The Year in Review

Good afternoon, dear reader, and welcome to the end of 2012.  Unless, of course, you live somewhere quite a ways to my East, and you've already entered 2013.  And, of course, this will be up on the Internet for all eternity, so a priori any reader of this is unlikely to still be in 2012.  But brushing all that foolishness aside, I am certainly still in 2012 right now, and I think I'll take this opportunity to look back over the year from a scientific perspective.

Let's be organized about it, and look at things in terms of different aspects of life as a scientist:

• Research Projects: the core of it all, carving truth from the substance of the world
• Publications: the primary product of research, and place to draw research threads together
• Funding: powerful amplifier of research, yet generally a trailing indicator of one's impact
• Position: one's institution and position within that institution affect opportunity greatly
• Impact: what difference one's research makes to others in the world
• Professional Service: organizing, reviewing, supervising students, etc.
• Work/Life Balance: that portion of life as a scientist which is not being a scientist

Not all of these need to advance every year, but in a healthy career, at least something significant should be happening in most categories.

Looking back over my own past year, the biggest change by far is in the area of work/life balance.  I've been running pretty hard for some years now, and since my wife is a scientist as well, "work/life balance" sometimes meant things like "let's sit all snuggled up on the couch while we work on our laptops."  In July, that changed irrevocably, with the birth of my daughter.  Now I live by an ironclad rule: from the time I get home to the time she goes to bed, I do not work, but spend time just being a parent to my child.  More than anything else, this means that I am having to give up perfectionism, and the notion that I can do it all and have it all.  My lack of effective triage has been slowly grinding me into dust, and with Harriet's arrival it has accelerated to the point where I can no longer pretend.  My goal now is to be only 80% of perfection.  This is extremely difficult, but feels doable---I suppose it is my New Year's Resolution.  Ask me at the end of 2013 how it has gone.

The other big news for me this year is in scientific publications, with four major journal articles and two book chapters, besides the usual collection of conference and workshop publications.  Those journal articles and book chapters loom larger than usual in my view, because of their contents: this is the year when we reported major results from my first funded project in synthetic biology, and in spatial computing we published two key formalizations of space/time computation (one for continuous space/time and the other for discrete), and a massive review of spatial computing programming languages.  Overall, it's been a very good year, and there's more in the pipeline from my ongoing research, so I feel very secure about my scientific base.

Funding's been much more of a mixed bag, but I'm still alive, and I'll just keep my fingers crossed on the proposals that are outstanding.  Position is a no-op (as one usually expects), and impact is hard to evaluate (Will my energy work escape the lab?  Only time will tell.), though Google Scholar indicates a significant uptick in my citations, which is always nice.

In the world of service, I am graduating a co-supervised PhD student, as I reported in this post.  The rest is pretty standard: we put out another special issue on spatial computing, and I'm continuing to act as an associate editor for ACM TAAS, plus running my seminar series at BBN and reviewing innumerable papers of highly variable quality.  I have also taken a big step by not being an organizer for the 2013 Spatial Computing Workshop (the sixth in the series, and I feel happy that we've been going long enough that I didn't know that number off the top of my head).  The 2012 edition was the best yet, and I have confidence that the others will do at least as well without me.

Putting it all together... I think I'm happy: strong on the scientific core and surviving OK everywhere else: not ideal, but a very good base to continue building on.  Next year will see big changes as well, both professionally and personally, and from where I sit right now, I think it will go OK.  And you can hear my perfectionism again, to not be all superlative, especially in a public forum like this.  Honestly, though, I think I prefer a quieter confidence that I can simply stand upon as a firm foundation for the year to come.

Better Living Through Manifold Geometry

"Better Living Through Manifold Geometry" was my cheeky title for our editorial introduction to the Computer Journal special issue on Spatial Computing that is just about to come out.  Alas, the final article appears to be receiving only the rather more boring simple appellation of "Editorial."

Regardless of title, though, the thing that I found quite striking as I actually read through all of the articles in the special issue, looking for the common threads that drew them together, is that spatial computing really has been getting much more coherent in intellectual approach, and that manifold geometry is one of the key concepts that keeps popping up.

My take on this is that it is not enough simply to recognize the locality and spatial embedding of a distributed system.  You also need representations that will let you take advantage of that insight, and normal Euclidean geometry, like we all learned in grade school, is just not sufficient.  We need our geometry to align with the structure of how information can actually flow, and the tool for that is a manifold.  The nice thing about manifolds is that they can give you the "stretchiness" of topology, warping around whatever constraints exist in the real world, yet they can still provide most of the nice geometric properties we want, like distances, angles, paths, volumes, etc.

The only problem is that we don't grow up learning about them, so most people find manifolds to be a  difficult and non-intuitive notion. Even our maps get flattened into Euclidean projections when the surface of the Earth is really a sphere.  And of course the formal mathematical notation typically just makes things worse.  But that's one of the things that I think we're nibbling away at, bit by bit, as we work on Proto and other spatial computing languages: how to capture the power and ideas of manifolds, but wrap it up in a way that makes it easy for any programmer to take advantage of it.

The International Journal of Mystery

Hi folks... I had a lovely vacation away from the internet last week, and now I'm back with another batch of scientific philosophizing.  Lots of discussions of papers queued up, but that will keep a little longer...

Recently, a junior colleague of mine was telling me about a journal publication he's working on, and told me he was a bit concerned because he wasn't sure whether the journal was actually any good or not.  To my great shame, the first words out of my mouth were "What's the impact factor?"  To my astonishment, his immediate reply: "What's an impact factor?"

I've been thinking more about this since.  Could I not have done something even slightly more worthy than immediately falling back to the shared common bugaboo of science?  After all, I don't generally pay all that much attention to impact factor either, and certainly can't quote numbers for most of the places I've published.  Is it so odd for my colleague to not have known about impact factors?  Moreover, I receive pseudo-personalized invitations to publish in various international journals every day and I ignore most of them as academic spam without even bothering to look up their impact factors. How do I actually judge the quality of a journal when I'm deciding whether to submit there?

First, for those of you so fortunate as to join my colleague in his innocence, let me explain.  Impact factor is a number used as a way of measuring how important a scientific journal is to a field of research---and therefore as a proxy for measuring how important a piece of research is by the company it keeps.   It is typically calculated using three years of journal articles indexed by Thomson Reuters, as the mean number of citations  in a given year to articles that a journal has published in the prior two years.  You're probably already thinking of objections: Why count only citations from journals? Who the hell is Thomson Reuters and how do they decide what's indexed? Why two years - don't we care if things stand the test of time? Can't people manipulate the system?  These, dear reader, are only the tip of the iceberg and there's a long tradition of scientists deriding impact factor as a metric, making up new alternative metrics that address some of the problems while creating other new ones, and generally adding to the chaos of standards.  Nevertheless, impact factor, like Microsoft Word, is the lowest common denominator that many are forced to bow to, by their institutions, by their funders, by their tenure committees...

Let's avoid going any further down that tempting rathole of a discussion.

Instead, let's return to the question at the root of the whole discussion:

Is this journal any damned good?

First, off, what do we mean by "good" when we're talking about journals? In my view, this basically boils down to three things.  In order, from most to least important:

1. Will my reputation be enhanced or tarnished by publishing here?  Some journals will add lustre to your work without anybody even reading it.  Rightly or wrongly, we primates love argument from authority.  Conversely, if you publish in a journal that's a total joke, people will wonder what's wrong with your work that you couldn't put it somewhere meaningful.
2. Will my work be read by lots of people?  I believe that most articles will only ever be noticed, let alone read, by people who found them by Googling for keywords in a literature search.  And your close colleagues should know about your work because you talk about it together.  Each community, though, typically has one or two publications that people just read because they feel it represents the pulse of their scientific community.  Get into one of those and you'll be seen by orders of magnitude more readers.
3. Will I be competently reviewed and professionally published? Amongst the great herd of middling journals, some are a pleasure to work with and some are a total train wreck.  In the end, though, if you get reviewers who give good feedback and the actual mechanics of publication are handled professionally, that's a nice bonus.

Ideally, impact factor ought to tell you about #1 and #2, but in practice I find it really only tells me about extreme highs.

So, what is it that I actually do in order to tell if a never-before-heard-of journal is any good?  Well, first I check the editorial board: Do I know them?  Do I know their institutions?  Of course, the really big names in a field are often not on boards, or on boards only ceremonially, since they're too busy.  I tend to look for the presence of solid mid-rank contributors and decent institutions---the sort of folks who I find form the strongest backbone of professional service.  But if nobody I've heard of in the field and nobody at reasonable institutions cares enough to help run the journal, then why should I think that publishing in a particular journal will make any impact?

If the editorial board hasn't convinced me one way or another, then maybe I'll check the impact factor, but really that's just a +/- test: if it has an impact factor of at least 1.0, that's a good sign, but a hazy one and not necessary, since many good venues have no impact factor and impact factor can be gamed.  More important is how long something has been around: anything that has survived at least a decade is likely to be solid (though again, not necessarily).

As for black marks: if a never-heard-of-it journal seems to have an extremely random or broad scope, then what could its community possibly be?  Those I always find suspicious, since it feels like they are just trolling for submissions.  Much worse than that, though, is if the publisher is a known bad actor, especially somebody who spams me repeatedly.  I'm sorry, Tamil Nadu, but your academic community will be forever tarred in my eyes by the people who fill my inbox with poorly targeted spam.

Sufficient? Hardly. But those, at least, are my own heuristics for dividing the worthy and the dubious when approaching yet another new journal. I suspect that this isn't a problem for people who don't do as much interdisciplinary work as I do, and that it was a lot easier a few decades ago when the number of journals was much lower. But think: if it's this hard to decide where to write, how much worse is the problem of finding what to read?  And that is a discussion for another time...

Author Order Semantics are Broken

OK, folks: rant time again.  This one's been in the back of my mind for a while, and dealing with several different papers recently that all managed their authors differently, the cognitive dissonance is high enough that I think it's time to get it out of my system.

Author ordering on scientific papers is totally broken.

Here's the thing: the order of authors on a paper matters a lot. It's exposure, since the first author is the one that gets associated with the paper, and you'll always see it cited as "[Busybody et. al, '01]," and not any of the other permutations.  And there's a lot of tea-leaf reading that goes on as people interpret the how to understand who's really to credit for the work in an article.  Only problem is, there's several conflicting theories of how to interpret author ordering.

Here's the main theories:

1. The first author is the most important, the second author less so, etc.
2. The first author is the most important, the last author is the senior author, the authors in between don't really matter.
3. Authors are listed alphabetically, with no author assumed to have significantly more credit.

Then there's lots of different sub-theories as well, having to do with who did the laboratory or coding work versus who did most of the writing, do you include only really important contributors or anybody who ever commented on the project, how does supervision play into the decision, etc.

Within any given community, there's usually some conventions, often driven by typical author list size. For example, my roots are largely in the more theoretical and software-driven side of computer science, where it's not unusual to see single-author papers, and most are probably 2-3 authors.  In that community, Theory #1 tends to dominate, and the bar for authorship is pretty high.  On the other hand, my work overlaps a lot with biology now as well, where there tend to be lots of authors, and Theory #2 is more typical.  I've also seen Theory #3 pop up in special circumstances, in which I am often unfairly privileged because my name begins with "B."

But these theories conflict, and no paper ever comes with a note saying which theory it belongs to.  Oh, there are journals where they have you put in a little assignment of responsibility saying "R.F. wrote the paper, J.X. performed the experiments, K.O. did the data analysis, and P.Q. killed mice until we begged him to stop."  But those are typically telegraphic at best, deliberately obscure at worst, and potentially subject to all sorts of odd internal group politics.

The real trouble comes at the boundary cases.  When there are ten authors, you can safely assume there's some tiebreaker policy in effect, and most of the ones in the middle aren't terribly important.  But what about 3 or 4 or 5?  Is the last the unimportant tag-along, or the all-important thought-leader / supervisor?  If three authors are in alphabetical order, is that a deliberate choice, or just a 1-in-6 coincidence?  How quickly does significance decay going down the list of authors?

Ultimately, I think the trouble comes from the fact that our language is linear, and we're trying to express a team structure that often is not.  If we had a symbology of authorship, that would perhaps help, so that one could draw the authorship as a graph with circles and boxes around names and arrows between them.  But that will never fly, and probably wouldn't make a difference anyway, since we still have to pick somebody to come first when we're talking about the paper with other people.

So, in the end, what do I think we should do about it?  I guess I'm feeling Churchillian tonight, because at the end of the day my feeling is this: author ordering is the worst possible way to indicate responsibility for a paper, but it's better than all the alternatives.

Keynote on Engineered Self-Organization

Just a brief note today, as I squeeze a post between proposal, paper, and parenting: earlier this month, I gave a keynote talk at the Through-Life Engineering Services Conference, a new conference put together by folks in England who are involved in a large mixed academia/industry project to tackle complexity in large-scale engineered systems like aerospace vehicles, the power grid, and trains.

Attending was fascinating for me, getting to see how people who are right in the middle of these manufacturing and management problems are actually thinking about things, and what applied research looks like in the area.  Actually, it helped clarify for me some ways of thinking and talking about my own research, particularly my work on energy demand management. Lots of interesting people too, and hopefully some of the possibly collaborations will come to pass.

The other nice thing about giving a keynote (and writing an invited paper to go along with it), is that it gave a good chance to put together a review on my work in engineered self-organization, and to pull together a unified view for myself of how all the pieces fit together. The talk, "Engineered Self-Organization Approaches to Adaptive Design," is on my webpage now (also in PDF), as is the paper---though unusually, I recommend reading my slides rather than the paper, since they were finished much later, and I think I understood the story my better by the time I wrote them.

Silent Communion

Tonight, I looked down into the growing dusk over Montana and saw a single tiny light burning amidst a vast expanse of snow.  It sat in the middle of dormant fields, wrapped around by the darker tendrils of a rough-hewn river system.  Five minutes later, another slides by, a fiercely orange pinprick of civilization alone in the wilderness of Western America.

Who are these lonely sentinels of the wilderness?  I hope they sit warm and content within their domains, no matter the frozen lands around, and I think how lovely silent it could be, alone in the snow and nothing to see but the land, the stars, and the planes passing by above.

Presentations and networking done, I am homeward bound through the night, pulled by the stream of pictures from home that trickled into my phone across the morning, images of my smiling daughter playing, laughing, sleeping, happy in Ananya's arms.  Tonight will be late and hard, gliding into Boston well past midnight, with an internal proposal deadline still to hit tomorrow.  But I wouldn't give it up for the world.  Just sometimes, looking down, I think how nice it would be to spend a month in a cabin in the wilderness, and just let everything stop for a while.

Swarm Presentation & Paper available

I've now posted the presentation and paper from my talk at the AAAI Fall Symposia online.  This is a case where I actually recommend the presentation, "From Spatial Computing to Tactical Command of Swarms,"and its accompanying bundle of live Proto demos over the paper.  The reason is simply that by the time I wrote the presentation, I understood much more clearly how to enunciate the contribution I am making in the area of swarm control.

It comes down to one of the core problems that I hit on again and again in all of these different areas: composability.  There are lots of clever ideas for how to make a swarm of robots do something together as a group.  Many of them come from natural inspiration (e.g., flocking like birds, foraging like ants or bees, flowing like water).  The problem however, is that robots are neither birds, nor insects, nor water. For any realistically complex application, there are a lot of different aspects that have to all be gotten right, and inevitably it is the cast that not all of those will be identical to any particular natural source.  For example, if you want your robots to flock together like birds, well, they probably don't steer like birds, and the consequences of hitting one another may be more severe than for birds, and their sensors pick up different sorts of information, and they communicate with different ranges, and so on and so forth.  So we need to take the basic natural behavior (e.g., bird-like flocking), and modulate it to fit the requirements of our actual platform and application.  Moreover, you're probably going to need to put a bunch of these different pieces together in order to get anything complicated done---and our ambitions for engineered systems are usually pretty complicated, even when the core ideas or main "normal mode" behavior is simple.

So what we get from a continuous abstraction like the amorphous medium, and composition models like Proto uses, is a clean model for how to put the pieces together to get complicated behavior.  Dataflow composition gives us a clean separation of different computations, state-through-feedback means we don't have to deal with weird interactions through persistent variables, and restriction---ah restriction, the most subtle spatial operation---lets us modulate behaviors by changing where they are being computed.

If you're interested, the talk lays it out pretty well, and the demos illustrate it really beautifully...

The Joy of Scientific Airline Travel

I'm writing this now in a plane, flying back from England, where I just gave a keynote on Engineered Self-Organization and spent a couple of days after the conference working out possible collaborations with colleagues.  I'll talk about all that sometime in the near future---right now, though, what I want to talk about is the joy of scientific air travel.

I never really flew much as a kid---my family tended to drive into the nearby wilderness for our vacations, so I never got exposed enough to become comfortable with flying.  When I started flying professionally in dribs and drabs during grad school, I was always completely afraid on takeoff and landing, willing the plane up into the air or safely down to the ground as I stared intensely out the window.  It didn't help either that I was coming from Boston, since all the landing paths at Logan Airport come in over the water, and you never have land below you until just moments before the wheels touch the ground.

These days, though, I rather look forward to it.  Somehow, flying transformed from a frightening necessity into a comfortable routine. Now I sit by the window just because I enjoy the view, and also because it gives me minimal interference from my fellow passengers. Once we're airborne, out comes my laptop or the papers I need to read, and there I am with nowhere to run and no Internet to find me (no, I have never paid for Gogo, and I pretend it doesn't exist).  It's a calm, focused time, tapping away getting things done, and with my MacBook Air these days, I can eke out around eight hours of battery if I'm just writing papers with my screen brightness turned down.

Sometimes I've got something in particular I need to do, other times I just open up my machine and take stock of the state of my intellectual world.  Some of my best thinking gets done while doing that (and you get some quality blog posts too).  It just seems rather ironic to me that one of the places I am most grounded is when I am 10,000 meters in the air.

I still sit by the window, whenever I can, so that I can look out at the world going by, see the intricacy of the land and the settlements of people upon it.  Clouds too, though I'll admit I find them boring after a while.  When it's clear down below, I love to watch my progress against the map and try to identify the landmarks as they go by.  Chicago is one of my favorite cities from the air, as is New York, and on a good day flying into Boston from the West, I can mark every major city, river, and highway from Utica on in.  Once, flying out of San Francisco, we passed right by Half-Dome in Yosemite, and it practically hovered there right outside my window, turning in three dimensions.

But why am I just talking about it?  This is a blog, and I can show you pictures just as easily.  Here are a few of my favorite memories from the air: flat and two dimensional, faded compared to how they looked in person, but maybe still enough to give you a feel.

Chicago

Hindu-Kush Mountains

Michigan Shore

If I ever stop caring to look out the airplane window, I'll know that I've lost an important part of my soul.

An Accidental Investigation of Publication Metrics

Dear reader, welcome once again to one of my more philosophical posts.  I've been working on reorganizing my webpage---something long in need of doing.  It used to make sense, when I was a grad student or a young postdoc, to have a simple list of all my publications.   Over the past few years, though, as both the number and variety of my publications has grown, I think this has become less sensible.  Now the list is rather long, and all silted up with the detritus of scientific publication---dead ends, early work, incremental reports, and important-but-boring filling in the gaps.

One of the things that makes my webpage such a mess is that my current list does not discriminate between types of publication: journals, book chapters, conferences, workshops, tech reports, and unpublished white-papers are all jumbled together in chronological order (possibly the worst reasonable ordering tiebreaker).

I used to solve the density problem by segregating the publications by subject area.  Subdividing further would be unsatisfactory to me these days, however, since there are so many connections between different pieces of work---do I put the first "functional blueprints" paper into morphogenetic engineering or spatial computing, since it was much more focused on spatial/cellular approaches than what came after?  How about my energy work, which started out as an application of Proto, but has evolved to shed both Proto and spatial computing in general?  There are far too many such boundary cases, and I don't want a reader to miss a publication because they're looking in the wrong section.

I suppose I could resolve the density problem by segregating them into type: put the Respectable Journals up front, followed by the High-Impact Factor Conferences, and so on.  Problem is, I've got tech reports and workshop papers that I think are more important than some of my journal papers.

Which leads to a general comment on scientific publication, I think.  So far, in my career at least, I find there to be a minimal correlation between importance of publication and "significance" of venue. An idea put forth first in a workshop (the amorphous medium abstraction), has become the most central element of my whole line of research, and I still cite that workshop paper.  Maybe someday it will be replaced with a Reputable Journal paper updating and expanding the results, but that hasn't happened yet, and isn't likely to happen soon, what with my jam-packed publication queue and parenthood.

So, let's see how my intuitions hold up against data (ah, the scientific lifestyle), and try plotting "venue" vs. "importance" .  First, I've gone through all the publications on my website and pulled out those that I think are "important," further coding some of them as "foundational"---meaning they are something whose importance I think is broad and durable, generally leading meaning it's at the root of a significant ongoing research program.  Now let's group them into publication classes using my CV, which lists 91 non-thesis publications (Google scholar finds more, but we'll ignore that whole can of worms for the moment).  In my CV, where publications are broken up into six classes, which we'll order by typical ferocity of peer review (a proxy for venue quality), in decreasing order: Journal, Conference, Book chapter, Workshop, Abstract, Informal (tech report, white-paper, etc.).  Plotting the numbers of each type as a stacked graph, we have:

Huh... my publication profile actually looks a lot more conventional than I expected.

It's completely unsurprising that the abstracts are barren of value, since they're typically just too short for anything significant---no more than two pages.  The big surprise, looking at this, is how barren the conferences are.  My guess is that a lot of those "unimportant" conference articles are steps on the way to a more complete result---and looking more deeply into them, it seems like about half of them are exactly that.  That workshop articles are largely barren is less of a surprise, since so many of them are position papers, dead ends, or roads not taken---and a deeper inspection confirms that completely.  Workshops are apparently where I toss ideas against the wall, and some of them stick (with massive importance), while most of them just fade away.

Digging into those journal articles further, I find that six of the eight journal articles started life as a "lesser" publication, and then were extended and upgraded into a full journal publication---which then supersedes the prior publication in importance, hogging all of the spotlight.  That's appropriate, I suppose.

Does this mean that I should expect the foundational workshop and informal publications to migrate into journals as well over time?  Perhaps they will---and in fact, I know that one of them is trying to already.

So what we have here in many ways is a "revisionist" picture of science, where the material that turns out to be important ends up migrating over time upwards in venue quality.  If that's the case, then "journal papers are more important" is only true for people who aren't the author: it's a selection process that retroactively highlights the important work, rather than a leading indicator.  Perhaps we should instead think of publications as some sort of an exploratory tree process.  Here's a notional diagram of what that might look like:

Color to match bar graph above.  Arrows indicate dependency, pointing from a dependent work to its source.  Size: large=journal, medium=conference/chapter, small=workshop/abstract/informal. Concentric publications indicate "venue promotions" that supersede a prior citation.

Let's say a research program started at the large bottom node with a workshop publication.  As it goes up and out, it grows and branches.  Importance tends to relate to how much research is running back through a publication.  Also, as publications become more important, they sometimes upgrade into more "quality" venues---which renders the prior version (shown as concentric) unimportant.  Sometimes a big step can be taken directly, sometimes it needs to go through bridging stages on the way.  And of course there are lots of things that end up staying unimportant, either because the initial idea was wrong, hit a dead end, or just plain got triaged by the 24-hours-per-day limit.

I suspect that I may have a somewhat higher than average branching factor, given the nature of my research and personality.  I don't know though---this may instead be an impression that I've gotten due to the operation of just such a process.  After all, the informal publications tend to fall away from visibility if they are not deliberately preserved and archived online by a researcher, and it's hard to see anything besides the mature work of another researcher.  It would be fascinating to study this over a number of scientists, but really hard to do effective coding on publications.

Coming back to the root problem that started me down this intriguing rathole: when it comes to laying out my webpage, since I'm going to be showing people a snapshot of time, I think it's only right to classify things by current perceived importance, and not by category.  And now, dear reader, an exercise for you: let's see just how long it takes between this post and an actual restructuring of my website.  If it happens very quickly, it probably means I'm engaged in proscrastination; if it takes more than six months, well, you have my permission to point and laugh.  And if you're a scientist reading this, would you be willing to contribute a coding of your own publications?

Swarm Control at AAAI Fall Symposia

Later this week, I'll be giving a talk at a symposium on Human Control of Bio-Inspired Robot Swarms (part of this year's AAAI Fall Symposium Series).  I like the AAAI symposia, because they're a really good place for position papers and preliminary work: you can put your ideas out there, get some good feedback, and at the same time put it in an archival place where people can cite it if they find it useful and inspiring.  I think it's also one of the things that keeps the AI community from being scoop-fearful like many other communities.

Anyway, my talk is about an application of my continuous space abstractions and Proto that should be pretty obvious: controlling large swarms of robots.  We published a journal paper aiming in this direction a couple of years ago, talking about how viewing a swarm as a "material" flowing through space makes it very simple to create complex swarm behaviors.  Rather than worry about all the individual robots and how they should interact, you specify how regions should stretch and squish and flow.

This isn't entirely new---other people have used continuous space models as well, though in a much more control theory or partial differential equations way.  The nice thing about Proto is that once you've got a behavior specified, you can build on it, modulate it by choosing which robots are participating, compose it with others, etc., and it's all very simple and easy to predict what's going to happen.  That's how you can build up such complex behaviors so quickly and easily---once you've got a few building blocks, you can go wild putting them together.

Well, this week's paper is pushing that work forward, asking the question: What's a good interface for letting ordinary people talk about what they want their swarm to do?  I'm proposing that a good starting point is a sort of "command and control" model where you break your swarm into units, and then talk about who's supposed to stick together, where they're supposed to move, and how much they should be long & thin vs. thick and fat.  Or to be more precise: specifying the first three moments of the swarm distribution for each unit.  That makes it easy to make formations like these:

Swarm moving in a dumbbell formation, and another in a chevron formation.

There's a bunch of other thoughts in there, and proposals for how we can turn this idea from early work into something practical for people to use with swarms.  Not that there is much in the way of actual swarms out their yet either (with some elegant notable exceptions), but that's only a matter of time and cheapening hardware, and there's a lot of folks working on that...

I'll post the paper after the talk is given.