. .LE .rh "Artificial Intelligence." This part will review the foundations of artificial intelligence and illustrate the problem of relating formal systems to human intelligence by examining some key aspects of knowledge representation. In particular, the recently proposed theory of situational semantics will be studied in some detail to bring into focus the problems involved in representing meaning. .LB NP The AI hypothesis: that human thinking and machine computing are radically the same NP Knowledge representation case study 1: inheritance systems NP Knowledge representation case study 2: uncertain knowledge .NP A theory of meaning: situational semantics. Abstract situations, events, event-types, roles, constraints, constraint-types.---Emacs: synopsis 2:35pm 1.27 Mail[2] (Text Fill)----54%  Linguistic meaning, attitudes. Representing mental states and events. .LE The text for this part will be .LB Barwise, J. and Perry, J. (1983) .ul Situations and attitudes. MIT Press, Cambridge, MA. .LE Other relevant books include .LB Cohen, P.R. (1985) .ul Heuristic reasoning about uncertainty: an artificial intelligence approach. Pitmans Advanced Program, London, England. .sp Haugeland, J. (1985) .ul Artificial intelligence: the very idea. MIT Press, Cambridge, MA. .sp Kanal, L.N. and Lemmer, J.F.\0(Editors) (1986) .ul Uncertainty in artificial intelligence. North Holland, Amsterdam.78 Mark set Machine learning: applications in expert systems and information retrieval. Ellis Horwood, Chichester, England. Gevarter, W.B. \fIIntelligent machines\fR. Prentice-Hall, Englewood Cliffs, NJ, 1985. .sp ti-4n R.S. Michalski, J.G. Carbonell, and T.M. Mitchell (Editors) (1983) Machine learning -- an artificial intelligence approach. Tioga, Palo Alto, CA. .sp ti-4n R.S. Michalski, J.G. Carbonell, and T.M. Mitchell (Editors) (1986) \fIMachine learning Volume 2\fP. Morgan Kaufmann, Los Altos, CA. .sp ti-4n T.M. Mitchell, J.G. Carbonell, and R.S. Michalski (Editors) (1986) .ul Machine learning -- a guide to current research. Kluwer, Boston, MA. Nilsson, N.J.Principles of artificial intelligence\fR.18 MBratko, I. \fIP\s-2ROLOG\s+2 programming for artificial intelligence\fR. Addison-Wesley, Wokingham, England, 1986. Bundy, A.The computer modelling of mathematical reasoning\fR. Academic Press, London, 1983. Bundy, A. (Editor) \fICatalogue of Artificial Intelligence tools\fR. Springer-Verlag, Berlin, 1984. .sp ti-4n Cohen, P.R. and Feigenbaum, E.A. (Editors) \c \fIThe handbook of Artificial Intelligence Volume III\fR. Kaufmann, Los Altos, CA, 1982. .sp ti-4n Charniak E., Riesbeck, C.K. and McDermott, D.V..sp 26 Oct .sp \02 Nov sp \09 Nov .sp 16 Nov 23 Nov .sp 30 Nov .sp \07 Dec fi .bp .sh "Bibliography" .sp Here is a representative selection of recent books on AI. LB .ti-4n Barr, A. and Feigenbaum, E.A. (Editors) \c \fIThe handbook of Artificial Intelligence\fR. Kaufmann, Los Altos, CA, \fIVolume I\fR, 1981, and \fIVolume II\fR, 1982. Bratko, I.\s-2ROLOG\s+[17@2 programming for0 M6pm 2.436pm 2.43 MM Mark setBarwise, J. and Perry, J. (1983) .ul Situations and attitudes. MIT Press, Cambridge, MA. Addison-Wesley, Wokingham, England, 1986. .sp .ti-4n Bundy, A. \fIThe computer modelling of mathematical reasoning\fR. Academic Press, London, 1983. .sp .ti-4n Bundy, A. (Editor) \fICatalogue of Artificial Intelligence tools\fR. Springer-Verlag, Berlin, 1984. .sp .ti-4n1[1@.[1@s[1@p Bratko, I. [1@.[1@t[1@i[1@-4n Bratko, I.  Mark set \fICatalogue of Artificial Intelligence tools\fR. Springer-Verlag, Berlin, 1984. sp Cohen, [3@P.R Volume III\fR.1982. .sp ti-4n Charniak E., Riesbeck, C.K. and McDermott, D.V. \fIArtificial intelligence programming\fR. Lawrence Erlbaum Associates, Hillsdale, New Jersey, 1980. .sp .ti-4n Forsyth, R. and Rada, R. (1986) ul Machine learning: applications in expert systems and information retrieval. Ellis Horwood, Chichester, England. Gevarter, W.B.Intelligent machines\fR. Prentice-Hall, Englewood Cliffs, NJ, 1985. R.S. Michalski, J.G. Carbonell, and T.M. Mitchell (Editors) (1983) .ul Machine learning -- an artificial intelligence approach. 5 Mark setCohen, P.R. (1985) .ul Heuristic reasoning about uncertainty: an artificial intelligence approach. Pitmans Advanced Program, London, England. [1@.[1@s[1@p Charniak E., Riesbeck, C.K. and McDermott, D.V. [1@.[1@t[1@i[1@-[1@4[1@n Charniak E., Riesbeck, C.K. and McDermott, D.V. MMMark set Prentice-Hall, Englewood Cliffs, NJ, 1985. .sp .ti-4n R.S. Michalski, J.G. Carbonell, and T.M. Mitchell (Editors) (1983) .ulMMMMMMMMMMMMark setCharniak E., Riesbeck, C.K. and McDermott, D.V. \fIArtificial intelligence programming\fR. Lawrence Erlbaum Associates, Hillsdale, New Jersey, 1980. .sp .ti-4n M7pm 1.97 Mail[17pm 1.97 Mail[1Gevarter, W.B. \fIIntelligent machines\fR. Prentice-Hall, Englewood Cliffs, NJ, 1985. sp .ti-4n R.S. Michalski, J.G. Carbonell, and T.M. Mitchell (Editors) (1983) .ul Machine learning -- an artificial intelligence approach. Tioga, Palo Alto, CA. .sp .ti-4n R.S. Michalski, J.G. Carbonell, and T.M. Mitchell (Editors) (1986) \fIMachine learning Volume 2\fP. Morgan Kaufmann, Los Altos, CA. .sp ti-4n T.M. Mitchell, J.G. Carbonell, and R.S. Michalski (Editors) (1986) .ul Machine learning -- a guide to current research. Kluwer, Boston, MA. .sp ti-4n Nilsson, N.J. \fIPrinciples of artificial intelligence\fR. Tioga, Palo Alto, CA, 1980. Osherson, D.N., Stob, D. and Weinstein, S. (1986) .ul21 Systems that learn.MIT Press, Cambridge, MA.Mark set.sp .ti-4n .sp .ti-4n M Mark set .sp Touretzky, D. (1986) .ul The mathematics of inheritance systems. Pitman, London. .LEBotMark setHaugeland, J. (1985) .ul Artificial intelligence: the very idea. MIT Press, Cambridge, MA. .sp Kanal, L.N. and Lemmer, J.F.\0(Editors) (1986) .ul Uncertainty in artificial intelligence. North Holland, Amsterdam. MMM[1@.[1@t[1@i[1@-[1@4[1@n Kanal, L.N. and Lemmer, J.F.\0(Editors) (1986) .ti-4n T.M. Mitchell, J.G. Carbonell, and R.S. Michalski (Editors) (1986) .ul Machine learning -- a guide to current research. Kluwer, Boston, MA. .sp .ti-4n Nilsson, N.J.Principles of artificial intelligence\fR. Tioga, Palo Alto, CA, 1980. Osherson, D.N., Stob, D. and Weinstein, S. (1986) Systems that learn. .ul Proceedings of the Fourth International Workshop on Machine Learning (June, 1987). Schank, R.C. and Riesbeck, C.K. \fIInside computer understanding: five programs plus miniatures\fR. Lawrence Erlbaum Associates, Hillsdale, New Jersey, 1981. .sp .ti-4n5Auto-saving...donesp .ti-4n Sowa, J.F. \fIConceptual structures\fR. Addison-Wesley, Reading, Massachusetts, 1984. Waterman, D.A.A guide to expert systems\fR. Addison-Wesley, Reading, MA, 1985. Wilensky, R. \fIL\s-2ISP\s+2craft\fR. W.W.Norton, New York, 1984. .sp .ti-4n Winograd, T. \fILanguage as a cognitive process. Volume I \(em Syntax\fR. Addison-Wesley, Reading, Massachusetts, 1984. .sp .ti-4n Winston, P.H. \fIArtificial Intelligence\fR. Addison-Wesley, Reading, Massachusetts, 1984 (second edition). .sp .ti-4n Winston, P.H. & Horn, B.K.P. \fIL\s-2ISP\s+2\fR. Addison-Wesley, Reading, Massachusetts, 1984 (second edition).30 .LE.bp 0Mark set.sp .ti-4n .sp .ti-4n Waterman, D.A. MM Mark set Mark setTouretzky, D. (1986) .ul The mathematics of inheritance systems. Pitman, London. 8pm 1.48 Winston, P.H. \fIArtificial Intelligence\fR. Addison-Wesley, Reading, Massachusetts, 1984 (second edition). .sp .ti-4n Winston, P.H. & Horn, B.K.P. \fIL\s-2ISP\s+2\fR. Addison-Wesley, Reading, Massachusetts, 1984 (second edition). LE .bp 0 .ce2 THE UNIVERSITY OF CALGARY DEPARTMENT OF COMPUTER SCIENCE .sp2 .ps+2 .ce CPSC 533 \(em Artificial Intelligence ps-2 .sh "Assignment 1. Introduction to \s-2LISP\s+2" (10% of course marks) Fully commented code, together with answers to the questions posed in Q.3, must be handed in on Tuesday January 20th. There will be .ul no extension of this deadline, except for good, documented, medical reasons. LB "1. " .NI "1. " 1.\ \ \c8pm 1.48 Mail[1] (Text Fill)----34 Mark set.NI "1. " 1.\ \ \c Write a recursive function \s-2POWER-OF-TWO\s+2 that computes the \fIn\fRth power of two, eg (\s-2POWER-OF-TWO\s+2 100) returns 1267650600228229401496703205376. [A simple test of function-definition and recursion.] .sp .NI "1. " 2.\ \ \c Write your own version of the Lisp function \s-2ASSOC\s+2. \s-2ASSOC\s+2 ``looks up'' a value in a list of lists, and returns the first list it finds whose first element matches the desired value. For example, (\s-2ASSOC\s+2 '\s-2Y\s+2 '((\s-2X\s+2 \s-2A\s+2) (\s-2Y\s+2 \s-2B\s+2) (\s-2Z\\s+2 \s-2C\s+2))) returns (\s-2Y\s+2 \s-2B\s+2). [Another simple test of function-definition and recursion.] sp NI "1. " 3.\ \ \c In \fBProblem 4-13\fP (p.73 and p.344), Winston & Horn define \s-2OUR-INTERSECTION\s+2 as .sp -0.5 .LB nf .ta \w'\0\0'u +\w'(\s-2COND\s+2 'u +\w'('u (\s-2DEFUN\s+2 \s-2OUR-INTERSECTION\s+2 (\s-2X\s+2 \s-2Y\s+2) (\s-2COND\s+2 ((\s-2NULL\s+2 \s-2X\s+2) \s-2NIL\s+2) ((\s-2MEMBER\s+2 (\s-2CAR\s+2 \s-2X\s+2) \s-2Y\s+2) 9Mark setNote that for all projects, choice of experiments and .ul presentation and analysis of results in the final report forms a particularly important part of the assessment. Due on Friday April 10th (the last day of the Winter term). .in-4n LE I hope that it will be found useful to discuss project progress in class and demonstrate them when complete. .sp There will be .ul no extension of deadlines on the project, except for good, documented, medical reasons.               Bot.LB NP .ul Project proposal (0%) .in+4n A (nominally) two-page report which describes the project and defines its scope. Although this is not assessed, it .ul must be handed in on time. Due on Tuesday February 3rd. in-4n .NP .ul Interim report (10%) .in+4n Detailed specification of the project, with plans for implementing the program and investigating its behavior. Must describe how the program's performance is to be assessed, and what tests and analysis are planned. Due on Tuesday March 3rd. .in-4n .NP .ul Final report (25%) .in+4n Complete demonstration and documentation of what was done. Note that for all projects, choice of experiments and .ul92% Ma general frame system for knowledge representation ATN parser for a simple subset of English NP .ul a version of Marcus's deterministic wait-and-see parsing scheme .NP a semantic grammar interpreter in a particular domain .NP ul a concept learning system (eg the M procedure). LE Of course, you may suggest your own project topic. pp The project is to be undertaken as a scientific investigation. It is \fInot\fR a test of whether you can hack together a system. Grades are given for the reports, not for programs (although programs may be included in an Appendix). As well as constructing a system, you are expected to investigate and analyze its performance and report on your findings. All projects will be done in L\s-2ISP\s+2 unless there are \fIvery\fR special reasons for using another language (to be discussed and agreed with me). .pp I intend that projects will mostly be done in pairs. Assessment will be based on written documents as follows: .LB NP85 M.ul an experimental comparison of several different search procedures in a particul\ar domain .NP ul a game-playing program for some simple two-person game using the alpha-beta tre\e-searching techniquerule interpreter, and the implementation of some rudimentary rule-based syste\m in it NP .ul a system which converts well-formed formulae in first-order predicate calculus \into clausal form NP .ul a resolution theorem prover .NP .ul a STRIPS-like planner in a blocks world .NP .ul a system to simplify, differentiate, and integrate expressions in symbolic alge\bra .NP .ul a general frame system for knowledge representation0 MFully commented code, together with a brief user manual and transcript of an interactive session, must be handed in on Tuesday January 27th. There will be ul no extension of this deadline, except for good, documented, medical reasons. .sp 3 Attachment 1 blocks.l br Attachment 2 commented transcript .bp 0 .ce2 THE UNIVERSITY OF CALGARY DEPARTMENT OF COMPUTER SCIENCE .sp2 .ps+2 ce CPSC 533 \(em Artificial Intelligence .ps-2 sh "Project" (total of 35% of course marks) .pp The aim of the project is to give you hands-on experience of designing and implementing an AI program of modest size, and investigating and reporting on its performance. Possible project topics are: LB NP .ul an experimental comparison of several different search procedures in a particul\[70`75 MMMMMMMMMMar domain .NP .ul a game-playing program for some simple two-person game using the alpha-beta tre\e-searching technique .NP .ul a rule interpreter, and the implementation of some rudimentary rule-based syste\m in it .NPDEPARTMENT OF COMPUTER SCIENCE .sp2 .ps+2 .ce CPSC 533 \(em Artificial Intelligence .ps-2 .sh "Assignment 1. Introduction to \s-2LISP\s+2" (10% of course marks) .sp Fully commented code, together with answers to the questions posed in Q.3, must be handed in on Tuesday January 20th. There will be .ul no extension of this deadline, except for good, documented, medical reasons. .LB "1. "58MMMMMMMMMMMMMMMark set Possible project topics are: .LB .NP .ul an experimental comparison of several different search procedures in a particul\ar domain .NP .ul a game-playing program for some simple two-person game using the alpha-beta tre\e-searching technique .NP .ul a rule interpreter, and the implementation of some rudimentary rule-based syste\m in it .NP .ul a system which converts well-formed formulae in first-order predicate calculus \Addison-Wesley, Reading, Massachusetts, 1984. .sp .ti-4n Winston, P.H. \fIArtificial Intelligence\fR. Addison-Wesley, Reading, Massachusetts, 1984 (second edition). .sp .ti-4n Winston, P.H. & Horn, B.K.P. \fIL\s-2ISP\s+2\fR. Addison-Wesley, Reading, Massachusetts, 1984 (second edition). .LE .bp 0 .ce2 THE UNIVERSITY OF CALGARY79pm 1.599pm 1.59 [5`6[2@70  [1@2[1@0 .NP .ul an experimental comparison of several different search procedures in a particul\ar domain .NP .ul a game-playing program for some simple two-person game using the alpha-beta tre\e-searching technique .NP .ul a rule interpreter, and the implementation of some rudimentary rule-based syste\m in it .NP .ul a system which converts well-formed formulae in first-order predicate calculus \[70`61.ul a system which converts well-formed formulae in first-order predicate calculus \into clausal form NP .ul a resolution theorem prover NP .ul a STRIPS-like planner in a blocks world .NP .ul a system to simplify, differentiate, and integrate expressions in symbolic alge\bra NP ul a general frame system for knowledge representation .NP .ul ATN parser for a simple subset of English NP .ul a version of Marcus's deterministic wait-and-see parsing schemesemantic grammar interpreter in a particular domain .NP ul a concept learning system (eg the M procedure). .LE70a concept learning system (eg the M procedure). .LE Of course, you may suggest your own project topic. pp The project is to be undertaken as a scientific investigation. It is \fInot\fR a test of whether you can hack together a system. Grades are given for the reports, not for programs (although programs may be included in an Appendix). As well as constructing a system, you are expected to investigate and analyze its performance and report on your findings. All projects will be done in L\s-2ISP\s+2 unless there are \fIvery\fR special reasons for using another language (to be discussed and agreed with me). .pp I intend that projects will mostly be done in pairs. Assessment will be based on written documents as follows: .LB Project proposal (0%) in+4n A (nominally) two-page report which describes the project and defines its scope. Although this is not assessed, it must be handed in on time. Due on Tuesday February 3rd. in-4n .NP ul7Wrote /vaxb.userb/profs/ian/courses/670/info.sheet--Loading shell...doneroff -a2                   **-Emacs: *shell* 2:39pm 1.59 Mail[1] (Shell: run)----All [5@VAXB [15`~/corseureses/670/info.sheet; lpq -Palw240pm 1.340pm 1.3 no entries vaxb: sending to vaxa Rank Owner Job Files Total Size 1st ian 287 (standard input) 20252 bytes VAXB1pm 1.50 Mail[21pm 1.50 Mail[22pm 1.232pm 1.233pm 1.38 Mail[13pm 1.38 Mail[14pm 1.54 Mail[04pm 1.54 Mail[05pm 1.37 Mail[15pm 1.37 Mail[16pm 1.256pm 1.257pm 0.87 Mail[07pm 0.87 Mail[08pm 1.17 Mail[18pm 1.17 Mail[19pm 1.31 Mail[29pm 1.31 Mail[250pm 1.4450pm 1.441pm 0.87 Mail[11pm 0.87 Mail[12pm 0.84 Mail[02pm 0.84 Mail[03pm 0.713pm 0.714pm 0.454pm 0.455pm 0.215pm 0.216pm 0.46pm 0.47pm 0.667pm 0.668pm 0.548pm 0.549pm 0.53 Mail[19pm 0.53 Mail[13:00pm 1.093:00pm 1.091pm 1.11pm 1.12pm 1.052pm 1.053pm 1.093pm 1.094pm 0.944pm 0.945pm 0.625pm 0.62C-z- rLoading rmail...doneCounting messages...20Garbage collecting...Counting messages...20donenew messages...done (1)Wrote /vaxb.userb/profs/ian/RMAIL1 new message readFrom: Danhua Mo  Date: 28 Aug 1987 14:00-MST Subject: paper To: ian@vaxb.LOCAL  Hi. I hate to bother you again, but it would be highly appreciated if you would take a look at the final draft of my paper. I put it in your mbox on the 7th floor. I am very busy at making the format right now because the deadline is on Monday. ---mo- Emacs: RMAIL 3:05pm 0.62 Mail[1] (RMAIL 38/38 Narrow) ----All------6pm 0.58[1] (RMAIL 38/38 Narrow) ----All-----6pm 0.58[1] (Text Fill)----77%-----7pm 0.87pm 0.88pm 0.708pm 0.709pm 0.73[09pm 0.73[010pm 1.27[110pm 1.27[11pm 0.931pm 0.932pm 0.58[02pm 0.58[03pm 0.99[13pm 0.99[14pm 1.00[04pm 1.00[05pm 0.95[15pm 0.95[16pm 0.906pm 0.907pm 0.97[27pm 0.97[28pm 1.48pm 1.49pm 1.04[09pm 1.04[020pm 0.82[320pm 0.82[31pm 0.64[11pm 0.64[12pm 0.99[32pm 0.99[33pm 1.23[13pm 1.23[14pm 1.12[04pm 1.12[05pm 1.72[15pm 1.72[1Date: Fri, 28 Aug 87 12:28:28 MDT From: Tim Bell To: cleary@vaxb.LOCAL, ian@vaxb.LOCAL Subject: Text compression corpusere is a list of the texts we decided on a while ago for the corpus. Could you please give me a pathname where I can get the texts that you volunteered? Also any suggestions, changes ...  ta, tim ------------------------------------------------------------------------- Text Compression Corpus abbrev source book1 Hardy - Far from the madding crowd book2 Ian's speech book paper1 Ian paper2 Ian paper3 Ian paper4 John paper5 John paper6 John news usenet sample progc C (Dan) ~100k progp Pascal (John) progm Modula-27/38,answered Narrow) ----TopFrom: Danhua Mo  Date: 28 Aug 1987 14:00-MST Subject: paper To: ian@vaxb.LOCALi. I hate to bother you again, but it would be highly appreciated if you would take a look at the final draft of my paper. I put it in your mbox on the 7th floor. I am very busy at making the format right now because the deadline is on Monday.  ---mo               8/38 Narrow) ----All---------6pm 1.75[26pm 1.75[27pm 1.217pm 1.218pm 1.19[18pm 1.19[1Mark set.pn 0 ls1 .ce2 THE UNIVERSITY OF CALGARY DEPARTMENT OF COMPUTER SCIENCE .sp2 .ps+2 .ce CPSC 670 \(em Artificial Intelligence .ps-2 .sp3 .LB NP .ul Instructor. John H Andreae, MA\ 782, 220-7299 Ian H Witten, MA\ 786, 220-6780 sp .NP ul Office hours. Tuesdays and Thursdays 14.00-15.00 (both instructors) .sp NP .ul Lectures. ???, Tuesdays and Thursdays 11.00-12.15 sp NPTop [11`s.** [36` and9pm 1.409pm 1.40     [1@M[1@T[1@A[1@ [1@2[1@5[1@6   .ul Assessment .ta \w'Winter Term 'u +\w'Midterm exam (in class time) 'u .sp Fall Term Assignment \0\07\(12% .br Project: .br \0\0\0\0progress report \0\05% .br \0\0\0\0final report \012\(12% .br \0\0\0\0in-class presentation \0\02\(12% .br Take-home exam \022\(12%  3%   Query replace: % Mark set Query replacing % with :   Project: .br \0\0\0\0progress report \0\05 br \0\0\0\0final report \012\(12 .br \0\0\0\0in-class presentation \0\02\(12 br Take-home exam \022\(12 sp Winter Term Class presentations \0\07\(12 .br Project: br \0\0\0\0progress report \0\05% .br \0\0\0\0final report \012\(12% br \0\0\0\0in-class presentation \0\02\(12% Term paper \022\(12% sp ____ h'-\w'total 'u'total\ \ 100%p .sh "Planned timetable" sp .nf730pm 1.57[230pm 1.57[2% Done [8@I-search: M ... i ....sh "Bibliography" sp Here is a representative selection of recent books on AI.c ...h ...p ti-4n augeland, J. (1985) ul Artificial intelligence: the very idea. MIT Press, Cambridge, MA. .sp ti-4n Kanal, L.N. and Lemmer, J.F.\0(Editors) (1986) ul Uncertainty in artificial intelligence. North Holland, Amsterdam. .sp ti-4n R.S. Michalski, J.G. Carbonell, and T.M. Mitchell (Editors) (1983) ul Machine learning -- an artificial intelligence approach. Tioga, Palo Alto, CA. .sp ti-4n R.S. Michalski, J.G. Carbonell, and T.M. Mitchell (Editors) (1986) \fIMachine learning Volume 2\fP. Morgan Kaufmann, Los Altos, CA. sp .ti-4n T.M. Mitchell, J.G. Carbonell, and R.S. Michalski (Editors) (1986) ul Machine learning -- a guide to current research. Kluwer, Boston, MA. 35Mark set Mark set Defining kbd macro... Def) ----All Def)----35%Mark setI-search: ...[6` ^J ...Failing I-search: ^JMark setKeyboard macro defined) ----All----)----35%----Auto-saving...Keyboard macro defined1pm 2.35[51pm 2.35[52pm 1.36[22pm 1.36[23pm 1.123pm 1.124pm 0.88[14pm 0.88[15pm 0.765pm 0.766pm 0.81[26pm 0.81[27pm 0.72[17pm 0.72[18pm 0.85[28pm 0.85[29pm 0.73[19pm 0.73[140pm 1.18[240pm 1.18[21pm 0.64[11pm 0.64[12pm 1.44[22pm 1.44[23pm 1.183pm 1.184pm 1.08[14pm 1.08[15pm 0.835pm 0.836pm 0.65[26pm 0.65[27pm 0.737pm 0.738pm 0.76[18pm 0.76[19pm 0.979pm 0.9750pm 1.93[29] (RMAIL 38/38 Narrow) ----All50pm 1.93[29] (Text Fill)----35%1pm 2.281pm 2.282pm 2.40[252pm 2.40[253pm 1.71[243pm 1.71[244pm 2.29[184pm 2.29[18Loading sendmail...donemailalias...doneTo: Subject: FCC: mbox.out --text follows this line--     --Emacs: *mail* 3:54pm 2.29[18] (Mail Fill)----All g**aines stuff 5pm 4.55pm 4.56pm 4.056pm 4.057pm 3.83[177pm 3.83[178pm 4.06[208pm 4.06[209pm 4.99[189pm 4.99[184:00pm 5.10[164:00pm 5.10[161pm 4.73[181pm 4.73[18please sti leave me the Bratkostuff about extending ID3 to dealwith intervals, and the b definitive book on decision trees. Have a good trip, wherever ian 2pm 4.75[152pm 4.75[15Sending...Wrote /vaxb.userb/profs/ian/mbox.outSending...done-- Defining kbd macro... Def)----All Def)----35%Mark setI-search: ...Mark set[6` [10`[1@,[1@ Mark set[4@R.S.3pm 3.22[293pm 3.22[29I-search: ... Mark setKeyboard macro defined)----All----)----35%----C-z-Carbonell, J.G[1@.[35`[38`Mitchell, T.M[1@.  Michalski, R.S., Carbonell, J.[2@G.[39`Mitchell, T.M[1@.  Mitchell, T.M[1@. Carbonell, J.G., and Michalski, R.[2@S. Wrote /vaxb.userb/profs/ian/courses/670/info.sheet--4pm 3.074pm 3.07 Michalski, R.S., Carbonell, J.G., and Mitchell, T.M. (Editors) (1986) \fIMachine learning Volume 2\fP. Morgan Kaufmann, Los Altos, CA. Mitchell, T.M., Carbonell, J.G[23@., and Michalski, R.S. VAXB roff -a2 ~/courses/670/info.sheet; lpq -Palw2 no entries  vaxb: sending to vaxa Rank Owner Job Files Total Size 1st ian 287 (standard input) 20252 bytes VAXB        --**-Emacs: *shell* 4:04pm 3.07[29] (Shell: run)----All----------.sp .ti-4n Nilsson, N.J.41cd ~/courses/670Directory /vaxb.userb/profs/ian/courses/670/roff VAXB roff  -a2 info.sheet; lpq -Palw2 5pm 3.39[255pm 3.39[255pm 3.39[25alw2 is ready and printing Rank Owner Job Files Total Size active andrews 772 info.dvi, regs.dvi 20308 bytes vaxb: sending to vaxa Rank Owner Job Files Total SizeBotVAXB6pm 2.44[246pm 2.44[246pm 2.44[247pm 3.68[197pm 3.68[197pm 3.68[198pm 5.36[178pm 5.36[178pm 5.36[179pm 5.409pm 5.409pm 5.4010pm 4.73[1810pm 4.73[1810pm 4.73[181pm 5.09[151pm 5.09[151pm 5.09[152pm 4.73[162pm 4.73[162pm 4.73[163pm 3.97[183pm 3.97[183pm 3.97[184pm 3.09[9] (Mail Fill)----All-4pm 3.09[9] (Shell: run)----Bot-4pm 3.09[9] (Text Fill)----41%-5pm 1.71[55pm 1.71[55pm 1.71[56pm 2.38[76pm 2.38[76pm 2.38[77pm 1.75[27pm 1.75[27pm 1.75[28pm 1.00[18pm 1.00[18pm 1.00[19pm 0.669pm 0.669pm 0.6620pm 0.76[220pm 0.76[220pm 0.76[21pm 0.45[01pm 0.45[01pm 0.45[02pm 0.38[22pm 0.38[22pm 0.38[23pm 0.543pm 0.543pm 0.544pm 0.64[04pm 0.64[04pm 0.64[05pm 0.91[25pm 0.91[25pm 0.91[26pm 1.21[16pm 1.21[16pm 1.21[17pm 0.987pm 0.987pm 0.988pm 1.378pm 1.378pm 1.379pm 1.23[29pm 1.23[29pm 1.23[230pm 1.90[430pm 1.90[430pm 1.90[41pm 1.39[31pm 1.39[31pm 1.39[32pm 1.06[12pm 1.06[12pm 1.06[13pm 0.65[23pm 0.65[23pm 0.65[24pm 1.39[14pm 1.39[14pm 1.39[15pm 2.55[35pm 2.55[35pm 2.55[36pm 1.576pm 1.576pm 1.577pm 1.00[17pm 1.00[17pm 1.00[18pm 0.688pm 0.688pm 0.689pm 0.549pm 0.549pm 0.5440pm 0.9640pm 0.9640pm 0.961pm 1.37[31pm 1.37[31pm 1.37[32pm 1.28[12pm 1.28[12pm 1.28[13pm 1.93[33pm 1.93[33pm 1.93[34pm 2.10 Mail[3] (Mail Fill)----All4pm 2.10 Mail[3] (Shell: run)----Bot4pm 2.10 Mail[3] (Text Fill)----41%5pm 1.04 Mail[15pm 1.04 Mail[15pm 1.04 Mail[16pm 0.58 Mail[06pm 0.58 Mail[06pm 0.58 Mail[07pm 0.377pm 0.377pm 0.378pm 0.36 Mail[18pm 0.36 Mail[18pm 0.36 Mail[19pm 1.73 Mail[39pm 1.73 Mail[39pm 1.73 Mail[350pm 1.18 Mail[250pm 1.18 Mail[250pm 1.18 Mail[21pm 1.01pm 1.01pm 1.02pm 0.53 Mail[02pm 0.53 Mail[02pm 0.53 Mail[03pm 2.00 Mail[33pm 2.00 Mail[33pm 2.00 Mail[3g**Auto-saving...doneFrom: Danhua Mo Date: 28 Aug 1987 14:00-MST Subject: paper To: ian@vaxb.LOCAL  Hi. I hate to bother you again, but it would be highly appreciated if you would take a look at the final draft of my paper. I put it in your mbox on the 7th floor. I am very busy at making the format right now because the deadline is on Monday. ---mo- Emacs: RMAIL 4:53pm 2.00 Mail[3] (RMAIL 38/38 Narrow) ----All-----Counting new messages...done (1)Wrote /vaxb.userb/profs/ian/RMAIL1 new message readDate: Fri, 28 Aug 87 16:43:11 MDT From: Mr. Invisible To: ian@vaxb.LOCAL  # to unbundle, sh this file echo inter.c 1>&2 cat >inter.c <<'End of inter.c' #include "weird.h" #define MAXLINES 255 /* number of lines read in a time = max # of\lines of one command result */ typedef char FILENAME[MAXSTR+1]; extern char *homep; FILENAME namearray[MAXLINES+1]; /* array of file lines */ FILENAME *namep = namearray, *memp = namearray; FILE *comfp; FILE *resfp; FILE *locfp; struct rightentry *ihead, *itail; /* pointers to list of commands to be saved\*/ int lineno = 0; short done = 0, abortit = 0, save = 0, haveold = 0; inter(result, command, location) char *result, *command, *location; 9/39 Narrow) ----Top4pm 1.88[2] (RMAIL 39/39 Narrow) ----Top-----4pm 1.88[2] (Shell: run)----Bot-----4pm 1.88[2] (Text Fill)----41%-----No following nondeleted messageFrom: Danhua Mo  Date: 28 Aug 1987 14:00-MST Subject: paper To: ian@vaxb.LOCAL  Hi. I hate to bother you again, but it would be highly appreciated if you would take a look at the final draft of my paper. I put it in your mbox on the 7th floor. I am very busy at making the format right now because the deadline is on Monday.  ---mo             8/39 Narrow) ----AllDate: Fri, 28 Aug 87 16:43:11 MDT From: Mr. Invisible To: ian@vaxb.LOCAL  # to unbundle, sh this file echo inter.c 1>&2 cat >inter.c <<'End of inter.c' #include "weird.h" #define MAXLINES 255 /* number of lines read in a time = max # of\lines of one command result */ typedef char FILENAME[MAXSTR+1]; extern char *homep; FILENAME namearray[MAXLINES+1]; /* array of file lines */ FILENAME *namep = namearray, *memp = namearray; FILE *comfp; FILE *resfp; FILE *locfp; struct rightentry *ihead, *itail; /* pointers to list of commands to be saved\*/ int lineno = 0; short done = 0, abortit = 0, save = 0, haveold = 0; inter(result, command, location) char *result, *command, *location;9/39 Narrow) ----Top5pm 1.25pm 1.25pm 1.26pm 0.63[06pm 0.63[06pm 0.63[0Save file /vaxb.userb/profs/ian/RMAIL? (y or n) nCheck all saved[>52l[>37h[1] + Done ( roff -a2 assignments; lpq -Palw2 ) VAXB ^Dlogout You are still logged in somewhere