# PCT robotics paper

**URL:** <http://discourse.iapct.org/t/pct-robotics-paper/8482>\
**Category:** CSG2016\
**Created:** [September 21, 2016, 9:41pm UTC](http://discourse.iapct.org/t/pct-robotics-paper/8482 "2016-09-21T21:41:39Z")\
**Posts on this page:** 15\
**Page:** 1

<div class="post-metadata">

**Author:** ![rupert](http://discourse.iapct.org/letter_avatar_proxy/v4/letter/r/f14d63/32.png) [@rupert](http://discourse.iapct.org/u/rupert)\
**Post date:** [September 21, 2016, 9:41pm UTC](http://discourse.iapct.org/t/pct-robotics-paper/8482/1 "2016-09-21T21:41:39Z")

</div>

[From Rupert Young (2016.09.21 22.40)]

“ **A General Architecture for Robotics Systems:** \*\* A  
Perception-based Approach to Artificial Life\*\*”

```
I am pleased to say that my paper has been accepted for publication

```

in the **Artificial Life** journal. It is basically applying  
the PCT architecture to robotics, but also positioning perceptual  
control as the missing ‘stuff’ of AI/AL (see attached).

```
It's a fairly long paper at 48 (book) pages (72 with refs and

```

appendices) with a fair bit of background of putting PCT into the  
context of AI/AL, and a basic robotic experimental system.

```
**Artificial Life** is a major journal in the field so it will

```

be interesting to see the exposure and feedback it receives.  
However, there’ll be a bit of a wait. I was going to annouce this  
soon, when they sent out the contents for the Winter edition, but,  
for some reason, it has now been bumped to the Summer edition next  
year. So, I thought I’d let you know now, and I’ll send an update  
nearer the time, along with pre-publication copies.

```
It's been a long road; by the time the paper is published it would

```

have been over three years since first submitted, but at least it  
has now been accepted.

```
Regards,

Rupert

```

[nature.pdf](http://discourse.iapct.org/uploads/short-url/6omBqE08PPjmKGKXQVrJXRiE4BO.pdf) (112 KB)

---

<div class="post-metadata">

**Author:** ![FredNickols](http://discourse.iapct.org/user_avatar/discourse.iapct.org/frednickols/32/2645_2.png) [@FredNickols](http://discourse.iapct.org/u/FredNickols)\
**Post date:** [September 21, 2016, 9:53pm UTC](http://discourse.iapct.org/t/pct-robotics-paper/8482/2 "2016-09-21T21:53:20Z")

</div>

[From Fred Nickols (2016.09.21.1752 ET)]

> **···**
>
> Yea! Congratulations, Rupert. Hard won and well deserved!
> 
> Fred Nickols, CPT
> 
> Writer & Consultant
> 
> **[DISTANCE CONSULTING LLC](https://urldefense.proofpoint.com/v2/url?u=http-3A__www.nickols.us_&d=CwMFaQ&c=8hUWFZcy2Z-Za5rBPlktOQ&r=-dJBNItYEMOLt6aj_KjGi2LMO_Q8QB-ZzxIZIF8DGyQ&m=wkjHuM9OpZDp4DcRKGiT71QrSUR1swWETodJsMq6Q8E&s=KemWkshkzRQwqHCkb3k73v_OzwtNmd6xaNZrcRbXyJI&e=)**
> 
> _“Assistance at a Distance”_
> 
> [View My Books on Amazon](https://urldefense.proofpoint.com/v2/url?u=https-3A__www.amazon.com_author_frednickols&d=CwMFaQ&c=8hUWFZcy2Z-Za5rBPlktOQ&r=-dJBNItYEMOLt6aj_KjGi2LMO_Q8QB-ZzxIZIF8DGyQ&m=wkjHuM9OpZDp4DcRKGiT71QrSUR1swWETodJsMq6Q8E&s=qT1NPzOAYNmtUpE8iLHPhr1Z68aoNXktttXwfsSaCxA&e=)
> 
> Sent from my iPad
> 
> On Sep 21, 2016, at 5:41 PM, Rupert Young [rupert@perceptualrobots.com](mailto:rupert@perceptualrobots.com) wrote:
> 
> > [From Rupert Young (2016.09.21 22.40)]
> 
> > “ **A General Architecture for Robotics Systems:** \*\* A  
> > Perception-based Approach to Artificial Life\*\*”
> 
> > ```
> > I am pleased to say that my paper has been accepted for publication
> > 
> > ```
> > 
> > in the **Artificial Life** journal. It is basically applying  
> > the PCT architecture to robotics, but also positioning perceptual  
> > control as the missing ‘stuff’ of AI/AL (see attached).
> > 
> > ```
> > It's a fairly long paper at 48 (book) pages (72 with refs and
> > 
> > ```
> > 
> > appendices) with a fair bit of background of putting PCT into the  
> > context of AI/AL, and a basic robotic experimental system.
> > 
> > ```
> > **Artificial Life** is a major journal in the field so it will
> > 
> > ```
> > 
> > be interesting to see the exposure and feedback it receives.  
> > However, there’ll be a bit of a wait. I was going to annouce this  
> > soon, when they sent out the contents for the Winter edition, but,  
> > for some reason, it has now been bumped to the Summer edition next  
> > year. So, I thought I’d let you know now, and I’ll send an update  
> > nearer the time, along with pre-publication copies.
> > 
> > ```
> > It's been a long road; by the time the paper is published it would
> > 
> > ```
> > 
> > have been over three years since first submitted, but at least it  
> > has now been accepted.
> > 
> > ```
> > Regards,
> > 
> > Rupert
> > 
> > ```
> 
> > \<nature.pdf\>

---

<div class="post-metadata">

**Author:** ![rsmarken](http://discourse.iapct.org/user_avatar/discourse.iapct.org/rsmarken/32/3553_2.png) [@rsmarken](http://discourse.iapct.org/u/rsmarken)\
**Post date:** [September 22, 2016, 4:49am UTC](http://discourse.iapct.org/t/pct-robotics-paper/8482/3 "2016-09-22T04:49:44Z")

</div>

[From Rick Marken (2016.09.21.2150)]

> **···**
>
> > Rupert Young (2016.09.21 22.40)–
> 
> > “ **A General Architecture for Robotics Systems:** \*\* A  
> > Perception-based Approach to Artificial Life\*\*”
> 
> > ```
> > I am pleased to say that my paper has been accepted for publication
> > 
> > ```
> > 
> > in the **Artificial Life** journal. It is basically applying  
> > the PCT architecture to robotics, but also positioning perceptual  
> > control as the missing ‘stuff’ of AI/AL (see attached).
> 
> RM: Congratulations Rupert!! How do we get a copy of the paper? The attached paper was by Rodney Brooks, who is not by any means a perceptual control theorist.
> 
> Best
> 
> Rick
> 
> > ```
> > It's a fairly long paper at 48 (book) pages (72 with refs and
> > 
> > ```
> > 
> > appendices) with a fair bit of background of putting PCT into the  
> > context of AI/AL, and a basic robotic experimental system.
> > 
> > ```
> > **Artificial Life** is a major journal in the field so it will
> > 
> > ```
> > 
> > be interesting to see the exposure and feedback it receives.  
> > However, there’ll be a bit of a wait. I was going to annouce this  
> > soon, when they sent out the contents for the Winter edition, but,  
> > for some reason, it has now been bumped to the Summer edition next  
> > year. So, I thought I’d let you know now, and I’ll send an update  
> > nearer the time, along with pre-publication copies.
> > 
> > ```
> > It's been a long road; by the time the paper is published it would
> > 
> > ```
> > 
> > have been over three years since first submitted, but at least it  
> > has now been accepted.
> > 
> > ```
> > Regards,
> > 
> > Rupert
> > 
> > ```
> 
> –  
> Richard S. Marken
> 
> “The childhood of the human race is far from over. We  
> have a long way to go before most people will understand that what they do for  
> others is just as important to their well-being as what they do for  
> themselves.” – William T. Powers

---

<div class="post-metadata">

**Author:** ![rupert](http://discourse.iapct.org/letter_avatar_proxy/v4/letter/r/f14d63/32.png) [@rupert](http://discourse.iapct.org/u/rupert)\
**Post date:** [September 22, 2016, 9:21am UTC](http://discourse.iapct.org/t/pct-robotics-paper/8482/4 "2016-09-22T09:21:25Z")

</div>

[From Rupert Young (2016.09.22 10.20)]

> **···**
>
> RM: Congratulations Rupert!! How do we get a copy of  
> the paper? The attached paper was by Rodney Brooks, who is  
> not by any means a perceptual control theorist.

---

<div class="post-metadata">

**Author:** ![Abbott](http://discourse.iapct.org/user_avatar/discourse.iapct.org/abbott/32/2879_2.png) [@Abbott](http://discourse.iapct.org/u/Abbott)\
**Post date:** [September 22, 2016, 12:14pm UTC](http://discourse.iapct.org/t/pct-robotics-paper/8482/5 "2016-09-22T12:14:04Z")

</div>

[From Bruce Abbott (2016.09.22.0815 EDT)]

Congratulations, Rupert! I look forward to reading the paper.

Bruce

Rupert Young (2016.09.21 22.40) –

“**A General Architecture for Robotics Systems: A Perception-based Approach to Arti  
ficial Life**”

I am pleased to say that my paper has been accepted for publication in the **Arti  
ficial Life** journal. It is basically applying the PCT architecture to robotics, but also positioning perceptual control as the missing ‘stuff’ of AI/AL (see attached).  
It’s a fairly long paper at 48 (book) pages (72 with refs and appendices) with a fair bit of background of putting PCT into the context of AI/AL, and a basic robotic experimental system.  
**Arti  
ficial Life** is a major journal in the field so it will be interesting to see the exposure and feedback it receives. However, there’ll be a bit of a wait. I was going to annouce this soon, when they sent out the contents for the Winter edition, but, for some reason, it has now been bumped to the Summer edition next year. So, I thought I’d let you know now, and I’ll send an update nearer the time, along with pre-publication copies.

It’s been a long road; by the time the paper is published it would have been over three years since first submitted, but at least it has now been accepted.

Regards,  
Rupert

---

<div class="post-metadata">

**Author:** ![chadtgreen](http://discourse.iapct.org/user_avatar/discourse.iapct.org/chadtgreen/32/92_2.png) [@chadtgreen](http://discourse.iapct.org/u/chadtgreen)\
**Post date:** [September 22, 2016, 3:53pm UTC](http://discourse.iapct.org/t/pct-robotics-paper/8482/6 "2016-09-22T15:53:14Z")

</div>

[Chad Green (2016.09.22.1153 EST)]

Fred, you’re also a participant on the EVALTALK listserv. A little over a week ago you posted this: “My favorite tagline is ‘Be sure you measure what you want.  
Be sure you want what you measure.’â€?

Did you notice Bob Williams’ reply to my post on Sept. 7 (Re: Complexity) concerning the implications of the paradigm wars in the systems science community? Toward  
the end he wrote:

“Because the management field failed to keep track of the important developments in the systems field in the 1970’s and 1980’s we now have a confusion of terminology.  
So for instance, nobody I know in the systems field talks about ‘at the systems level’ when talking about very large management processes. That comes straight out of the management field, but creates enormous problems for evaluators who believe that ‘systems’  
is solely about ‘big stuff’.â€?

Now take a look at your levels of HPCT example here:  
[http://www.nickols.us/LevelsofHPCT.pdf](https://urldefense.proofpoint.com/v2/url?u=http-3A__www.nickols.us_LevelsofHPCT.pdf&d=CwMGaQ&c=8hUWFZcy2Z-Za5rBPlktOQ&r=-dJBNItYEMOLt6aj_KjGi2LMO_Q8QB-ZzxIZIF8DGyQ&m=sSwT3URBML_0w7ZMPW99ijtSUGXkFPozGLrQNlHYVBI&s=RH1oOt1wG9rWAk-wOLi4d7USSveNOzvga8Ycqouelh8&e=) . Doesn’t level 11 also appear to reflect this outdated “at the systems levelâ€? construct to which Williams was referring? In other words, has PCT also failed to keep  
track of the significant developments in the systems field?

Maturana’s notion of the self as a dynamic relation rather than a persistent object may be a good replacement candidate for HPCT’s Level 11. Here’s a relevant  
passage from Maturana’s (1995) excellent paper Biology of Self-consciousness:

“As the self arises as an experience in the experience of self-consciousness, self-consciousness and self take place as dynamic relations in the flow of languaging,  
and cannot be talked about without living them as experiences in the flow of language. The result of this situation is that all explanatory propositions that do not propose to treat the self as an entity (that can be ‘experienced’) seem off the mark. Strictly,  
however, that is not the problem for the explanation, which as a generative mechanism only proposes a process that if it were to take place would give as a result the experience to be explained, and does not replace the explained experience as an experience.  
But that the explanation should show that the self, self-consciousness and consciousness are but relational dynamics in the flow of our living as human beings, seems difficult to accept because we exist for ourselves as entities.”

Source: [http://www.univie.ac.at/constructivism/archive/fulltexts/639.html](http://www.univie.ac.at/constructivism/archive/fulltexts/639.html)

Best,

Chad

> **···**
>
> Chad T. Green, PMP
> 
> Research Office
> 
> Loudoun County Public Schools
> 
> 21000 Education Court
> 
> Ashburn, VA 20148
> 
> Voice: 571-252-1486
> 
> Fax: 571-252-1575
> 
> “We are not what we know but what we are willing to learn.â€? - Mary Catherine Bateson
> 
> **From:** Fred Nickols [[mailto:fred@nickols.us](mailto:fred@nickols.us)]  
> **Sent:** Wednesday, September 21, 2016 5:53 PM  
> **To:** csgnet@lists.illinois.edu  
> **Subject:** Re: PCT robotics paper
> 
> [From Fred Nickols (2016.09.21.1752 ET)]
> 
> Yea! Congratulations, Rupert. Hard won and well deserved!
> 
> Fred Nickols, CPT
> 
> Writer & Consultant
> 
> **[  
> DISTANCE  
> CONSULTING LLC](https://urldefense.proofpoint.com/v2/url?u=http-3A__www.nickols.us_&d=CwMFaQ&c=8hUWFZcy2Z-Za5rBPlktOQ&r=-dJBNItYEMOLt6aj_KjGi2LMO_Q8QB-ZzxIZIF8DGyQ&m=wkjHuM9OpZDp4DcRKGiT71QrSUR1swWETodJsMq6Q8E&s=KemWkshkzRQwqHCkb3k73v_OzwtNmd6xaNZrcRbXyJI&e=)**
> 
> _“Assistance at a Distance”_
> 
> [  
> View  
> My Books on Amazon](https://urldefense.proofpoint.com/v2/url?u=https-3A__www.amazon.com_author_frednickols&d=CwMFaQ&c=8hUWFZcy2Z-Za5rBPlktOQ&r=-dJBNItYEMOLt6aj_KjGi2LMO_Q8QB-ZzxIZIF8DGyQ&m=wkjHuM9OpZDp4DcRKGiT71QrSUR1swWETodJsMq6Q8E&s=qT1NPzOAYNmtUpE8iLHPhr1Z68aoNXktttXwfsSaCxA&e=)
> 
> Sent from my iPad
> 
> On Sep 21, 2016, at 5:41 PM, Rupert Young [rupert@perceptualrobots.com](mailto:rupert@perceptualrobots.com) wrote:
> 
> > [From Rupert Young (2016.09.21 22.40)]
> 
> > “**A General Architecture for Robotics Systems: A Perception-based Approach to Arti  
> > ficial Life**”
> 
> > I am pleased to say that my paper has been accepted for publication in the  
> > **Arti  
> > ficial Life** journal. It is basically applying the PCT architecture to robotics, but also positioning perceptual control as the missing ‘stuff’ of AI/AL (see attached).  
> > It’s a fairly long paper at 48 (book) pages (72 with refs and appendices) with a fair bit of background of putting PCT into the context of AI/AL, and a basic robotic experimental system.  
> > **Arti  
> > ficial Life** is a major journal in the field so it will be interesting to see the exposure and feedback it receives. However, there’ll be a bit of a wait. I was going to annouce this soon, when they sent out the contents for the Winter edition, but, for  
> > some reason, it has now been bumped to the Summer edition next year. So, I thought I’d let you know now, and I’ll send an update nearer the time, along with pre-publication copies.
> > 
> > It’s been a long road; by the time the paper is published it would have been over three years since first submitted, but at least it has now been accepted.
> > 
> > Regards,
> > 
> > Rupert
> 
> > \<nature.pdf\>

---

<div class="post-metadata">

**Author:** ![MartinT](http://discourse.iapct.org/letter_avatar_proxy/v4/letter/m/ee7513/32.png) [@MartinT](http://discourse.iapct.org/u/MartinT)\
**Post date:** [September 22, 2016, 9:53pm UTC](http://discourse.iapct.org/t/pct-robotics-paper/8482/7 "2016-09-22T21:53:40Z")

</div>

[Martin Taylor 2016.09.22.17,36]

> [From Rupert Young (2016.09.21 22.40)]

> “ **A General Architecture for Robotics Systems:** \*\* A  
> Perception-based Approach to Artificial Life\*\*”

> ```
> I am pleased to say that my paper has been accepted for
> 
> ```
> 
> publication in the **Artificial Life** journal. It is  
> basically applying the PCT architecture to robotics, but also  
> positioning perceptual control as the missing ‘stuff’ of AI/AL  
> (see attached).

```
Let me add my belated congratulations to the others. I think you are

```

right about “missing stuff”. I also note that Brooks talks about  
perhaps going up to “five layers of artificial neurons rather than  
today’s standard of three”. Powers has eleven, with the possibility  
of layers within levels such as relationships of relationships (as  
he discussed with me at CSG 93). The problem of setting all the  
parameters for such complex neural networks is daunting when they  
are seen as one-way machines for making sense of the sensory  
environment, but not when each individual control “chunk” has its  
parameters set by the need for good control. That’s a technical  
detail, but an important one, in the same way as Powers’s discovery  
of the e-coli process allowed him to reduce reorganization time from  
the age of the Universe to continuous variation during a lifetime.

```
Again, congratulations.

Martin

```

---

<div class="post-metadata">

**Author:** ![rupert](http://discourse.iapct.org/letter_avatar_proxy/v4/letter/r/f14d63/32.png) [@rupert](http://discourse.iapct.org/u/rupert)\
**Post date:** [September 23, 2016, 8:07pm UTC](http://discourse.iapct.org/t/pct-robotics-paper/8482/8 "2016-09-23T20:07:39Z")

</div>

[From Rupert Young (2016.09.23 21.00)]

(Martin Taylor 2016.09.22.17,36)

> The problem of setting all the parameters for such complex neural networks is daunting when they are seen as one-way machines for making sense of the sensory environment, but not when each individual control "chunk" has its parameters set by the need for good control.

Yes, I think this is a very crucial point; that highlights a major difference between conventional wisdom and PCT. I see the problem, of trying to define a transfer function between input and output, in the real world, would only make sense if the system were a Laplacian Demon with entire knowledge of the state and dynamics of the universe, which is, of course, completely unfeasible.

For a long while I've been trying to come up with a simple, everyday analogy that would convey the difference between the adaptive nature of PCT and conventional open loop control with its hyper-sensitivity to parameters and the complexity of model-based control. Any ideas anyone?

Regards,  
Rupert

---

<div class="post-metadata">

**Author:** ![rogerkmoore](http://discourse.iapct.org/letter_avatar_proxy/v4/letter/r/b5ac83/32.png) [@rogerkmoore](http://discourse.iapct.org/u/rogerkmoore)\
**Post date:** [September 23, 2016, 9:13pm UTC](http://discourse.iapct.org/t/pct-robotics-paper/8482/9 "2016-09-23T21:13:43Z")

</div>

[Roger K. Moore 2016.9.23.11.10 BST]

> **···**
>
> On 23 September 2016 at 21:07, Rupert Young [rupert@perceptualrobots.com](mailto:rupert@perceptualrobots.com) wrote:
> 
> > [From Rupert Young (2016.09.23 21.00)]
> > 
> > For a long while I’ve been trying to come up with a simple, everyday analogy that would convey the difference between the adaptive nature of PCT and conventional open loop control with its hyper-sensitivity to parameters and the complexity of model-based control. Any ideas anyone?
> 
> You might be interested in the following which appeared as a (record length) footnote in a paper I published in 2007 …
> 
> “Heating an arbitrary room to a particular temperature requires the injection of just the right amount of heat based on the room’s size, the presence of other sources of heat and the means for heat loss. All this can be calculated analytically, but if any of the variables change, e.g. a window is opened or more people come into the room, then these disturbances would have to be sensed, their implications measured and the overall calculations repeated. Realising that such changes are unpredictable and happening all the time, and that the number of required sensors would get out of hand, the stochastic modeller decides instead to collect a database (in an attempt to capture the unexplained variability) with which to train a probabilistic system. The resulting device gives the right temperature 95% of the time (as long as the test conditions match the training conditions) but, in order to reduce the error rate even further, the only approach that is found to work is to collect more and more data. After many years of research, there is still a residual of variability that cannot be explained, and performance asymptotes. Through all this, it has been failed to notice that a simple thermostat would have quite adequately handled the infinity of possible conditions to a defined level of accuracy.”
> 
> Full paper here [http://fulltext.study/preview/pdf/568942.pdf](https://urldefense.proofpoint.com/v2/url?u=http-3A__fulltext.study_preview_pdf_568942.pdf&d=CwMFaQ&c=8hUWFZcy2Z-Za5rBPlktOQ&r=-dJBNItYEMOLt6aj_KjGi2LMO_Q8QB-ZzxIZIF8DGyQ&m=r42tgsO35NgyQ5IBjk0MWTA9MvPZHthUFwFlxZelCaE&s=ZuPl0YV6RUHSmZRN4HCpwyR2gdSq6jhFy_9pSUAELuM&e=)
> 
> Cheers
> 
> Roger
> 
> > Regards,
> > 
> > Rupert

---

<div class="post-metadata">

**Author:** ![rogerkmoore](http://discourse.iapct.org/letter_avatar_proxy/v4/letter/r/b5ac83/32.png) [@rogerkmoore](http://discourse.iapct.org/u/rogerkmoore)\
**Post date:** [September 23, 2016, 9:18pm UTC](http://discourse.iapct.org/t/pct-robotics-paper/8482/10 "2016-09-23T21:18:08Z")

</div>

Ooops, sorry about the $$$ link - I can send a pdf if anyone’s interested.

> **···**
>
> On 23 September 2016 at 22:13, Prof. Roger K. Moore [r.k.moore@sheffield.ac.uk](mailto:r.k.moore@sheffield.ac.uk) wrote:
> 
> > [Roger K. Moore 2016.9.23.11.10 BST]
> 
> On 23 September 2016 at 21:07, Rupert Young [rupert@perceptualrobots.com](mailto:rupert@perceptualrobots.com) wrote:
> 
> > [From Rupert Young (2016.09.23 21.00)]
> > 
> > For a long while I’ve been trying to come up with a simple, everyday analogy that would convey the difference between the adaptive nature of PCT and conventional open loop control with its hyper-sensitivity to parameters and the complexity of model-based control. Any ideas anyone?
> 
> You might be interested in the following which appeared as a (record length) footnote in a paper I published in 2007 …
> 
> “Heating an arbitrary room to a particular temperature requires the injection of just the right amount of heat based on the room’s size, the presence of other sources of heat and the means for heat loss. All this can be calculated analytically, but if any of the variables change, e.g. a window is opened or more people come into the room, then these disturbances would have to be sensed, their implications measured and the overall calculations repeated. Realising that such changes are unpredictable and happening all the time, and that the number of required sensors would get out of hand, the stochastic modeller decides instead to collect a database (in an attempt to capture the unexplained variability) with which to train a probabilistic system. The resulting device gives the right temperature 95% of the time (as long as the test conditions match the training conditions) but, in order to reduce the error rate even further, the only approach that is found to work is to collect more and more data. After many years of research, there is still a residual of variability that cannot be explained, and performance asymptotes. Through all this, it has been failed to notice that a simple thermostat would have quite adequately handled the infinity of possible conditions to a defined level of accuracy.”
> 
> Full paper here [http://fulltext.study/preview/pdf/568942.pdf](https://urldefense.proofpoint.com/v2/url?u=http-3A__fulltext.study_preview_pdf_568942.pdf&d=CwMFaQ&c=8hUWFZcy2Z-Za5rBPlktOQ&r=-dJBNItYEMOLt6aj_KjGi2LMO_Q8QB-ZzxIZIF8DGyQ&m=b_pMDWDC_BOrN1auuervV5Tp_mx7QQtDLLqSWK3IrHk&s=W2Ohx3s3wEn8vRU42pcz6OknIO4_fJSYGaOntkPX7II&e=)
> 
> Cheers
> 
> Roger
> 
> > Regards,
> > 
> > Rupert

---

<div class="post-metadata">

**Author:** ![Alex\_Gomez-Marin](http://discourse.iapct.org/letter_avatar_proxy/v4/letter/a/ebca7d/32.png) [@Alex\_Gomez-Marin](http://discourse.iapct.org/u/Alex_Gomez-Marin)\
**Post date:** [September 23, 2016, 9:35pm UTC](http://discourse.iapct.org/t/pct-robotics-paper/8482/11 "2016-09-23T21:35:23Z")

</div>

very nice!

btw, use sci-hub.cc

it opens all paywalls!

> **···**
>
> On 23 September 2016 at 22:13, Prof. Roger K. Moore [r.k.moore@sheffield.ac.uk](mailto:r.k.moore@sheffield.ac.uk) wrote:
> 
> > [Roger K. Moore 2016.9.23.11.10 BST]
> 
> On 23 September 2016 at 21:07, Rupert Young [rupert@perceptualrobots.com](mailto:rupert@perceptualrobots.com) wrote:
> 
> > [From Rupert Young (2016.09.23 21.00)]
> > 
> > For a long while I’ve been trying to come up with a simple, everyday analogy that would convey the difference between the adaptive nature of PCT and conventional open loop control with its hyper-sensitivity to parameters and the complexity of model-based control. Any ideas anyone?
> 
> You might be interested in the following which appeared as a (record length) footnote in a paper I published in 2007 …
> 
> “Heating an arbitrary room to a particular temperature requires the injection of just the right amount of heat based on the room’s size, the presence of other sources of heat and the means for heat loss. All this can be calculated analytically, but if any of the variables change, e.g. a window is opened or more people come into the room, then these disturbances would have to be sensed, their implications measured and the overall calculations repeated. Realising that such changes are unpredictable and happening all the time, and that the number of required sensors would get out of hand, the stochastic modeller decides instead to collect a database (in an attempt to capture the unexplained variability) with which to train a probabilistic system. The resulting device gives the right temperature 95% of the time (as long as the test conditions match the training conditions) but, in order to reduce the error rate even further, the only approach that is found to work is to collect more and more data. After many years of research, there is still a residual of variability that cannot be explained, and performance asymptotes. Through all this, it has been failed to notice that a simple thermostat would have quite adequately handled the infinity of possible conditions to a defined level of accuracy.”
> 
> Full paper here [http://fulltext.study/preview/pdf/568942.pdf](https://urldefense.proofpoint.com/v2/url?u=http-3A__fulltext.study_preview_pdf_568942.pdf&d=CwMFaQ&c=8hUWFZcy2Z-Za5rBPlktOQ&r=-dJBNItYEMOLt6aj_KjGi2LMO_Q8QB-ZzxIZIF8DGyQ&m=b_pMDWDC_BOrN1auuervV5Tp_mx7QQtDLLqSWK3IrHk&s=W2Ohx3s3wEn8vRU42pcz6OknIO4_fJSYGaOntkPX7II&e=)
> 
> Cheers
> 
> Roger
> 
> > Regards,
> > 
> > Rupert

---

<div class="post-metadata">

**Author:** ![FredNickols](http://discourse.iapct.org/user_avatar/discourse.iapct.org/frednickols/32/2645_2.png) [@FredNickols](http://discourse.iapct.org/u/FredNickols)\
**Post date:** [September 23, 2016, 11:06pm UTC](http://discourse.iapct.org/t/pct-robotics-paper/8482/12 "2016-09-23T23:06:29Z")

</div>

I’m interested.

Fred Nickols

> **···**
>
> **From:** Prof. Roger K. Moore [[mailto:r.k.moore@sheffield.ac.uk](mailto:r.k.moore@sheffield.ac.uk)]  
> **Sent:** Friday, September 23, 2016 5:18 PM  
> **To:** csgnet@lists.illinois.edu  
> **Subject:** Re: PCT robotics paper
> 
> Ooops, sorry about the $$$ link - I can send a pdf if anyone’s interested.
> 
> On 23 September 2016 at 22:13, Prof. Roger K. Moore [r.k.moore@sheffield.ac.uk](mailto:r.k.moore@sheffield.ac.uk) wrote:
> 
> [Roger K. Moore 2016.9.23.11.10 BST]
> 
> On 23 September 2016 at 21:07, Rupert Young [rupert@perceptualrobots.com](mailto:rupert@perceptualrobots.com) wrote:
> 
> [From Rupert Young (2016.09.23 21.00)]
> 
> For a long while I’ve been trying to come up with a simple, everyday analogy that would convey the difference between the adaptive nature of PCT and conventional open loop control with its hyper-sensitivity to parameters and the complexity of model-based control. Any ideas anyone?
> 
> You might be interested in the following which appeared as a (record length) footnote in a paper I published in 2007 …
> 
> “Heating an arbitrary room to a particular temperature requires the injection of just the right amount of heat based on the room’s size, the presence of other sources of heat and the means for heat loss. All this can be calculated analytically, but if any of the variables change, e.g. a window is opened or more people come into the room, then these disturbances would have to be sensed, their implications measured and the overall calculations repeated. Realising that such changes are unpredictable and happening all the time, and that the number of required sensors would get out of hand, the stochastic modeller decides instead to collect a database (in an attempt to capture the unexplained variability) with which to train a probabilistic system. The resulting device gives the right temperature 95% of the time (as long as the test conditions match the training conditions) but, in order to reduce the error rate even further, the only approach that is found to work is to collect more and more data. After many years of research, there is still a residual of variability that cannot be explained, and performance asymptotes. Through all this, it has been failed to notice that a simple thermostat would have quite adequately handled the infinity of possible conditions to a defined level of accuracy.”
> 
> Full paper here [http://fulltext.study/preview/pdf/568942.pdf](https://urldefense.proofpoint.com/v2/url?u=http-3A__fulltext.study_preview_pdf_568942.pdf&d=CwMFaQ&c=8hUWFZcy2Z-Za5rBPlktOQ&r=-dJBNItYEMOLt6aj_KjGi2LMO_Q8QB-ZzxIZIF8DGyQ&m=b_pMDWDC_BOrN1auuervV5Tp_mx7QQtDLLqSWK3IrHk&s=W2Ohx3s3wEn8vRU42pcz6OknIO4_fJSYGaOntkPX7II&e=)
> 
> Cheers
> 
> Roger
> 
> > Regards,  
> > Rupert

---

<div class="post-metadata">

**Author:** ![Matti](http://discourse.iapct.org/user_avatar/discourse.iapct.org/matti/32/3066_2.png) [@Matti](http://discourse.iapct.org/u/Matti)\
**Post date:** [September 24, 2016, 12:33pm UTC](http://discourse.iapct.org/t/pct-robotics-paper/8482/13 "2016-09-24T12:33:38Z")

</div>

[From MK (2016.09.24.1430 CET)]

Rupert Young (2016.09.23 21.00)]--

> For a long while I've been trying to come up with a simple, everyday analogy  
> that would convey the difference between the adaptive nature of PCT and  
> conventional open loop control with its hyper-sensitivity to parameters and  
> the complexity of model-based control. Any ideas anyone?

Marionette vs unskilled marionettist at the outdoor theatre in windy weather.

M

---

<div class="post-metadata">

**Author:** ![rupert](http://discourse.iapct.org/letter_avatar_proxy/v4/letter/r/f14d63/32.png) [@rupert](http://discourse.iapct.org/u/rupert)\
**Post date:** [September 28, 2016, 3:12pm UTC](http://discourse.iapct.org/t/pct-robotics-paper/8482/14 "2016-09-28T15:12:12Z")

</div>

[From Rupert Young (2016.09.28 15.50)]

(Roger K. Moore 2016.9.23.11.10 BST]

```
That's a very good way of describing it; and yes I'd like a copy of

```

the paper please.

```
It may be over-optimistic, but I'd like to come up with some snappy

```

one-liners to give an impression of the difference in difficulty and  
complexity between the two approaches, such as,

```
... trying to whack a golf ball hundreds of yards into a little

```

hole, as opposed to moving the hole to where the ball falls.

```
Suggestions welcome.

Regards,

Rupert

```

> **···**
>
> You might be interested in the  
> following which appeared as a (record length) footnote in a  
> paper I published in 2007 …
> 
> ```
> "Heating an arbitrary room to a particular temperature
> 
> ```
> 
> requires the injection of just the right amount of heat  
> based on the room’s size, the presence of other sources of  
> heat and the means for heat loss. All this can be  
> calculated analytically, but if any of the variables  
> change, e.g. a window is opened or more people come into  
> the room, then these disturbances would have to be sensed,  
> their implications measured and the overall calculations  
> repeated. Realising that such changes are unpredictable  
> and happening all the time, and that the number of  
> required sensors would get out of hand, the stochastic  
> modeller decides instead to collect a database (in an  
> attempt to capture the unexplained variability) with which  
> to train a probabilistic system. The resulting device  
> gives the right temperature 95% of the time (as long as  
> the test conditions match the training conditions) but, in  
> order to reduce the error rate even further, the only  
> approach that is found to work is to collect more and more  
> data. After many years of research, there is still a  
> residual of variability that cannot be explained, and  
> performance asymptotes. Through all this, it has been  
> failed to notice that a simple thermostat would have quite  
> adequately handled the infinity of possible conditions to  
> a defined level of accuracy."
> 
> Full paper here [http://fulltext.study/preview/pdf/568942.pdf](https://urldefense.proofpoint.com/v2/url?u=http-3A__fulltext.study_preview_pdf_568942.pdf&d=CwMFaQ&c=8hUWFZcy2Z-Za5rBPlktOQ&r=-dJBNItYEMOLt6aj_KjGi2LMO_Q8QB-ZzxIZIF8DGyQ&m=r42tgsO35NgyQ5IBjk0MWTA9MvPZHthUFwFlxZelCaE&s=ZuPl0YV6RUHSmZRN4HCpwyR2gdSq6jhFy_9pSUAELuM&e=)

---

<div class="post-metadata">

**Author:** ![rogerkmoore](http://discourse.iapct.org/letter_avatar_proxy/v4/letter/r/b5ac83/32.png) [@rogerkmoore](http://discourse.iapct.org/u/rogerkmoore)\
**Post date:** [September 28, 2016, 3:37pm UTC](http://discourse.iapct.org/t/pct-robotics-paper/8482/15 "2016-09-28T15:37:42Z")

</div>

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