____________________________________
p.265
“If a system is hard-to-detect or hard-to-measure at some instant and no substances can be added to an object, then these substances generating easy-to-detect and easy-to-measure field should be added to ambient medium, and the state of the object can be judged from the state of ambient medium” [1].
“If a system is hard-to-detect or hard-to-measure at some instant and no substances can be added to an object, then these [cute little demons] generating easy-to-detect and easy-to-measure field should be added to ambient medium, and the state of the object can be judged from the state of ambient medium”, [because the {cute little demons} are going to generate easy-to-detect and easy-to-measure field, which can be detect and measure from the state of the ambient medium] [1].
• Maxwell's demon, a tiny imp, agents, small smart demon, “small smart people”, “inanimate particles”, “tiny smart particles”, ...
• “small smart people” that could do anything a problem solver needed to do in the problem-to-solution transition.
• During TRIZ classes, Altshuller realized that the weak point of empathy is the strong tendency to reject any action that is unacceptable to the human organism.
• In the Israeli teaching experience, it was found that students did not always use small smart people effectively. It seems that subconsciously some students were reluctant to place these small smart people in situations that would be life threatening to humans, such as in strong acids or extreme fields.
pp.283-284
This chapter discusses a problem-solving based on solving Agents. In contrast to all other TRIZ heuristics, this is the first TRIZ method to have been developed almost independently in numerous countries -- Russia, Israel, and then US. Agents in TRIZ originally were “small smart people” that could do anything a problem solver needed to do in the problem-to-solution transition. They were derived by Altshuller at the end of 1960s from Synectics, the American method of creativity activation. Ten year earlier, William Gordon, the author of Synectics, had suggested using personal analogy or empathy in the solving process [1]. The essence of empathy is that a persons “enters” into the object to be improved and tries to imagine the action required by the problem. During TRIZ classes, Altshuller realized that the weak point of empathy is the strong tendency to reject any action that is unacceptable to the human organism. This drawback is overcome with the help of “small smart people” in modeling [2]. A transition from the “small smart people” to “inanimate particles” was proposed by Solomon D. Tetel'baum about 15 years ago [3], but the idea was not supported by other TRIZniks who often used teams of boys and girls during their lessons. Due to emigration of some TRIZniks from USSR to Israel in the 1980s, this methodology became popular in the Middle East. In the Israeli teaching experience, it was found that students did not always use small smart people effectively. It seems that subconsciously some students were reluctant to place these small smart people in situations that would be life threatening to humans, such as in strong acids or extreme fields. Therefore, Genady Filkovsky, Roni Horowirz, and Jacob Goldenberg from the Open University in Israel replaced small smart people with inanimate particles [4].* This particles method is now used actively in the Israeli derivative of TRIZ simplification named SIT, where it represents almost half of these problem-solving activities [4,5]. However, some of the author's students have argued that they are more easily imagine various actions performed by the “small smart people” than by inanimate particles. This is all a matter of sematics and the term itself is not as important as the method. But we will use the neutral term agents. The experience of Russian, Israeli, and American specialists is summarized and generalized in the Agents Method described in this chapter.
* In general, this idea is not new in problem solving; even the famous antique Greek philosopher Demokrit used small particles for explanation of natural phenomena. The famous physicist James Clerk Maxwell used small demons (human-like beings) for resolution of scientific problems.
( Savransky, Semyon D., Engineering of creativity : introduction to TRIZ methodology of inventive problem solving / by Semyon D. Savransky., 1. engineering--methodology., 2. problem solving--methodology., 3. creative thinking., 4. technological innovations., 2000, pp.283-284 )
____________________________________
Howard Rheingold, Tools for thought, 1985 [ ]
Maxwell's demon
pp.121-122
James Clerk Maxwell, yet another 19th century scientist, proposed a paradox concerning this elusive quality called entropy, which seems to relate such intuitively dissimilar measures as
energy,
information,
order, and
predict ability.
The paradox became in famous among physicists under the name "Maxwell's demon." Consider a container split by a barrier with an opening small enough to pass only one molecule at a time from one side to the other. On one side is a volume of hot gas, in which the average energy of the molecules is higher than the average energy of the molecules in the cold side of the container. According to the second law, the hotter, more active molecules should eventually migrate across to the other side of the container, losing energy in collisions with slower-moving molecules, until both sides reach the same temperature.
What would happen, Maxwell asked, if you could place a tiny imp at the molecular gate, a demon who didn't contribute energy to the system, but who could open and close the gate between the two sides of the container? Now what if the imp decides to only let the occasional slow-moving, colder molecule pass from the hot to the cold side when it randomly approaches the gate? Taken far enough, this policy would mean that the hot side would get hotter and the cold side would get colder, and entropy would decrease instead of increase, without any energy being added to the system!
([
Conversely, what if the imp decides to only let the occasional fast-moving, hotter molecule passes from the cold to the hot side when it randomly approaches the gate? Taken far enough, this policy would mean that the cold side would get colder and the hot side would get hotter, and entropy would decrease instead of increase, without any energy being added to the system!
Moreover, what if both methods is used. By obtaining information of each molecule as it approaches the gate, our all-knowing-imp let the colder molecule passes from hot to the cold side; and let the hotter molecule passes from the cold to the hot side. In this way, the demon is able to add more order (entropy) to the system.
That is one big if, isn't it. Because how does the all-knowing-imp differentiate or get the needed information about the molecule.
])
In 1922, a Hungarian student of physics by the name of Leo Szilard (later to be von Neumann's colleague on the Manhattan Project), then in Berlin (Germany), finally solved the paradox of Maxwell's demon by demonstrating that the demon does indeed contribute energy to the system, but like a good magician, the demon does not expend that energy in its most visible activity -- moving the gate -- but in what it knows about the system. The demon is part of the system, and it has to do some work in order to differentiate the hot and cold molecules at the proper time to open the gate. Simply by obtaining the information about the molecules that it needs to know to operate the gate, the demon adds more entropy (order) to the system than it subtracts.
(Tools for thought : the history and future of mind-expanding technology, Howard Rheingold, 1985, pp.121-122)
____________________________________
(In Hawaiian legend, the Menehune were strong, skillful imps who would work all night without stopping.)
M. Mitchell Waldrop, The Dream Machine, 2001 [ ]
Bob Metcalfe (Robert Metcalfe)
p.372
This last task, especially, kept him in a more or less constant state of jet lag, which was how he happened to find himself in Steve Crocker's guest room one night during a swing through Washington, D.C., tossing and turning on the sofa bed. Desperate for something to put him under, he happened to catch sight of a thick blue volume on the bookshelf next to the bed: American Federation of Information Processing Societies Conference Proceeding, volume 37, fall 1970. Perfect. Metcalfe started reading “The Aloha System”, a paper by Norman Abramson of the University of Hawaii.
p.372
The Aloha system, he learned, was an experimental, ARPA-funded network that transmitted computer data via radio waves, instead of via the telephone lines used in the Arpanet. The University of Hawaii, he also learned, was natural setting for such an experiment: its campuses on the various islands were separated by large stretches of open ocean, which made for telephone connections that were noise, unreliable, and very expensive. Abramson's paper accordingly described a network in which the main IBM System/360 computer on Oahu sent packets of data back and forth to terminals out on the island campuses via radio. Serving as a front end to the 360 was Menehune, a small, packet-switching computer that handled the actual radio connections and that was similar in function to an Arpanet IMP. (In Hawaiian legend, the Menehune were strong, skillful imps who would work all night without stopping.)
pp.372-373
Now, on the surface, Metcalfe could see, this was a matter of straight substitution: anywhere Arpanet had a wire, Alohanet had a radio link. Beneath the surface, however, when you got down to the nitty-gritty of how the packet transmission were regulated, the differences were much more interesting. On the Arpanet, where the bits flowed through telephone lines, an IMP with packets to send could wait for a break in the traffic, so to speak; that way, the packets never collided with one another. But on the Alohanet, where the bits were carried by staticky, interference-prone radio waves, a terminal with packets to send had no way of knowing what the traffic was like. It could transmit back and forth to Menehune (with luck), but it probably couldn't even HEAR what the other nodes in the network were sending. So, since waiting would be pointless, Alohanet allowed each terminal to fire off a packet to Menehune whenever it needed to, regardless of what the others were doing. If the terminal heard Menehune acknowledge receipt of that packet, then fine. But if it didn't--meaning that another terminal's packet had arrived simultaneously and turned the bits into gibberish--the first terminal would just back off, wait for a random interval of time, and then transmit its packet again. Since the second terminal would also be retransmitting, but with a different random interval, both packets now had a reasonable chance of arriving unscathed.
p.373
Beautiful! thought Metcalfe. It was control witout control: the terminals were completely free agents, unregulated and unsynchronized by Menehune. And yet the packets got through anyhow.
p.373
Or did they? Alas, wrote Abramson, the system's beauty came at the price of instability. Using a branch of mathematics known as queuing theory, he argued that Alohanet couldn't use more than about 17 percent of the total capacity in its radio channel without causing a kind of chain reaction. Push it past that point, and each collision of packets would trigger the transmission of replacement packets, which would increase the probability of more collisions, which would generate more replacement--on and on until every packet was statistically guaranteed to hit another packet. The system would grind to a halt.
p.373
By now, recalls Metcalfe, he was wide awake: this couldn't be right. As he wrote about it later, “The Abramson paper ... made two assumptions about the computer terminal user behavior that, on Steve Crocker's sofabed late at night, I found totally unacceptable. Abramson's model assumed that there were an infinite number of terminal users, and that each of them would go on typing whether or not they received answers to earlier inputs.”
p.373
He would give them theory. He would do this queuing analysis RIGHT. “That night”, he remembers, “and in the weeks to follow, I worked hard on the less-tractable mathematics of Aloha channels with a few users, each of whom would insist on receiving a response to [his] input before typing a new one. I worked so hard, in fact, that Xerox sent me to work with Professor Abramson for a month--in Hawaii!”
p.373
By October 1972, says Metcalfe, just in time for the Arpanet “coming-out” party in Washington, he had produced a paper showing that an Aloha-type network could indeed be made stable under much heavier loads than Abramson had believed.
(Waldrop, M. Mitchell.; The dream machine : J. C. R. Licklider and the revolution that made computing personal / M. Mitchell Waldrop., 1. Licklider, J. C. R., 2. microcomputers--history, 2001, )
____________________________________
Benedict Carey, How we learn, 2014 [ ]
p.196
These kind of stories always remind of the Grimms' fairy
tale “The Golden Bird”, in which a young man on a mission to
find a magic bird with golden feathers falls in love with a
princess, whose father the king will grant her hand on one
condition: that the young man dig away the hill that stops the
view from his window in eight (8) days. The only complication?
This is no hill, it's a mountain, and after seven (7) days of
digging, the young man collapses in defeat. That's when his
friend the fox whispers, “Lie down and go to sleep; I will work
for you”. And in the morning, the mountain is gone.
(Carey, Benedict., How we learn: the surprising truth about when, where, and why it happens/ Benedict Carey., 1. learning, psychology of., 2. learning., BF318.C366 2014, 153.1'5--dc23, 2014, )
____________________________________
p.265
“If a system is hard-to-detect or hard-to-measure at some instant and no substances can be added to an object, then these substances generating easy-to-detect and easy-to-measure field should be added to ambient medium, and the state of the object can be judged from the state of ambient medium” [1].
“If a system is hard-to-detect or hard-to-measure at some instant and no substances can be added to an object, then these [cute little demons] generating easy-to-detect and easy-to-measure field should be added to ambient medium, and the state of the object can be judged from the state of ambient medium”, [because the {cute little demons} are going to generate easy-to-detect and easy-to-measure field, which can be detect and measure from the state of the ambient medium] [1].
• Maxwell's demon, a tiny imp, agents, small smart demon, “small smart people”, “inanimate particles”, “tiny smart particles”, ...
• “small smart people” that could do anything a problem solver needed to do in the problem-to-solution transition.
• During TRIZ classes, Altshuller realized that the weak point of empathy is the strong tendency to reject any action that is unacceptable to the human organism.
• In the Israeli teaching experience, it was found that students did not always use small smart people effectively. It seems that subconsciously some students were reluctant to place these small smart people in situations that would be life threatening to humans, such as in strong acids or extreme fields.
pp.283-284
This chapter discusses a problem-solving based on solving Agents. In contrast to all other TRIZ heuristics, this is the first TRIZ method to have been developed almost independently in numerous countries -- Russia, Israel, and then US. Agents in TRIZ originally were “small smart people” that could do anything a problem solver needed to do in the problem-to-solution transition. They were derived by Altshuller at the end of 1960s from Synectics, the American method of creativity activation. Ten year earlier, William Gordon, the author of Synectics, had suggested using personal analogy or empathy in the solving process [1]. The essence of empathy is that a persons “enters” into the object to be improved and tries to imagine the action required by the problem. During TRIZ classes, Altshuller realized that the weak point of empathy is the strong tendency to reject any action that is unacceptable to the human organism. This drawback is overcome with the help of “small smart people” in modeling [2]. A transition from the “small smart people” to “inanimate particles” was proposed by Solomon D. Tetel'baum about 15 years ago [3], but the idea was not supported by other TRIZniks who often used teams of boys and girls during their lessons. Due to emigration of some TRIZniks from USSR to Israel in the 1980s, this methodology became popular in the Middle East. In the Israeli teaching experience, it was found that students did not always use small smart people effectively. It seems that subconsciously some students were reluctant to place these small smart people in situations that would be life threatening to humans, such as in strong acids or extreme fields. Therefore, Genady Filkovsky, Roni Horowirz, and Jacob Goldenberg from the Open University in Israel replaced small smart people with inanimate particles [4].* This particles method is now used actively in the Israeli derivative of TRIZ simplification named SIT, where it represents almost half of these problem-solving activities [4,5]. However, some of the author's students have argued that they are more easily imagine various actions performed by the “small smart people” than by inanimate particles. This is all a matter of sematics and the term itself is not as important as the method. But we will use the neutral term agents. The experience of Russian, Israeli, and American specialists is summarized and generalized in the Agents Method described in this chapter.
* In general, this idea is not new in problem solving; even the famous antique Greek philosopher Demokrit used small particles for explanation of natural phenomena. The famous physicist James Clerk Maxwell used small demons (human-like beings) for resolution of scientific problems.
( Savransky, Semyon D., Engineering of creativity : introduction to TRIZ methodology of inventive problem solving / by Semyon D. Savransky., 1. engineering--methodology., 2. problem solving--methodology., 3. creative thinking., 4. technological innovations., 2000, pp.283-284 )
____________________________________
Howard Rheingold, Tools for thought, 1985 [ ]
Maxwell's demon
pp.121-122
James Clerk Maxwell, yet another 19th century scientist, proposed a paradox concerning this elusive quality called entropy, which seems to relate such intuitively dissimilar measures as
energy,
information,
order, and
predict ability.
The paradox became in famous among physicists under the name "Maxwell's demon." Consider a container split by a barrier with an opening small enough to pass only one molecule at a time from one side to the other. On one side is a volume of hot gas, in which the average energy of the molecules is higher than the average energy of the molecules in the cold side of the container. According to the second law, the hotter, more active molecules should eventually migrate across to the other side of the container, losing energy in collisions with slower-moving molecules, until both sides reach the same temperature.
What would happen, Maxwell asked, if you could place a tiny imp at the molecular gate, a demon who didn't contribute energy to the system, but who could open and close the gate between the two sides of the container? Now what if the imp decides to only let the occasional slow-moving, colder molecule pass from the hot to the cold side when it randomly approaches the gate? Taken far enough, this policy would mean that the hot side would get hotter and the cold side would get colder, and entropy would decrease instead of increase, without any energy being added to the system!
([
Conversely, what if the imp decides to only let the occasional fast-moving, hotter molecule passes from the cold to the hot side when it randomly approaches the gate? Taken far enough, this policy would mean that the cold side would get colder and the hot side would get hotter, and entropy would decrease instead of increase, without any energy being added to the system!
Moreover, what if both methods is used. By obtaining information of each molecule as it approaches the gate, our all-knowing-imp let the colder molecule passes from hot to the cold side; and let the hotter molecule passes from the cold to the hot side. In this way, the demon is able to add more order (entropy) to the system.
That is one big if, isn't it. Because how does the all-knowing-imp differentiate or get the needed information about the molecule.
])
In 1922, a Hungarian student of physics by the name of Leo Szilard (later to be von Neumann's colleague on the Manhattan Project), then in Berlin (Germany), finally solved the paradox of Maxwell's demon by demonstrating that the demon does indeed contribute energy to the system, but like a good magician, the demon does not expend that energy in its most visible activity -- moving the gate -- but in what it knows about the system. The demon is part of the system, and it has to do some work in order to differentiate the hot and cold molecules at the proper time to open the gate. Simply by obtaining the information about the molecules that it needs to know to operate the gate, the demon adds more entropy (order) to the system than it subtracts.
(Tools for thought : the history and future of mind-expanding technology, Howard Rheingold, 1985, pp.121-122)
____________________________________
(In Hawaiian legend, the Menehune were strong, skillful imps who would work all night without stopping.)
M. Mitchell Waldrop, The Dream Machine, 2001 [ ]
Bob Metcalfe (Robert Metcalfe)
p.372
This last task, especially, kept him in a more or less constant state of jet lag, which was how he happened to find himself in Steve Crocker's guest room one night during a swing through Washington, D.C., tossing and turning on the sofa bed. Desperate for something to put him under, he happened to catch sight of a thick blue volume on the bookshelf next to the bed: American Federation of Information Processing Societies Conference Proceeding, volume 37, fall 1970. Perfect. Metcalfe started reading “The Aloha System”, a paper by Norman Abramson of the University of Hawaii.
p.372
The Aloha system, he learned, was an experimental, ARPA-funded network that transmitted computer data via radio waves, instead of via the telephone lines used in the Arpanet. The University of Hawaii, he also learned, was natural setting for such an experiment: its campuses on the various islands were separated by large stretches of open ocean, which made for telephone connections that were noise, unreliable, and very expensive. Abramson's paper accordingly described a network in which the main IBM System/360 computer on Oahu sent packets of data back and forth to terminals out on the island campuses via radio. Serving as a front end to the 360 was Menehune, a small, packet-switching computer that handled the actual radio connections and that was similar in function to an Arpanet IMP. (In Hawaiian legend, the Menehune were strong, skillful imps who would work all night without stopping.)
pp.372-373
Now, on the surface, Metcalfe could see, this was a matter of straight substitution: anywhere Arpanet had a wire, Alohanet had a radio link. Beneath the surface, however, when you got down to the nitty-gritty of how the packet transmission were regulated, the differences were much more interesting. On the Arpanet, where the bits flowed through telephone lines, an IMP with packets to send could wait for a break in the traffic, so to speak; that way, the packets never collided with one another. But on the Alohanet, where the bits were carried by staticky, interference-prone radio waves, a terminal with packets to send had no way of knowing what the traffic was like. It could transmit back and forth to Menehune (with luck), but it probably couldn't even HEAR what the other nodes in the network were sending. So, since waiting would be pointless, Alohanet allowed each terminal to fire off a packet to Menehune whenever it needed to, regardless of what the others were doing. If the terminal heard Menehune acknowledge receipt of that packet, then fine. But if it didn't--meaning that another terminal's packet had arrived simultaneously and turned the bits into gibberish--the first terminal would just back off, wait for a random interval of time, and then transmit its packet again. Since the second terminal would also be retransmitting, but with a different random interval, both packets now had a reasonable chance of arriving unscathed.
p.373
Beautiful! thought Metcalfe. It was control witout control: the terminals were completely free agents, unregulated and unsynchronized by Menehune. And yet the packets got through anyhow.
p.373
Or did they? Alas, wrote Abramson, the system's beauty came at the price of instability. Using a branch of mathematics known as queuing theory, he argued that Alohanet couldn't use more than about 17 percent of the total capacity in its radio channel without causing a kind of chain reaction. Push it past that point, and each collision of packets would trigger the transmission of replacement packets, which would increase the probability of more collisions, which would generate more replacement--on and on until every packet was statistically guaranteed to hit another packet. The system would grind to a halt.
p.373
By now, recalls Metcalfe, he was wide awake: this couldn't be right. As he wrote about it later, “The Abramson paper ... made two assumptions about the computer terminal user behavior that, on Steve Crocker's sofabed late at night, I found totally unacceptable. Abramson's model assumed that there were an infinite number of terminal users, and that each of them would go on typing whether or not they received answers to earlier inputs.”
p.373
He would give them theory. He would do this queuing analysis RIGHT. “That night”, he remembers, “and in the weeks to follow, I worked hard on the less-tractable mathematics of Aloha channels with a few users, each of whom would insist on receiving a response to [his] input before typing a new one. I worked so hard, in fact, that Xerox sent me to work with Professor Abramson for a month--in Hawaii!”
p.373
By October 1972, says Metcalfe, just in time for the Arpanet “coming-out” party in Washington, he had produced a paper showing that an Aloha-type network could indeed be made stable under much heavier loads than Abramson had believed.
(Waldrop, M. Mitchell.; The dream machine : J. C. R. Licklider and the revolution that made computing personal / M. Mitchell Waldrop., 1. Licklider, J. C. R., 2. microcomputers--history, 2001, )
____________________________________
Benedict Carey, How we learn, 2014 [ ]
p.196
These kind of stories always remind of the Grimms' fairy
tale “The Golden Bird”, in which a young man on a mission to
find a magic bird with golden feathers falls in love with a
princess, whose father the king will grant her hand on one
condition: that the young man dig away the hill that stops the
view from his window in eight (8) days. The only complication?
This is no hill, it's a mountain, and after seven (7) days of
digging, the young man collapses in defeat. That's when his
friend the fox whispers, “Lie down and go to sleep; I will work
for you”. And in the morning, the mountain is gone.
(Carey, Benedict., How we learn: the surprising truth about when, where, and why it happens/ Benedict Carey., 1. learning, psychology of., 2. learning., BF318.C366 2014, 153.1'5--dc23, 2014, )
____________________________________
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