WEBVTT 00:00:09.330 --> 00:00:10.970 PROFESSOR PATRICK WINSTON: Ladies and gentlemen, the 00:00:10.970 --> 00:00:13.670 engineers drinking song. 00:00:13.670 --> 00:00:16.309 Back in the day, I've drunk quite a lot to that song. 00:00:16.309 --> 00:00:20.420 And as drinking songs go, it's not bad. 00:00:20.420 --> 00:00:24.260 I caution you, however, before playing this song in the 00:00:24.260 --> 00:00:28.240 presence of small children, audition it first. 00:00:28.240 --> 00:00:32.460 Some of the verses are sufficiently gross as to make 00:00:32.460 --> 00:00:37.130 a sailor go beyond blushing. 00:00:37.130 --> 00:00:39.380 It's an interesting song because there are an infinite 00:00:39.380 --> 00:00:41.580 number of verses. 00:00:41.580 --> 00:00:43.840 Here's the mathematical proof. 00:00:43.840 --> 00:00:46.920 Suppose there were a finite number of verses. 00:00:46.920 --> 00:00:49.080 Then there would be a last verse. 00:00:49.080 --> 00:00:52.540 And if there were a last verse, then some drunk alumni 00:00:52.540 --> 00:00:54.570 would compose a new one. 00:00:54.570 --> 00:00:58.400 Therefore, there is no last verse, the size is not finite, 00:00:58.400 --> 00:01:02.430 and there are an infinite number of verses. 00:01:02.430 --> 00:01:05.610 I play it for you today because I'm an engineer. 00:01:05.610 --> 00:01:07.336 I like to build stuff. 00:01:07.336 --> 00:01:09.010 I build stuff out of wood. 00:01:09.010 --> 00:01:10.810 I build stuff out of metal. 00:01:10.810 --> 00:01:12.830 I build stuff out of rocks. 00:01:12.830 --> 00:01:16.470 And I especially like to write programs. 00:01:16.470 --> 00:01:20.180 I don't know, sometimes people come to me and say, I'm 00:01:20.180 --> 00:01:21.960 majoring in computer science, but I don't 00:01:21.960 --> 00:01:23.030 like to write programs. 00:01:23.030 --> 00:01:26.470 I've always been mystified by that. 00:01:26.470 --> 00:01:29.900 I mean, if you want to show how tough you are, you can go 00:01:29.900 --> 00:01:32.430 bungee jumping or drive a nail through your hand or something 00:01:32.430 --> 00:01:33.680 like that instead. 00:01:35.850 --> 00:01:38.750 But I've written quite a few programs for demonstrating 00:01:38.750 --> 00:01:42.650 stuff in this subject. 00:01:42.650 --> 00:01:46.520 They're all written in Java, principally because I can 00:01:46.520 --> 00:01:48.660 therefore make them available to you and to the rest of the 00:01:48.660 --> 00:01:50.785 world by way of Web Start. 00:01:50.785 --> 00:01:55.050 A few weeks ago, I was mucking around with the system and 00:01:55.050 --> 00:01:57.490 broke the version on the server, and within 15 minutes, 00:01:57.490 --> 00:02:01.490 I got an email from somebody in the depths of Anatolia 00:02:01.490 --> 00:02:03.960 complaining about it and asking me to bring it back up. 00:02:07.500 --> 00:02:09.699 This particular program is patterned 00:02:09.699 --> 00:02:14.830 after an early AI classic. 00:02:14.830 --> 00:02:19.130 And it was the business end of a program written by Terry 00:02:19.130 --> 00:02:24.290 Winograd, who became, and is, a professor of computer 00:02:24.290 --> 00:02:27.220 science at Stanford University-- 00:02:27.220 --> 00:02:28.940 which is on the west coast for those of you-- 00:02:31.990 --> 00:02:35.510 on the strength of his work on the natural language front end 00:02:35.510 --> 00:02:37.100 of this program. 00:02:37.100 --> 00:02:40.260 But the natural language part is not what makes it of 00:02:40.260 --> 00:02:44.079 interest for us today. 00:02:44.079 --> 00:02:48.440 It's more the other kinds of stuff. 00:02:48.440 --> 00:02:51.550 Let's pile these things up. 00:02:51.550 --> 00:02:55.320 Now, I'm going to ask to do something, maybe put 00:02:55.320 --> 00:02:58.970 B2 on top of B7. 00:03:07.140 --> 00:03:07.840 Not bad. 00:03:07.840 --> 00:03:09.320 How about B6 on B3? 00:03:18.500 --> 00:03:20.880 This program's kind of clever. 00:03:25.140 --> 00:03:26.579 Let me do one more. 00:03:26.579 --> 00:03:28.280 Let's put B7 on B2. 00:03:39.250 --> 00:03:40.510 OK, now let's see. 00:03:40.510 --> 00:03:42.530 Maybe B5 on B2? 00:03:46.170 --> 00:03:49.730 B4 on B3 first, maybe? 00:03:52.820 --> 00:03:55.885 Oh, I must have clicked the wrong button. 00:03:55.885 --> 00:03:56.675 Oh, there it goes. 00:03:56.675 --> 00:03:58.780 OK. 00:03:58.780 --> 00:04:02.716 Let's put B4 on B1. 00:04:02.716 --> 00:04:08.190 Agh, my mouse keeps getting out of control. 00:04:15.660 --> 00:04:17.519 Now, let's put B1 on B2. 00:04:17.519 --> 00:04:18.779 This is an example I'm actually going to 00:04:18.779 --> 00:04:20.029 work out on the board. 00:04:23.646 --> 00:04:25.740 Oh, I see. 00:04:25.740 --> 00:04:27.750 My touch pad accidentally got activated. 00:04:27.750 --> 00:04:30.440 B1 on B2. 00:04:44.550 --> 00:04:45.800 Now, let's ask a question. 00:04:58.075 --> 00:04:59.560 OK. 00:04:59.560 --> 00:05:00.190 Well. 00:05:00.190 --> 00:05:01.636 SPEAKER 2: [SINGING] 00:05:01.636 --> 00:05:03.564 PROFESSOR PATRICK WINSTON: Stop. 00:05:03.564 --> 00:05:07.420 SPEAKER 3: [LAUGHTER] 00:05:07.420 --> 00:05:08.866 PROFESSOR PATRICK WINSTON: Had enough of that. 00:05:11.760 --> 00:05:12.550 Let's see. 00:05:12.550 --> 00:05:15.460 Why did you put-- 00:05:15.460 --> 00:05:22.720 why did you want to get rid of B4? 00:05:22.720 --> 00:05:24.660 OK, one-- 00:05:24.660 --> 00:05:28.540 SPEAKER 2: [SINGING] 00:05:28.540 --> 00:05:34.010 PROFESSOR PATRICK WINSTON: Maybe they think, that's what 00:05:34.010 --> 00:05:35.575 happens when you use software you write yourself. 00:05:42.010 --> 00:05:51.662 Why did you want to clear the top of B2? 00:05:51.662 --> 00:05:55.050 Did I do that? 00:05:55.050 --> 00:06:01.050 Why did you clear the top of B1? 00:06:01.050 --> 00:06:01.520 OK. 00:06:01.520 --> 00:06:03.400 SPEAKER 2: [SINGING] 00:06:03.400 --> 00:06:06.700 SPEAKER 3: [LAUGHTER] 00:06:06.700 --> 00:06:08.460 PROFESSOR PATRICK WINSTON: Oh, it's haunting me. 00:06:08.460 --> 00:06:09.290 Yeah. 00:06:09.290 --> 00:06:13.350 So the drinking song is easily offended, I guess. 00:06:29.160 --> 00:06:31.310 But I won't develop that scenario again. 00:06:31.310 --> 00:06:35.260 What I want to show you is that this program looks like 00:06:35.260 --> 00:06:39.430 it's kind of smart, and it somehow can answer questions 00:06:39.430 --> 00:06:40.230 about its own behavior. 00:06:40.230 --> 00:06:41.800 Have you ever written a program that's answered 00:06:41.800 --> 00:06:44.020 questions about its own behavior? 00:06:44.020 --> 00:06:44.620 Probably not. 00:06:44.620 --> 00:06:46.322 Would you like to learn how to do that? 00:06:46.322 --> 00:06:47.680 OK. 00:06:47.680 --> 00:06:49.470 So by the end of the hour, you'll be able to write this 00:06:49.470 --> 00:06:53.400 program and many more like it that know how to answer 00:06:53.400 --> 00:06:55.040 questions about their own behavior. 00:06:55.040 --> 00:06:57.020 There have been tens of thousands of such programs 00:06:57.020 --> 00:07:00.175 written, but only by people who know the stuff I'm going 00:07:00.175 --> 00:07:03.080 to tell you about right now, OK? 00:07:03.080 --> 00:07:05.240 So what I want to do is I want to start by taking this 00:07:05.240 --> 00:07:08.500 program apart on the board and talking to you about the 00:07:08.500 --> 00:07:12.670 modules, the subroutines that it contains. 00:07:12.670 --> 00:07:14.070 So here it is. 00:07:17.440 --> 00:07:20.280 The first thing we need to think about, 00:07:20.280 --> 00:07:21.530 here are some blocks. 00:07:23.600 --> 00:07:27.500 What has to happen if I'm going to put the bottom block 00:07:27.500 --> 00:07:29.570 on the larger block? 00:07:29.570 --> 00:07:32.680 Well, first of all, I have to find space for it. 00:07:32.680 --> 00:07:35.340 Then I have to grasp the lower block. 00:07:35.340 --> 00:07:38.380 And I have to move it and I have to ungrasp it. 00:07:38.380 --> 00:07:42.620 So those are four things I need to do in order to achieve 00:07:42.620 --> 00:07:44.140 what I want to do. 00:07:44.140 --> 00:07:52.450 So therefore, I know that the put-on method has four pieces. 00:07:52.450 --> 00:07:57.480 It has to find space on the target block. 00:07:57.480 --> 00:08:05.500 It has to grasp the block that it's been commanded to move. 00:08:05.500 --> 00:08:10.580 Then it has to move, and then it has to ungrasp. 00:08:15.720 --> 00:08:18.680 But taking hints from some of the questions that it did 00:08:18.680 --> 00:08:23.350 answer before I got haunted by the music, taking our cue from 00:08:23.350 --> 00:08:26.850 that, we know that in order to grasp something, in this 00:08:26.850 --> 00:08:30.150 particular world, you can't have anything on top of it. 00:08:30.150 --> 00:08:36.929 So grasp, therefore, may call clear top in order to get 00:08:36.929 --> 00:08:41.710 stuff off from the target object. 00:08:41.710 --> 00:08:45.020 And that may happen in an iterative loop because there 00:08:45.020 --> 00:08:47.450 may be several things on top. 00:08:47.450 --> 00:08:49.120 And how do you get rid of stuff? 00:08:49.120 --> 00:08:50.370 Well, by calling get rid of. 00:08:54.810 --> 00:08:57.660 And that may go around a loop several times. 00:08:57.660 --> 00:09:04.210 And then, the way you get rid of stuff is by calling put-on. 00:09:04.210 --> 00:09:06.780 So that gives us recursion, and it's from the recursion 00:09:06.780 --> 00:09:09.110 that we get a lot of the apparent complexity of the 00:09:09.110 --> 00:09:13.920 program's behavior when it solves a problem. 00:09:13.920 --> 00:09:16.830 Now, in order to find space, you also have to 00:09:16.830 --> 00:09:19.070 call get rid of. 00:09:19.070 --> 00:09:20.820 So that's where I meant to put this other iterative 00:09:20.820 --> 00:09:22.270 loop, not down here. 00:09:22.270 --> 00:09:25.400 Cleat top has got the iterative loop inside of it. 00:09:25.400 --> 00:09:26.930 So that's the structure of the program. 00:09:26.930 --> 00:09:29.610 It's extremely simple. 00:09:29.610 --> 00:09:31.810 And you might say to me, well, how can you get such 00:09:31.810 --> 00:09:34.140 apparently complex-looking behavior out of 00:09:34.140 --> 00:09:35.065 such a simple program? 00:09:35.065 --> 00:09:36.665 A legitimate question. 00:09:36.665 --> 00:09:41.090 But before we tackle that one head on, I'd like to do a 00:09:41.090 --> 00:09:42.870 simulation of this program with a very 00:09:42.870 --> 00:09:44.120 simple blocks problem. 00:09:48.790 --> 00:09:51.380 And it's the one I almost showed you, but 00:09:51.380 --> 00:09:53.180 it goes like this. 00:09:53.180 --> 00:09:54.786 Here's B1. 00:09:54.786 --> 00:09:58.920 We'll call this BX because I forgot its name. 00:09:58.920 --> 00:10:01.250 Here's BY. 00:10:01.250 --> 00:10:03.380 And here's B2. 00:10:03.380 --> 00:10:12.080 And the task is to put B1 on B2. 00:10:12.080 --> 00:10:17.090 And according to our system diagram, that results in four 00:10:17.090 --> 00:10:19.410 calls to subroutines. 00:10:19.410 --> 00:10:20.660 We have to find space. 00:10:25.750 --> 00:10:27.995 We have to grasp B1. 00:10:34.270 --> 00:10:38.630 We have to move, and then we ungrasp. 00:10:44.120 --> 00:10:46.260 Now, the way we grasp something is the first thing 00:10:46.260 --> 00:10:49.900 we have to do is clear off its top. 00:11:03.250 --> 00:11:04.640 So grasp calls clear top. 00:11:08.760 --> 00:11:10.900 And clear top in turn calls get rid of. 00:11:18.080 --> 00:11:18.620 And let me see. 00:11:18.620 --> 00:11:19.650 Let me keep track of these. 00:11:19.650 --> 00:11:23.630 This is clearing the top of B1, and this is 00:11:23.630 --> 00:11:26.230 getting rid of BX. 00:11:26.230 --> 00:11:34.685 And the way we get rid of BX is by putting BX on the table. 00:11:38.710 --> 00:11:44.800 And then that in turn causes calls to another find space, 00:11:44.800 --> 00:11:48.314 another grasp, another move, and another ungrasp. 00:11:48.314 --> 00:11:51.410 So that's a little trace of the program as it works on 00:11:51.410 --> 00:11:53.960 this simple problem. 00:11:53.960 --> 00:11:56.440 So how does it go about answering the questions that I 00:11:56.440 --> 00:11:59.660 demonstrated to you a moment ago? 00:11:59.660 --> 00:12:03.340 Let's do that by using this trace. 00:12:03.340 --> 00:12:09.810 So how, for example, does it answer the question, why did 00:12:09.810 --> 00:12:11.060 you get rid of BX? 00:12:17.980 --> 00:12:19.520 [INAUDIBLE], what do you think? 00:12:19.520 --> 00:12:21.343 How can it answer that question? 00:12:21.343 --> 00:12:22.593 SPEAKER 4: [INAUDIBLE] 00:12:26.280 --> 00:12:29.480 PROFESSOR PATRICK WINSTON: So it goes up one level and 00:12:29.480 --> 00:12:31.590 reports what it sees. 00:12:31.590 --> 00:12:36.800 So it says, and said in the demonstration, I got rid of BX 00:12:36.800 --> 00:12:40.660 because I was trying to clear the top of B1. 00:12:40.660 --> 00:12:44.325 So if I were to say why did you clear the top of B1, it 00:12:44.325 --> 00:12:47.015 would say because I was trying to grasp it. 00:12:47.015 --> 00:12:49.840 If I were to say, why did you grasp B1, it would say because 00:12:49.840 --> 00:12:51.990 I was putting B1 on B2. 00:12:51.990 --> 00:12:56.120 If I say, why did you put B1 on B2, it would say, 00:12:56.120 --> 00:12:59.910 slavishly, because you told me to. 00:12:59.910 --> 00:13:03.770 OK, so that's how it deals with why questions. 00:13:03.770 --> 00:13:06.620 How about how questions? 00:13:09.410 --> 00:13:11.680 Timothy, what do you think about that one? 00:13:11.680 --> 00:13:13.400 How would it go about answering a question about how 00:13:13.400 --> 00:13:14.650 you did something? 00:13:17.210 --> 00:13:18.400 Do you have a thought? 00:13:18.400 --> 00:13:21.428 TIMOTHY: Um, yeah, it would think about what I was trying 00:13:21.428 --> 00:13:21.750 to accomplish. 00:13:21.750 --> 00:13:21.820 PROFESSOR PATRICK WINSTON: Yeah, but 00:13:21.820 --> 00:13:22.710 how would it do that? 00:13:22.710 --> 00:13:24.550 How would the program do that? 00:13:24.550 --> 00:13:26.150 We know that answering a why question makes 00:13:26.150 --> 00:13:28.710 it go up one level. 00:13:28.710 --> 00:13:30.660 How does it answer a how question? 00:13:30.660 --> 00:13:31.800 Sebastian? 00:13:31.800 --> 00:13:32.560 SEBASTIAN: It goes down one level. 00:13:32.560 --> 00:13:34.420 PROFESSOR PATRICK WINSTON: You go down one level. 00:13:34.420 --> 00:13:40.210 So you start off all the way up here with a put-on. 00:13:40.210 --> 00:13:42.360 You will say, oh, well I did these four things. 00:13:42.360 --> 00:13:43.890 You say, why did you grasp B1? 00:13:43.890 --> 00:13:46.170 It will say because I was trying to clear its top. 00:13:46.170 --> 00:13:46.960 Why did you clear its top? 00:13:46.960 --> 00:13:48.930 Because I was trying to get rid of it. 00:13:48.930 --> 00:13:50.080 Why were you trying to get rid of it? 00:13:50.080 --> 00:13:52.890 Because I was trying to put it on the table. 00:13:52.890 --> 00:13:56.570 So that's how it answers how questions, by going down in 00:13:56.570 --> 00:14:02.280 this tree and this trace of the program of action so as to 00:14:02.280 --> 00:14:05.100 see how things are put together. 00:14:05.100 --> 00:14:07.940 What are these things that are being put together? 00:14:07.940 --> 00:14:10.460 What's the word I've been avoiding so as to bring this 00:14:10.460 --> 00:14:13.360 to a crescendo? 00:14:13.360 --> 00:14:17.310 What are these objectives, these things it wants to do? 00:14:17.310 --> 00:14:19.530 They're goals. 00:14:19.530 --> 00:14:25.780 So this thing is leaving a trace, which is a goal tree. 00:14:25.780 --> 00:14:29.260 Does that sound familiar? 00:14:29.260 --> 00:14:31.400 Three days ago, we talked about goal trees in connection 00:14:31.400 --> 00:14:33.820 with integration. 00:14:33.820 --> 00:14:36.440 So this thing is building a goal tree, also known as an 00:14:36.440 --> 00:14:38.320 and-or tree. 00:14:38.320 --> 00:14:40.940 So this must be an and tree. 00:14:40.940 --> 00:14:44.300 And if this is an and tree, are there any and nodes? 00:14:44.300 --> 00:14:45.590 Sure, there's one right there. 00:14:48.860 --> 00:14:53.030 So do you think then that you can answer questions about 00:14:53.030 --> 00:14:56.740 your own behavior as long as you build an and-or tree? 00:14:56.740 --> 00:14:58.232 Sure. 00:14:58.232 --> 00:15:00.880 Does this mean that the integration program could 00:15:00.880 --> 00:15:04.110 answer questions about its own behavior? 00:15:04.110 --> 00:15:05.990 Sure. 00:15:05.990 --> 00:15:08.140 Because they both build goal trees, and wherever you got a 00:15:08.140 --> 00:15:10.270 goal tree, you can answer certain kinds of questions 00:15:10.270 --> 00:15:11.520 about your own behavior. 00:15:15.290 --> 00:15:22.910 So let me see if in fact it really does build itself a 00:15:22.910 --> 00:15:25.660 goal tree as it solves problems. 00:15:25.660 --> 00:15:31.560 So this time, we'll put B6 on B3 this time. 00:15:35.400 --> 00:15:37.000 But watch it develop its goal tree. 00:15:42.490 --> 00:15:44.950 So in contrast to the simple example I was working on the 00:15:44.950 --> 00:15:49.726 board, this gets to be a pretty complicated goal tree. 00:15:49.726 --> 00:15:52.550 But I could still answers questions about behavior. 00:15:52.550 --> 00:15:59.430 For example, I could say, why did you put B6 on B3? 00:16:02.440 --> 00:16:04.790 Because you told me to. 00:16:04.790 --> 00:16:08.090 All right, so the complexity of the behavior is largely a 00:16:08.090 --> 00:16:10.780 consequence not of the complexity of the program in 00:16:10.780 --> 00:16:13.230 this particular case, but the building of this giant goal 00:16:13.230 --> 00:16:17.880 tree as a consequence of the complexity of the problem. 00:16:17.880 --> 00:16:20.400 This brings us to one of our previous matters-- 00:16:20.400 --> 00:16:24.130 early on to one of the gold star ideas of today. 00:16:24.130 --> 00:16:29.080 And this gold star idea goes back to a lecture given in the 00:16:29.080 --> 00:16:32.470 late '60s by Herb Simon, who was the first Nobel Laureate 00:16:32.470 --> 00:16:36.030 in the pseudo Nobel Prize for economics. 00:16:36.030 --> 00:16:36.690 Is that right, Bob? 00:16:36.690 --> 00:16:37.390 Was he the first? 00:16:37.390 --> 00:16:39.810 All right, he was the first winner of the Nobel Prize, 00:16:39.810 --> 00:16:42.800 pseudo Nobel Prize in economics. 00:16:42.800 --> 00:16:45.170 And in this lecture, which was titled "The Sciences of the 00:16:45.170 --> 00:16:50.500 Artificial," he said imagine that you're looking on a beach 00:16:50.500 --> 00:16:53.705 at the path of a ant. 00:16:53.705 --> 00:16:56.580 And he said, well, you know, the path of the ant looks 00:16:56.580 --> 00:16:58.070 extremely complicated. 00:16:58.070 --> 00:17:02.640 And you're tempted to think the ant is some kind of genius 00:17:02.640 --> 00:17:05.670 or monster brain ant. 00:17:05.670 --> 00:17:10.780 But in fact, when you take a closer look, what you discover 00:17:10.780 --> 00:17:14.490 is that there are a bunch of pebbles on the beach, and all 00:17:14.490 --> 00:17:19.608 the ant is doing is avoiding those pebbles on his way home. 00:17:19.608 --> 00:17:23.450 So the complexity of the behavior, said Simon, is a 00:17:23.450 --> 00:17:25.900 consequence of the complexity of the environment, not the 00:17:25.900 --> 00:17:27.900 complexity of the program. 00:17:27.900 --> 00:17:30.620 So that's the metaphoric Simon's ant. 00:17:30.620 --> 00:17:39.910 And what it says is that the complexity of the behavior is 00:17:39.910 --> 00:17:50.270 the max of the complexity of the program and the complexity 00:17:50.270 --> 00:17:51.520 of the environment. 00:17:58.720 --> 00:18:01.960 So that's something we'll see many times during the rest of 00:18:01.960 --> 00:18:02.830 the semester. 00:18:02.830 --> 00:18:05.000 Complex behavior, simple program. 00:18:05.000 --> 00:18:06.105 You think it's going to be complicated. 00:18:06.105 --> 00:18:09.130 It turns out to be simple because the problem has the 00:18:09.130 --> 00:18:11.810 complexity, not the program. 00:18:14.350 --> 00:18:17.350 So that brings us to check box three in today's talk, and 00:18:17.350 --> 00:18:19.520 there's a little bit of a scene here because now I want 00:18:19.520 --> 00:18:24.140 to stop talking about goal-centered programming and 00:18:24.140 --> 00:18:28.840 start talking about rule-based expert systems. 00:18:28.840 --> 00:18:35.190 The rule-based expert systems were developed in a burst of 00:18:35.190 --> 00:18:38.870 enthusiasm about the prospects for commercial applications of 00:18:38.870 --> 00:18:45.530 artificial intelligence in the mid-1980s. 00:18:45.530 --> 00:18:50.010 At that time, it was supposed lengthy articles are written, 00:18:50.010 --> 00:18:52.620 but you could account for useful aspects of human 00:18:52.620 --> 00:18:55.980 intelligence by writing all the knowledge in the form of 00:18:55.980 --> 00:18:57.870 simple rules. 00:18:57.870 --> 00:19:02.700 So if this is true, then that's true. 00:19:02.700 --> 00:19:07.070 If you want to achieve this, then do that. 00:19:07.070 --> 00:19:09.690 But all the knowledge had to be encapsulated in the form of 00:19:09.690 --> 00:19:12.440 simple rules. 00:19:12.440 --> 00:19:13.780 So what might you want to do with this? 00:19:13.780 --> 00:19:15.110 All sorts of things. 00:19:15.110 --> 00:19:16.510 Thousands of these systems were written, 00:19:16.510 --> 00:19:18.760 as I indicated before. 00:19:18.760 --> 00:19:19.890 But here's an example. 00:19:19.890 --> 00:19:21.790 I'm going to work out an example having to do with 00:19:21.790 --> 00:19:23.870 identification. 00:19:23.870 --> 00:19:28.980 And this example is patterned off of a classic program, 00:19:28.980 --> 00:19:31.885 strangely also written at Stanford, called MYCIN. 00:19:31.885 --> 00:19:34.210 It was developed to diagnose bacterial 00:19:34.210 --> 00:19:36.470 infections in the blood. 00:19:36.470 --> 00:19:37.140 So you come in. 00:19:37.140 --> 00:19:40.800 You got some horrible disease, and the doctor gets curious 00:19:40.800 --> 00:19:43.880 about what antibiotic would be perfect for your disease. 00:19:43.880 --> 00:19:45.500 He starts asking a lot of questions. 00:19:49.380 --> 00:19:54.710 So I'm not going to deal with that because that world has 00:19:54.710 --> 00:19:59.250 all kinds of unpronounceable terms like bacterioides and 00:19:59.250 --> 00:20:01.310 anaerobic and stuff like that. 00:20:01.310 --> 00:20:04.770 So it's completely analogous to talk about identifying 00:20:04.770 --> 00:20:12.210 animals in a little zoo, sort of a small town type of zoo. 00:20:12.210 --> 00:20:15.320 So I'm going to suggest that we write down on a piece of 00:20:15.320 --> 00:20:18.710 paper all the things we can observe about an animal, and 00:20:18.710 --> 00:20:23.210 then we'll try to figure out what the animal is. 00:20:23.210 --> 00:20:26.610 So I don't know, what can we start with? 00:20:26.610 --> 00:20:27.860 Has hair. 00:20:34.440 --> 00:20:37.660 Then there are some characteristics of the 00:20:37.660 --> 00:20:39.080 following form. 00:20:39.080 --> 00:20:40.330 Has claws. 00:20:45.590 --> 00:20:46.840 Sharp teeth. 00:20:53.580 --> 00:20:55.060 And forward-pointing eyes. 00:21:04.390 --> 00:21:08.120 And these are all characteristics of carnivores. 00:21:08.120 --> 00:21:10.190 We happen to have forward-pointing eyes too, but 00:21:10.190 --> 00:21:12.560 that's more because we used to swing around the trees a lot, 00:21:12.560 --> 00:21:14.420 and we needed the stereo vision. 00:21:14.420 --> 00:21:16.530 And we don't have the claws and the sharp teeth 00:21:16.530 --> 00:21:18.400 that go with it. 00:21:18.400 --> 00:21:21.030 But anyhow, those are typically characteristics of 00:21:21.030 --> 00:21:23.040 carnivores, as is eating meat. 00:21:28.250 --> 00:21:31.090 And this particular little animal we're looking at has 00:21:31.090 --> 00:21:35.435 also got spots, and it's very fast. 00:21:39.770 --> 00:21:42.550 What is it? 00:21:42.550 --> 00:21:44.480 Well, everybody says it's a cheetah. 00:21:44.480 --> 00:21:46.970 Let's see how our program would figure that out. 00:21:46.970 --> 00:21:53.210 Well, program might say, let's see if it has hair, then rule 00:21:53.210 --> 00:21:57.985 one says that that means it must be a mammal. 00:22:03.690 --> 00:22:07.450 We can imagine another rule that says if you have sharp 00:22:07.450 --> 00:22:12.410 claws, sharp teeth, and forward-pointing eyes, then 00:22:12.410 --> 00:22:14.750 you're a carnivore. 00:22:14.750 --> 00:22:17.670 And I'm using sort of hardware notation here. 00:22:17.670 --> 00:22:19.940 That's an and gate, right? 00:22:19.940 --> 00:22:23.040 So that means we have to have all of these characteristics 00:22:23.040 --> 00:22:26.170 before we will conclude that the animal is a carnivore. 00:22:32.490 --> 00:22:36.870 Now, this animal has been also observed to eat meat. 00:22:36.870 --> 00:22:40.750 So that means we've got extra evidence that the animal is 00:22:40.750 --> 00:22:42.700 carnivorous. 00:22:42.700 --> 00:22:52.020 And now, because the animal is a mammal and a carnivore and 00:22:52.020 --> 00:22:59.700 has spots, and it's very fast, then the animal is a cheetah. 00:23:05.116 --> 00:23:07.850 And I hope all of our African students agree that 00:23:07.850 --> 00:23:09.000 it must be a cheetah. 00:23:09.000 --> 00:23:09.980 It's a small zoo-- 00:23:09.980 --> 00:23:11.420 I mean, a big zoo. 00:23:11.420 --> 00:23:11.830 Who knows what it is? 00:23:11.830 --> 00:23:13.700 It's probably got some unpronouncable name-- 00:23:13.700 --> 00:23:14.990 there's possibilities. 00:23:14.990 --> 00:23:17.010 But for our small zoo, that will do. 00:23:17.010 --> 00:23:20.260 So we have group now written down in the form 00:23:20.260 --> 00:23:22.270 of these and gates. 00:23:22.270 --> 00:23:27.030 Several rules, R1, R2-- and there needs to 00:23:27.030 --> 00:23:29.040 be an and gate here-- 00:23:29.040 --> 00:23:31.665 that's R3 and an R4. 00:23:36.950 --> 00:23:40.500 All of which indicate that this animal is a cheetah. 00:23:40.500 --> 00:23:43.155 So we built ourself a little rule-based expert system that 00:23:43.155 --> 00:23:45.400 can recognize exactly one animal, but you could imagine 00:23:45.400 --> 00:23:48.540 filling out this system with other rules so that you could 00:23:48.540 --> 00:23:50.750 recognize giraffes and penguins and all the other 00:23:50.750 --> 00:23:52.590 sorts of things you find in a small zoo. 00:23:55.890 --> 00:23:57.920 So when you have a system like this that works as I've 00:23:57.920 --> 00:24:01.236 indicated, then what we're going to call that, we're 00:24:01.236 --> 00:24:04.660 going to give that a special name, and we're going to call 00:24:04.660 --> 00:24:16.560 that a forward-chaining rule-based-- 00:24:16.560 --> 00:24:17.810 because it uses rules-- 00:24:26.050 --> 00:24:30.403 expert system. 00:24:34.320 --> 00:24:38.050 And we're going to put expert in parentheses because when 00:24:38.050 --> 00:24:41.280 these things were developed, for marketing reasons, they 00:24:41.280 --> 00:24:45.470 called them expert systems instead of novice systems. 00:24:45.470 --> 00:24:47.490 But are they really experts in a human sense? 00:24:47.490 --> 00:24:51.880 Not really, because they have these knee-jerk rules. 00:24:51.880 --> 00:24:53.500 They're not equipped with anything you might want to 00:24:53.500 --> 00:24:55.800 call common sense. 00:24:55.800 --> 00:24:59.700 They don't have an ability to deal with previous cases, like 00:24:59.700 --> 00:25:01.940 we do when we go to medical school. 00:25:01.940 --> 00:25:04.170 So they really ought to be called rule-based novice 00:25:04.170 --> 00:25:06.120 systems because they reason like novices on 00:25:06.120 --> 00:25:08.170 the basis of rules. 00:25:08.170 --> 00:25:09.585 But the tradition is to call them 00:25:09.585 --> 00:25:11.590 rule-based expert systems. 00:25:11.590 --> 00:25:18.310 And this one works forward from the facts we give it to 00:25:18.310 --> 00:25:20.260 the conclusion off on the right. 00:25:20.260 --> 00:25:22.350 That's why it's a forward-chaining system. 00:25:26.850 --> 00:25:28.420 Can this system answer questions 00:25:28.420 --> 00:25:29.670 about its own behavior? 00:25:34.715 --> 00:25:36.160 [INAUDIBLE], what do you think? 00:25:36.160 --> 00:25:36.900 SPEAKER 5: [INAUDIBLE]. 00:25:36.900 --> 00:25:38.010 PROFESSOR PATRICK WINSTON: Why? 00:25:38.010 --> 00:25:40.986 SPEAKER 5: [INAUDIBLE]. 00:25:40.986 --> 00:25:41.730 PROFESSOR PATRICK WINSTON: Because it 00:25:41.730 --> 00:25:43.466 looks like a goal tree. 00:25:43.466 --> 00:25:44.960 Right. 00:25:44.960 --> 00:25:48.130 This is, in fact, building a goal tree because each of 00:25:48.130 --> 00:25:55.000 these rules that require several things to be true is 00:25:55.000 --> 00:25:56.920 creating an and node. 00:25:56.920 --> 00:26:00.180 And each of these situations here where you have multiple 00:26:00.180 --> 00:26:02.190 reasons for believing that the thing is a carnivore, that's 00:26:02.190 --> 00:26:03.960 creating an or node. 00:26:03.960 --> 00:26:05.820 And we already know that you can answer questions about 00:26:05.820 --> 00:26:10.440 your own behavior if you leave behind a trace of a goal tree. 00:26:10.440 --> 00:26:11.620 So look at this. 00:26:11.620 --> 00:26:20.260 If I say to it, why were you interested in 00:26:20.260 --> 00:26:21.520 the animal's claws? 00:26:21.520 --> 00:26:25.020 Because I was trying to see if it was a carnivore. 00:26:25.020 --> 00:26:28.710 How did you know that the animal is a mammal? 00:26:28.710 --> 00:26:31.190 Because it has hair. 00:26:31.190 --> 00:26:33.450 Why did you think it was a cheetah? 00:26:33.450 --> 00:26:36.190 Because it's a mammal, a carnivore, has 00:26:36.190 --> 00:26:38.010 spots, and very fast. 00:26:38.010 --> 00:26:41.570 So by working forward and backward in this goal tree, 00:26:41.570 --> 00:26:44.990 this too can answer questions about its own behavior. 00:26:44.990 --> 00:26:47.020 So now you know how, going forward, you can write 00:26:47.020 --> 00:26:50.900 programs that answer questions about their behavior. 00:26:50.900 --> 00:26:52.950 Either you write the subroutines so that each one 00:26:52.950 --> 00:26:55.710 is wrapped around a goal, so you've got goal-centered 00:26:55.710 --> 00:26:59.420 programming, or you build a so-called expert system using 00:26:59.420 --> 00:27:03.010 rules, in which case it's easy to make it leave behind a 00:27:03.010 --> 00:27:06.180 trace of a goal tree, which makes it possible to answer 00:27:06.180 --> 00:27:08.670 questions about its own behavior, just as this 00:27:08.670 --> 00:27:09.170 [INAUDIBLE] 00:27:09.170 --> 00:27:10.420 program did. 00:27:12.290 --> 00:27:16.330 But now, a little more vocabulary. 00:27:16.330 --> 00:27:20.260 I'm going to save time by erasing all of these things 00:27:20.260 --> 00:27:22.210 that I previously drew by way of connections. 00:27:25.818 --> 00:27:33.610 And I'm going to approach this zoo in a little different way. 00:27:33.610 --> 00:27:39.360 I'm going to not ask any questions about the animal. 00:27:39.360 --> 00:27:43.510 Instead, I'm going to say, mommy, is this thing I'm 00:27:43.510 --> 00:27:46.220 looking at a cheetah? 00:27:46.220 --> 00:27:50.640 And how would mommy go about figuring it out. 00:27:50.640 --> 00:27:54.330 In her head, she would say, well, I don't know. 00:27:54.330 --> 00:27:58.425 If it's going to be a cheetah, then it must be the case that 00:27:58.425 --> 00:28:05.010 it's a carnivore, and it must be the case that it has spots. 00:28:05.010 --> 00:28:09.350 And it must be the case that it's very fast. 00:28:09.350 --> 00:28:12.210 So so far, what we've established is that if it's 00:28:12.210 --> 00:28:14.940 going to be a cheetah, it has to have the four 00:28:14.940 --> 00:28:17.880 characteristics that mommy finds behind 00:28:17.880 --> 00:28:20.440 this rule are four. 00:28:20.440 --> 00:28:24.710 So instead of working forward from facts, what I'm going to 00:28:24.710 --> 00:28:31.460 do is work backward from a hypothesis. 00:28:31.460 --> 00:28:35.720 So here the hypothesis is this thing is a cheetah. 00:28:35.720 --> 00:28:40.110 How do I go about showing whether that's true or not? 00:28:40.110 --> 00:28:44.250 Well, I haven't done anything so far because all I know is a 00:28:44.250 --> 00:28:46.740 cheetah if all these things are true, but are they true? 00:28:46.740 --> 00:28:51.160 Well, to find out if it's a mammal, I can use rule one. 00:28:51.160 --> 00:28:55.270 And if I know or can determine that the animal has hair, then 00:28:55.270 --> 00:28:57.790 that part of it is taken care of. 00:28:57.790 --> 00:29:01.990 And I can similarly work my way back through carnivore. 00:29:01.990 --> 00:29:06.870 I say, well, it's a carnivore if it has claws, sharp teeth, 00:29:06.870 --> 00:29:09.680 and forward-pointing eyes. 00:29:09.680 --> 00:29:11.860 And then as much as the animal in question 00:29:11.860 --> 00:29:13.720 does, then I'm through. 00:29:13.720 --> 00:29:14.555 I know it's a carnivore. 00:29:14.555 --> 00:29:16.980 I don't have to go through and show that it's a carnivore 00:29:16.980 --> 00:29:18.230 another way. 00:29:18.230 --> 00:29:20.400 So I never actually ask questions about 00:29:20.400 --> 00:29:23.600 whether it eats meat. 00:29:23.600 --> 00:29:27.720 Finally, the final two conditions are met by just an 00:29:27.720 --> 00:29:30.020 inspection of the animal. 00:29:30.020 --> 00:29:31.300 That's to say, it's in the database. 00:29:31.300 --> 00:29:34.400 I don't have to use any rules to determine that the animal 00:29:34.400 --> 00:29:37.290 has spots and is very fast. 00:29:37.290 --> 00:29:39.610 So now, I've got everything in place to say that it's a 00:29:39.610 --> 00:29:42.490 cheetah, because it's a carnivore, because it has 00:29:42.490 --> 00:29:45.330 claws, sharp teeth, and forward-pointing eyes, and all 00:29:45.330 --> 00:29:47.980 the rest of this stuff is similarly determined by going 00:29:47.980 --> 00:29:54.170 backwards, backwards from the hypothesis toward the facts, 00:29:54.170 --> 00:29:57.730 instead of from the facts forward to the conclusions. 00:29:57.730 --> 00:30:09.180 So building a system that works like that, I have a 00:30:09.180 --> 00:30:15.980 backward-chaining rule-based expert system. 00:30:15.980 --> 00:30:19.040 But there's a very important characteristic of this system 00:30:19.040 --> 00:30:22.620 in both backward and forward mode, and that is that this 00:30:22.620 --> 00:30:24.400 thing is a deduction system. 00:30:29.090 --> 00:30:31.760 That's because it's working with facts 00:30:31.760 --> 00:30:33.750 to produce new facts. 00:30:33.750 --> 00:30:35.730 When you have a deduction system, you can never take 00:30:35.730 --> 00:30:37.920 anything away. 00:30:37.920 --> 00:30:40.790 But these rule-based systems are also used in another mode, 00:30:40.790 --> 00:30:42.530 where it's possible to take something away. 00:30:42.530 --> 00:30:45.110 See, in fact world, in deduction world, you're 00:30:45.110 --> 00:30:46.920 talking about proving things. 00:30:46.920 --> 00:30:51.320 And once you prove something is true, it can't be false. 00:30:51.320 --> 00:30:54.100 If it is, you've got a contradiction in your system. 00:30:54.100 --> 00:30:56.340 But if you think of this as a programming language, if you 00:30:56.340 --> 00:30:59.510 think of using rules as a programming language, then you 00:30:59.510 --> 00:31:04.770 can think of arranging it so these rules add or subtract 00:31:04.770 --> 00:31:06.380 from the database. 00:31:06.380 --> 00:31:11.500 Let me show you an example of a couple of systems. 00:31:11.500 --> 00:31:15.650 First of all, since I've talked about the MYCIN system, 00:31:15.650 --> 00:31:41.910 let me show you an example of a MYCIN dialogue. 00:31:41.910 --> 00:31:43.750 That's a MYCIN dialogue. 00:31:43.750 --> 00:31:47.320 And you can see the appearance of words you have to go to 00:31:47.320 --> 00:31:48.570 medical school to learn. 00:31:51.000 --> 00:31:53.830 And here's a typical MYCIN rule, just like the rules for 00:31:53.830 --> 00:31:58.252 doing zoo analysis, only a more complicated domain. 00:31:58.252 --> 00:32:01.130 But here's another example of a system that was written, not 00:32:01.130 --> 00:32:04.300 in the '80s, but just a couple of years ago by a student in 00:32:04.300 --> 00:32:09.340 the architecture department, Ph.D. thesis. 00:32:09.340 --> 00:32:13.150 He was interested in the architecture of a Portuguese 00:32:13.150 --> 00:32:15.250 architect named Siza. 00:32:15.250 --> 00:32:18.180 And Siza's done a lot of mass housing stuff. 00:32:18.180 --> 00:32:20.570 And Siza has the idea that you ought to be able to design 00:32:20.570 --> 00:32:22.500 your own house. 00:32:22.500 --> 00:32:27.240 And so Jose Duarte, a Portuguese student, a Ph.D. 00:32:27.240 --> 00:32:31.260 student in architecture, wrote a rule-based system that was 00:32:31.260 --> 00:32:35.950 capable of designing Siza-like houses in response to the 00:32:35.950 --> 00:32:39.080 requirements and recommendations and desires of 00:32:39.080 --> 00:32:42.740 the people who are going to occupy the houses. 00:32:42.740 --> 00:32:46.150 So the most compelling part of this thing, of this exercise, 00:32:46.150 --> 00:32:51.780 was that Duarte took some of the designs of the program, 00:32:51.780 --> 00:32:55.330 mixed them up with some of the designs of Siza, and put them 00:32:55.330 --> 00:32:58.840 in front of Siza, and said, which ones did you do? 00:32:58.840 --> 00:33:01.550 And Siza couldn't tell. 00:33:01.550 --> 00:33:04.600 So somehow, the rule-based system that was built using 00:33:04.600 --> 00:33:08.530 this kind of technology was sufficient to confuse even the 00:33:08.530 --> 00:33:13.040 expert that they were patterned after. 00:33:13.040 --> 00:33:14.555 But this program is a little complicated. 00:33:14.555 --> 00:33:17.710 It, too, has its own specialized lingo. 00:33:17.710 --> 00:33:21.460 So I'm not going to talk about it in detail, but rather talk 00:33:21.460 --> 00:33:24.320 instead about an analogous problem. 00:33:24.320 --> 00:33:27.910 And that is a problem that everyone has faced at one 00:33:27.910 --> 00:33:31.210 point or another, and that is the problem of putting 00:33:31.210 --> 00:33:34.952 groceries in a bag at a grocery store. 00:33:34.952 --> 00:33:37.110 It's the same thing, right? 00:33:37.110 --> 00:33:39.770 Instead of putting rooms in a house, you're putting 00:33:39.770 --> 00:33:41.660 groceries in a bag. 00:33:41.660 --> 00:33:46.640 And there must be some rules about how to do that. 00:33:46.640 --> 00:33:49.910 In fact, maybe some of you have been professional grocery 00:33:49.910 --> 00:33:52.420 store baggers? 00:33:52.420 --> 00:33:54.870 [INAUDIBLE] a grocery store professional bagger. 00:33:54.870 --> 00:33:57.760 You're a-- 00:33:57.760 --> 00:33:58.480 which one? 00:33:58.480 --> 00:34:00.850 LISA: [INAUDIBLE] 00:34:00.850 --> 00:34:01.475 PROFESSOR PATRICK WINSTON: Yeah, what is your name? 00:34:01.475 --> 00:34:01.770 LISA: Lisa. 00:34:01.770 --> 00:34:03.120 PROFESSOR PATRICK WINSTON: Lisa. 00:34:03.120 --> 00:34:05.640 OK, well we got two professional 00:34:05.640 --> 00:34:07.730 grocery store baggers. 00:34:07.730 --> 00:34:10.429 And I'm going to be now simulating a highly paid 00:34:10.429 --> 00:34:13.800 knowledge engineer desirous of building a program that knows 00:34:13.800 --> 00:34:16.300 how to bag groceries. 00:34:16.300 --> 00:34:22.300 So I'm going to visit your site, Market Basket, and I'm 00:34:22.300 --> 00:34:26.889 going to ask Lisa, now fearful of losing her job, if she 00:34:26.889 --> 00:34:30.250 would tell me about how she bags groceries. 00:34:30.250 --> 00:34:32.780 So could you suggest a rule? 00:34:32.780 --> 00:34:34.226 LISA: Sure. 00:34:34.226 --> 00:34:36.154 Large items in the bottom. 00:34:36.154 --> 00:34:37.600 PROFESSOR PATRICK WINSTON: Large items in the bottom. 00:34:37.600 --> 00:34:39.350 You see, that's why I'm a highly paid knowledge 00:34:39.350 --> 00:34:43.090 engineer, because I translate what she said 00:34:43.090 --> 00:34:44.090 into an if-then rule. 00:34:44.090 --> 00:34:46.300 So if large, then bottom. 00:34:46.300 --> 00:34:47.370 So now I-- 00:34:47.370 --> 00:34:49.030 SPEAKER 3: [LAUGHTER] 00:34:49.030 --> 00:34:50.020 PROFESSOR PATRICK WINSTON: So how about you, [INAUDIBLE]? 00:34:50.020 --> 00:34:51.270 Have you got a suggestion? 00:34:53.870 --> 00:34:56.355 About how to bag groceries? 00:34:56.355 --> 00:34:59.840 SPEAKER 6: The small things on top. 00:34:59.840 --> 00:35:02.180 PROFESSOR PATRICK WINSTON: If small, then on top. 00:35:02.180 --> 00:35:04.690 Lisa, have you got anything else you could tell me? 00:35:04.690 --> 00:35:07.070 LISA: Don't put too many heavy things in the same bag. 00:35:07.070 --> 00:35:08.500 PROFESSOR PATRICK WINSTON: Don't put too many heavy 00:35:08.500 --> 00:35:09.750 things in the same bag. 00:35:09.750 --> 00:35:14.330 So if heavy greater than three, then new bag, or 00:35:14.330 --> 00:35:16.560 something like that. 00:35:16.560 --> 00:35:17.615 Is that all we're going to be able to-- does anybody else 00:35:17.615 --> 00:35:18.430 want to volunteer? 00:35:18.430 --> 00:35:20.677 [INAUDIBLE], have you bagged groceries in Turkey? 00:35:20.677 --> 00:35:22.665 LISA: So they don't have grocery 00:35:22.665 --> 00:35:26.144 baggers, so we have to-- 00:35:26.144 --> 00:35:27.635 PROFESSOR PATRICK WINSTON: So everybody's a professional 00:35:27.635 --> 00:35:28.640 bagger in Turkey. 00:35:28.640 --> 00:35:28.915 Yeah. 00:35:28.915 --> 00:35:30.165 It's outsourced to the customers. 00:35:32.324 --> 00:35:34.694 SPEAKER 7: So no squishies on the bottom. 00:35:34.694 --> 00:35:35.642 So if you have-- 00:35:35.642 --> 00:35:36.590 PROFESSOR PATRICK WINSTON: No squishies on the bottom. 00:35:36.590 --> 00:35:37.064 SPEAKER 7: If you have tomatoes-- 00:35:37.064 --> 00:35:38.050 PROFESSOR PATRICK WINSTON: That's good. 00:35:38.050 --> 00:35:38.400 Tomatoes. 00:35:38.400 --> 00:35:40.260 SPEAKER 7: You don't want them to get squished. 00:35:40.260 --> 00:35:40.880 PROFESSOR PATRICK WINSTON: Now, there's a very different 00:35:40.880 --> 00:35:44.070 thing about squishies and tomatoes because tomato is 00:35:44.070 --> 00:35:46.395 specific, and squishy isn't. 00:35:46.395 --> 00:35:49.090 Now, one tendency of MIT students, of course, is that 00:35:49.090 --> 00:35:52.270 we all tend to generalize. 00:35:52.270 --> 00:35:54.190 I once knew a professor in the Sloan School 00:35:54.190 --> 00:35:56.270 who seemed real smart. 00:35:56.270 --> 00:35:57.240 And-- 00:35:57.240 --> 00:36:00.150 SPEAKER 3: [LAUGHTER] 00:36:00.150 --> 00:36:00.410 PROFESSOR PATRICK WINSTON: Then I 00:36:00.410 --> 00:36:02.270 figured out what he did. 00:36:02.270 --> 00:36:06.250 If I were to say, I'm thinking about a red apple. 00:36:06.250 --> 00:36:10.720 They'd sit back and say, oh, I see you're contemplating 00:36:10.720 --> 00:36:12.330 colored fruit today. 00:36:12.330 --> 00:36:16.740 They're just taking it up one level of abstraction. 00:36:16.740 --> 00:36:17.530 Not a genius. 00:36:17.530 --> 00:36:20.320 He also was able to talk for an hour after he drew a 00:36:20.320 --> 00:36:21.460 triangle on the board. 00:36:21.460 --> 00:36:22.520 Amazing people. 00:36:22.520 --> 00:36:24.160 Anyhow, where were we? 00:36:24.160 --> 00:36:27.690 Oh, yes, bagging groceries. 00:36:27.690 --> 00:36:31.690 So we're making some progress, but not as 00:36:31.690 --> 00:36:32.950 much as I would like. 00:36:32.950 --> 00:36:37.800 And so in order to really make progress on tasks like this, 00:36:37.800 --> 00:36:38.790 you have to exercise-- 00:36:38.790 --> 00:36:43.040 you know about some principles of knowledge engineering. 00:36:43.040 --> 00:36:48.310 So principle number one, which I've listed over here as part 00:36:48.310 --> 00:36:52.950 of a gold star idea, is deal with specific cases. 00:36:52.950 --> 00:36:55.870 So while you're at the site, if all you do is talk to the 00:36:55.870 --> 00:36:59.210 experts like Lisa and [INAUDIBLE], all you're going 00:36:59.210 --> 00:37:01.490 to get is vague generalities because they won't think of 00:37:01.490 --> 00:37:03.260 everything. 00:37:03.260 --> 00:37:05.620 So what you do is you say, well, let me 00:37:05.620 --> 00:37:07.870 watch you on the line. 00:37:07.870 --> 00:37:09.810 And then you'll see that they have to have some way of 00:37:09.810 --> 00:37:12.540 dealing with the milk. 00:37:12.540 --> 00:37:14.220 And then you'll see that they have to have some way of 00:37:14.220 --> 00:37:15.600 dealing with the potato chips. 00:37:15.600 --> 00:37:19.150 Nobody mentioned potato chips, except insofar as they might 00:37:19.150 --> 00:37:20.400 be squishy. 00:37:22.650 --> 00:37:24.520 We don't have a definition for squishy. 00:37:24.520 --> 00:37:27.840 Nobody talked about the macaroni. 00:37:27.840 --> 00:37:29.790 And no one talked about the motor oil. 00:37:29.790 --> 00:37:32.050 This is a convenience store. 00:37:32.050 --> 00:37:35.240 I don't want that in the same bag with the meat. 00:37:35.240 --> 00:37:37.890 And then no one talked about canned stuff. 00:37:37.890 --> 00:37:40.450 Here's a can of olives. 00:37:40.450 --> 00:37:45.930 So by looking at specific cases, you elicit from people 00:37:45.930 --> 00:37:48.620 knowledge they otherwise would not have thought to give you. 00:37:51.210 --> 00:37:55.450 That's knowledge engineering rule number one. 00:37:55.450 --> 00:37:57.310 And within a very few minutes, you'll have all three 00:37:57.310 --> 00:37:59.480 knowledge engineering rules and be prepared to be a highly 00:37:59.480 --> 00:38:00.730 paid knowledge engineer. 00:38:03.190 --> 00:38:05.350 Heuristic, let's call these heuristics. 00:38:05.350 --> 00:38:07.120 Heuristic number one, specific cases. 00:38:07.120 --> 00:38:11.100 Heuristic number two is ask questions about things that 00:38:11.100 --> 00:38:15.070 appear to be the same, but are actually handled differently. 00:38:15.070 --> 00:38:20.888 So there's some Birds Eye frozen peas, and here-- 00:38:20.888 --> 00:38:27.160 ugh, some fresh cut sweet peas. 00:38:27.160 --> 00:38:31.540 And to me, the person who's never touched a grocery bag in 00:38:31.540 --> 00:38:33.900 my life-- maybe I'm from Mars-- 00:38:33.900 --> 00:38:34.990 I can't tell the difference. 00:38:34.990 --> 00:38:36.910 They're both peas. 00:38:36.910 --> 00:38:40.980 But I observe that the experts are handling these objects 00:38:40.980 --> 00:38:42.650 differently. 00:38:42.650 --> 00:38:45.940 So I say, why did you handle those peas differently from 00:38:45.940 --> 00:38:48.250 those peas, and what do they say? 00:38:48.250 --> 00:38:51.410 One's canned, and one's frozen. 00:38:51.410 --> 00:38:53.150 So what happens? 00:38:53.150 --> 00:38:57.280 Bingo, I've got some new words in my vocabulary. 00:38:57.280 --> 00:38:59.660 And those new vocabulary words are going to give me power 00:38:59.660 --> 00:39:01.570 over the domain because I can now use 00:39:01.570 --> 00:39:03.380 those words in my rules. 00:39:03.380 --> 00:39:07.540 And I can write rules like if frozen, then put them all 00:39:07.540 --> 00:39:09.910 together in a little plastic bag. 00:39:09.910 --> 00:39:11.660 Actually, that's too complicated, but that's what 00:39:11.660 --> 00:39:13.000 we end up doing, right? 00:39:13.000 --> 00:39:14.720 Why do we put them all together in a 00:39:14.720 --> 00:39:16.141 little plastic bag? 00:39:16.141 --> 00:39:18.105 SPEAKER 8: [INAUDIBLE] 00:39:18.105 --> 00:39:19.087 PROFESSOR PATRICK WINSTON: What's that? 00:39:19.087 --> 00:39:20.070 SPEAKER 8: [INAUDIBLE] 00:39:20.070 --> 00:39:20.690 PROFESSOR PATRICK WINSTON: Well, there are two 00:39:20.690 --> 00:39:21.940 explanations. 00:39:24.390 --> 00:39:26.750 There's the MIT explanation. 00:39:26.750 --> 00:39:31.120 We know that temperature flow is equal to the fourth power 00:39:31.120 --> 00:39:33.400 of the temperature difference and the surface area and all 00:39:33.400 --> 00:39:34.130 that kind of stuff. 00:39:34.130 --> 00:39:39.500 We want to get them all together in a ball, sphere. 00:39:39.500 --> 00:39:41.660 The normal explanation is that they're going to melt anyway, 00:39:41.660 --> 00:39:43.808 so they might as well not get everything else wet. 00:39:43.808 --> 00:39:44.784 All right. 00:39:44.784 --> 00:39:50.640 SPEAKER 3: [LAUGHTER] 00:39:50.640 --> 00:39:52.760 PROFESSOR PATRICK WINSTON: So that's heuristic number two. 00:39:52.760 --> 00:39:56.140 And actually, there's heuristic number three, that I 00:39:56.140 --> 00:39:59.860 just want to relate to you for the first time because I have 00:39:59.860 --> 00:40:01.770 been dealing with it a lot over this past summer. 00:40:01.770 --> 00:40:04.160 Heuristic number three is you build a system, and you see 00:40:04.160 --> 00:40:05.820 when it cracks. 00:40:05.820 --> 00:40:07.770 And when it cracks is when you don't have one of the rules 00:40:07.770 --> 00:40:11.130 you need in order to execute-- 00:40:11.130 --> 00:40:13.180 in order to get the program to execute as 00:40:13.180 --> 00:40:14.890 you want it to execute. 00:40:14.890 --> 00:40:17.730 So if I were to write a grocery store bagging program 00:40:17.730 --> 00:40:20.290 and have it bag some groceries, again, eventually 00:40:20.290 --> 00:40:22.680 it would either make a mistake or come to a grinding halt, 00:40:22.680 --> 00:40:24.350 and bingo, I know that there's a missing rule. 00:40:28.150 --> 00:40:32.090 Isn't that what happens when you do a problem set, and you 00:40:32.090 --> 00:40:34.360 hit an impasse? 00:40:34.360 --> 00:40:36.920 You're performing an experiment on yourself, and 00:40:36.920 --> 00:40:40.560 you're discovering that you don't have the whole program. 00:40:40.560 --> 00:40:45.140 In fact, I've listed this as a gold star idea having to do 00:40:45.140 --> 00:40:47.460 with engineering yourself because all of these things 00:40:47.460 --> 00:40:50.400 that you can do for knowledge engineering are things you can 00:40:50.400 --> 00:40:52.170 do when you learn a new subject yourself. 00:40:52.170 --> 00:40:54.730 Because essentially, you're making yourself into an expert 00:40:54.730 --> 00:40:56.940 system when you're learning circuit theory or 00:40:56.940 --> 00:41:00.230 electromagnetism or something of that sort. 00:41:00.230 --> 00:41:02.420 You're saying to yourself, well, let's look at some 00:41:02.420 --> 00:41:04.310 specific cases. 00:41:04.310 --> 00:41:06.870 Well, what are the vocabulary items here that tell me why 00:41:06.870 --> 00:41:08.450 this problem is different from that problem? 00:41:08.450 --> 00:41:11.380 Oh, this is a cylinder instead of a sphere. 00:41:11.380 --> 00:41:13.200 Or you're working with a problem set, and you discover 00:41:13.200 --> 00:41:16.000 you can't work with the problem, and you need to get 00:41:16.000 --> 00:41:17.860 another chunk of knowledge that makes it possible 00:41:17.860 --> 00:41:19.600 for you to do it. 00:41:19.600 --> 00:41:23.260 So this sort of thing, which you think of primarily as a 00:41:23.260 --> 00:41:26.400 mechanism, heuristics for doing knowledge engineering, 00:41:26.400 --> 00:41:32.515 are also mechanisms for making yourself smarter. 00:41:36.910 --> 00:41:40.990 So that concludes what I want to talk with you about today. 00:41:40.990 --> 00:41:43.320 But the bottom line is, that if you build a rule-based 00:41:43.320 --> 00:41:44.880 expert system, it can answer questions 00:41:44.880 --> 00:41:46.540 about its own behavior. 00:41:46.540 --> 00:41:49.850 If you build a program that's centered on goals, it can 00:41:49.850 --> 00:41:51.815 answer questions about its own behavior. 00:41:51.815 --> 00:41:54.190 If you build an integration program, it can answer 00:41:54.190 --> 00:41:56.120 questions about its own behavior. 00:41:56.120 --> 00:41:57.950 And if you want to build one of these systems, and you need 00:41:57.950 --> 00:42:00.760 to extract knowledge from an expert, you need to approach 00:42:00.760 --> 00:42:03.610 it with these kinds of heuristics because the expert 00:42:03.610 --> 00:42:06.320 won't think what to tell you unless you elicit that 00:42:06.320 --> 00:42:09.680 information by specific cases, by asking questions about 00:42:09.680 --> 00:42:12.760 differences, and by ultimately doing some experiments to see 00:42:12.760 --> 00:42:14.850 where your program is correct. 00:42:14.850 --> 00:42:19.960 So that really concludes what I had to say, except I want to 00:42:19.960 --> 00:42:25.080 ask the question, is this all we need to know about human 00:42:25.080 --> 00:42:26.010 intelligence? 00:42:26.010 --> 00:42:27.260 Can these things be-- 00:42:27.260 --> 00:42:30.500 are these things really smart? 00:42:30.500 --> 00:42:34.890 And the traditional answer is no, they're not really smart 00:42:34.890 --> 00:42:39.610 because their intelligence is this sort of thin veneer. 00:42:39.610 --> 00:42:44.090 And when you try to get underneath it, as written, 00:42:44.090 --> 00:42:46.160 they tend to crack. 00:42:46.160 --> 00:42:50.030 For example, we talk about a rule, we could talk about a 00:42:50.030 --> 00:42:53.290 rule that knows that you should put the potato chips on 00:42:53.290 --> 00:42:54.540 the top of the bag. 00:42:57.080 --> 00:43:00.375 But a program that knows that would have no idea why you 00:43:00.375 --> 00:43:03.660 would want to put the potato chips on top of the bag. 00:43:03.660 --> 00:43:05.610 They wouldn't know that if you put them on the bottom of the 00:43:05.610 --> 00:43:09.870 bag, they'll get crushed. 00:43:09.870 --> 00:43:13.310 And it wouldn't know that if they get crushed, the customer 00:43:13.310 --> 00:43:17.630 will get angry, because people don't like to eat crushed 00:43:17.630 --> 00:43:19.340 potato chips. 00:43:19.340 --> 00:43:21.280 So that's what I mean when I say the knowledge of these 00:43:21.280 --> 00:43:23.790 things tends to be a veneer. 00:43:23.790 --> 00:43:28.000 So the MYCIN program, during debugging, once prescribed a 00:43:28.000 --> 00:43:31.595 barrel of penicillin to be administered to a patient for 00:43:31.595 --> 00:43:34.340 its disease. 00:43:34.340 --> 00:43:38.510 They don't know, they don't have any common sense. 00:43:38.510 --> 00:43:43.620 So the question then becomes, well, I don't know. 00:43:43.620 --> 00:43:45.460 Does rule-based-- 00:43:45.460 --> 00:43:50.550 do rules have anything to do with common sense? 00:43:50.550 --> 00:43:53.340 And I'm becoming a little bit agnostic on that subject. 00:43:57.840 --> 00:44:00.440 Because there are certain indications, there are certain 00:44:00.440 --> 00:44:06.600 situations, in which rules could be said to play a role 00:44:06.600 --> 00:44:08.495 in our ordinary understanding of things. 00:44:11.250 --> 00:44:13.560 Would you like to see a demonstration? 00:44:13.560 --> 00:44:17.060 What I'm going to show you, when the clip speaks up-- 00:44:17.060 --> 00:44:21.640 well, before I make any promises, let me see if I'm 00:44:21.640 --> 00:44:22.910 actually connected to the web. 00:44:34.260 --> 00:44:36.770 MIT, good. 00:44:36.770 --> 00:44:38.820 MIT. 00:44:38.820 --> 00:44:39.700 Guest. 00:44:39.700 --> 00:44:40.950 Yeah, that's me. 00:44:53.820 --> 00:44:55.070 Sounds good. 00:45:23.930 --> 00:45:26.650 OK, I just tested the system, and I've seen it is actually 00:45:26.650 --> 00:45:28.330 connected to the web. 00:45:28.330 --> 00:45:32.230 And I'm going to adjust some systems options here. 00:45:32.230 --> 00:45:35.120 I'll get rid of the text box. 00:45:35.120 --> 00:45:38.160 And we'll get rid of those changes scale a little bit. 00:45:38.160 --> 00:45:40.780 What I'm going to do is I'm going to read a little 00:45:40.780 --> 00:45:44.050 synopsis of the Macbeth plot. 00:45:44.050 --> 00:45:45.190 You're MIT students. 00:45:45.190 --> 00:45:46.920 I'm sure you're all classically educated and very 00:45:46.920 --> 00:45:49.830 familiar with Shakespearean plots. 00:45:49.830 --> 00:45:52.980 So I'm going to read one. 00:45:52.980 --> 00:45:56.330 I'm going to read a version of a Macbeth plot. 00:45:56.330 --> 00:45:59.260 And it's going to go along like this. 00:45:59.260 --> 00:46:03.040 It's basically reading a rule base so far. 00:46:03.040 --> 00:46:07.910 And pretty soon, it's going to get beyond the rule base and 00:46:07.910 --> 00:46:09.435 start reading the Macbeth story. 00:46:12.320 --> 00:46:13.740 And there it is. 00:46:13.740 --> 00:46:14.720 It's read the Macbeth story. 00:46:14.720 --> 00:46:16.860 Let me show you what the Macbeth story looks like as 00:46:16.860 --> 00:46:18.660 it's actually retained by the system. 00:46:18.660 --> 00:46:19.320 That's it. 00:46:19.320 --> 00:46:20.570 Read that. 00:46:25.480 --> 00:46:27.310 OK, you ran out of time because the 00:46:27.310 --> 00:46:28.490 machine's already finished. 00:46:28.490 --> 00:46:30.830 It takes about five seconds to read this story. 00:46:30.830 --> 00:46:32.920 Now, as you look at this little synopsis of Macbeth, 00:46:32.920 --> 00:46:35.510 there are a couple things to note. 00:46:35.510 --> 00:46:39.090 For one thing, it says that Duncan is murdered. 00:46:39.090 --> 00:46:42.650 Duncan, I hope this doesn't bother you. 00:46:42.650 --> 00:46:44.530 Duncan is murdered by Macbeth. 00:46:44.530 --> 00:46:48.205 But at no time does it say that Duncan is dead. 00:46:48.205 --> 00:46:50.800 But you know Duncan's dead because he was murdered. 00:46:50.800 --> 00:46:53.168 If murdered, then dead. 00:46:53.168 --> 00:46:55.000 SPEAKER 3: [LAUGHTER]. 00:46:55.000 --> 00:46:56.640 PROFESSOR PATRICK WINSTON: So if you look a little further 00:46:56.640 --> 00:47:01.890 down, what you see is that Macduff kills Macbeth. 00:47:01.890 --> 00:47:04.260 Fourth line up from the bottom. 00:47:04.260 --> 00:47:08.410 Why did Macduff kill Macbeth? 00:47:08.410 --> 00:47:11.630 Doesn't say why in this story, but you have no trouble 00:47:11.630 --> 00:47:16.950 figuring out that it's because he got angry. 00:47:16.950 --> 00:47:19.970 And when you get angry, you don't necessarily kill 00:47:19.970 --> 00:47:21.300 somebody, but it's possible. 00:47:21.300 --> 00:47:24.190 SPEAKER 3: [LAUGHTER]. 00:47:24.190 --> 00:47:25.240 PROFESSOR PATRICK WINSTON: So now that you see what's in the 00:47:25.240 --> 00:47:28.550 story, let me take you back to this display. 00:47:28.550 --> 00:47:31.210 It's what we call an elaboration graph. 00:47:31.210 --> 00:47:33.360 And when I blow it up, you can see that there's some familiar 00:47:33.360 --> 00:47:35.340 looking things in there. 00:47:35.340 --> 00:47:38.630 For example, up here in the left-hand corner, Macbeth 00:47:38.630 --> 00:47:41.700 murders Duncan, right over there. 00:47:41.700 --> 00:47:45.510 And over here, Macduff kills Macbeth. 00:47:45.510 --> 00:47:50.710 And if you look at what is a consequence of that, it looks 00:47:50.710 --> 00:47:53.010 like there must be a rule that says if you murder somebody, 00:47:53.010 --> 00:47:54.670 you harm them. 00:47:54.670 --> 00:47:58.540 And if you murder somebody, then they're dead. 00:47:58.540 --> 00:48:01.270 And one reason why you might kill somebody is because they 00:48:01.270 --> 00:48:02.500 angered you. 00:48:02.500 --> 00:48:05.880 And if you go the other way, one consequence of killing 00:48:05.880 --> 00:48:10.160 somebody is that you harm them, and that they die too. 00:48:10.160 --> 00:48:13.120 And if you harm somebody, they get angry, and their state 00:48:13.120 --> 00:48:14.370 goes negative. 00:48:19.050 --> 00:48:22.550 So that suggests that there are some things that we have 00:48:22.550 --> 00:48:26.145 on our hands that are very compiled and very, strangely 00:48:26.145 --> 00:48:30.970 enough, very rule-like in their character. 00:48:30.970 --> 00:48:35.730 Now, to close, I'm just going to read Hamlet. 00:48:35.730 --> 00:48:38.900 The Hamlet demonstration is much like the Macbeth one. 00:48:38.900 --> 00:48:42.270 In fact, Hamlet and Macbeth are very alike in their plot. 00:48:42.270 --> 00:48:44.530 But there's one thing that's well-illustrated by our 00:48:44.530 --> 00:48:47.940 particular capturing of Hamlet here. 00:48:47.940 --> 00:48:50.910 And that is that you'll note that the ratio of gray stuff 00:48:50.910 --> 00:48:54.060 to white stuff is considerable. 00:48:54.060 --> 00:48:59.810 The gray stuff is stuff that has been deduced by rules. 00:48:59.810 --> 00:49:02.140 And the reason there's so much gray stuff in this Hamlet 00:49:02.140 --> 00:49:05.380 story is because everybody's related to everybody else. 00:49:05.380 --> 00:49:08.920 So when you kill anybody, you irritate everybody else. 00:49:12.690 --> 00:49:13.750 So look at that. 00:49:13.750 --> 00:49:16.310 A few white things, those are the things that are explicit 00:49:16.310 --> 00:49:18.630 in the story, and lots of gray stuff. 00:49:18.630 --> 00:49:22.270 So what this is suggesting is that when we tell a story, 00:49:22.270 --> 00:49:25.440 it's mostly a matter of controlled hallucination. 00:49:25.440 --> 00:49:28.560 I know what rules are in your head, so I could take 00:49:28.560 --> 00:49:31.030 advantage of that in telling the story and not have to tell 00:49:31.030 --> 00:49:33.490 you anything I'm sure you're going to know. 00:49:33.490 --> 00:49:36.640 And so that's why, we've discovered, that storytelling 00:49:36.640 --> 00:49:39.500 is largely a matter of just controlling how you're going 00:49:39.500 --> 00:49:41.010 along, a kind of controlled hallucination.