NB: Some of the movies use old formats and may not work on your machine without conversion. All are also now available in the WEBM format, which should work, and should be tried first.
All the videos are downloadable, under creative commons 3.0 licences.
Anyone interested in obtaining and running the toolkit (including some of the demos
'frozen' here) should see The Free Poplog Web site.
There is a pop-forum email group that can be used for asking questions, reporting new experiments, etc.
More examples may be added later. And there's always
the Pop11 Eliza
as a mildly entertaining last resort. See also the Youtube videos demonstrating
Poplog and some of its packages and teaching materials, listed here:
Note on Pre-SimAgent demos: Items 10 and 12 below include demos using our pop11 tools before SimAgent was developed. Experience with those projects helped to show the need for the SimAgent tools and the RCLIB graphical interface. If you would like to explore some pre-cursors to SimAgent take a look at the demos near the end of this file. (Code for both still works.)
Later movies were produced using the excellent Xvidcap tool -- a far more convenient method. All were generated using Poplog + the SimAgent toolkit running on either a Sun computer or a PC with the linux operating system.
However, the .mpg movies produced originally don't seem to work with all modern players, so webm versions have been added, all tested using the VLC player.
In some of the movies the mouse-movements produced by interacting with the running program produce movements of picture objects without the mouse pointer being visible. So the resulting movements may look mysterious.
Some of the later movies recorded with the xvidcap tool do show the mouse pointer as well as the objects moved.
When the demos (apart from the first two, and the minder movies, produced before RCLIB had been produced) are running "live", objects can be moved with the mouse and their new locations in the simulated scenario (virtual world) will immediately be sensed either by those objects or by others, or both, and some behaviours may be changed as a result. All of this happens in a running virtual machine, though the effects can be seen on a physical display that records what happens in the virtual machine.
E.g. in the sheepdog demos, virtual sheep that have been put in the virtual pen can be moved out using the mouse, and the virtual sheepdog will then notice and try to get them back. The 'hybrid sheepdog' movies also include trees and sheep moved with the mouse. Likewise some of the other movies.
However, since these are recordings, not 'live' demos, you will not be able to use your own mouse to change anything!
All the graphical interaction depends on the use of the core SimAgent toolkit augmented with the RCLIB graphical toolkit implemented as an extension to Pop11 in Poplog. This handles not only the display of scenarios on the screen, but also the interaction using mouse and keyboard.
Some features of RCLIB are described end demonstrated here
The first two movies show the 'boids' (flocking) program and a tileworld agent, provided by Mike Lees, at Nottingham, supervised by Brian Logan.
The flocking program shows agents 'flying' randomly at first then gradually coming together in a flock. This may not seem obvious fora while because the agents treat the display square as a toroid, i.e. the upper and lower edges are joined, and the left and right edges are joined, so that an agent exiting across an edge re-enters at the corresponding point on the opposite edge.
The tileworld movie (No 2) is harder to understand, but has some agents moving around in a rectangular grid triggering various events.
The earlier sheepdog was designed by Peter Waudby, a Physics graduate, for his Cognitive Science MSc project, in 1996. The sheep are autonomous, and partly unpredictable, unlike the dog. They react to the presence of the dog by trying to move away from it. If they detect something in their path (another sheep, a tree, or part of the pen) they tend to swerve to avoid it, a strategy that can sometimes lead to a sheep getting jammed between two or three other things. The sheep behaviours are mostly unchanged in all the later demonstrations, except for one of the sheep in Dean Petters' program, described below. That sheep has to "learn" to move away from the dog, as explained later.
The program remains available (slightly modified
by Aaron Sloman) as a demo included in the Poplog Simagent toolkit:
SimAgent code and comments.
(Peter later went on to work for a (then) well known computer game company.)
The third and fourth demos use an extended version of Peter Waudby's sheepdog scenario designed and implemented by Tom Carter, a philosophy graduate with no programming experience prior to starting the Birmingham MSc in Cognitive Science.
His project extended Peter Waudby's program by making the sheepdog autonomous, but only able to herd one (randomly selected) sheep at a time, though a human could use the mouse to move the dog, the sheep and the trees (obstacles) so as to frustrate or help the dog in its task.
Tom's sheepdog has several sub-strategies, that get turned on an off automatically according to the situation. It first selects a sheep, then, if not already close to that sheep, moves until it is close to it, possibly avoiding trees, the pen, or other sheep on the way -- or failing to avoid them!
When close to its chosen sheep it attempts to steer that sheep until it is within the 'cone' of space emerging from the pen. It then changes strategy and attempts to move the sheep into the pen. But if the sheep is moved out of the cone, or the dog is moved away from the sheep, or a tree is moved to block the path, the dog can (in many cases) change its behaviour, reverting to an earlier stage if needed. The code for Tom Carter's sheepdog, slightly modified by Aaron Sloman, is available here, as part of the poplog teaching materials for SimAgent.
Tom's report on his project is online here (PDF).
There is no learning in that sheepdog or in any of the others below. One student attempted to use evolutionary computation to develop a successful sheepdog, but the resulting animal was able only to heard one sheep if the gate of the pen, the sheep and the dog lay on the same line in that order, initially.
The next sheepdog includes the reactive capabilities of the previous versions, but adds deliberative capabilities that allow plans to be made several steps ahead, even in a maze-like configuration of trees.
The fifth example, the hybrid deliberative/reactive sheepdog was an introductory mini-project by Marek Kopicki done in November-December 2003, during his MSc course building on the earlier sheepdog programs. Marek had done a degree in physics and began a PhD in physics, funded by part time programming work. When he decided to switch to computing he came to Birmingham to do our MSc. Although this was a degree in computer science, not cognitive science, he was interested in learning some AI, so I supervised his first term mini-project. This was the result.
Marek's program demonstrates a 'hybrid' deliberative/reactive version of Tom
sheepdog presented earlier. The new sheepdog is able to find its way round far
more complex barriers than the purely reactive one can. It does this by
sometimes pausing to 'think' about where to go: i.e. it makes a plan, using the
"probabilistic roadmap" technique described below. Having embarked on a
plan, it does not blindly follow it: instead it can tell that something has
changed since the plan was made and there is a new better option (e.g. a short
cut) or a new obstacle. It sometimes proceeds by making a small adjustment to
the existing plan, and sometimes by making an entirely new plan.
So it alternates between simply reacting to what it senses while following an existing plan and making a new plan: one of its internal reactive behaviours is to switch to local plan-repair mode.
The lines shown on the display indicate the sheepdog's current plan, and while it is moving it also displays its 'line of sight' to a later plan location. This is used for smoothing the plan, and for detecting new short-cuts.
So the dog combines interleaved local re-planning and reactive plan execution with minor (smoothing) adjustments, occasionally having to switch to global re-planning because the current plan has met an obstacle.
There are three movies. The first two were produced using the Xvidcap tool.
The third movie was produced earlier using a different technique and does not show movements of the mouse.
This shows the sheepdog fetching the sheep one at a time and steering them to the pen. It repeatedly plans a route, displayed on the screen, then follows the route, reactively adjusting its plan as new obstacles and opportunities are discovered, resulting from objects (trees, sheep, dog) being moved with the mouse. Sometimes a new obstacle turns up that requires global re-planning.
You can see the mouse moving things in the display. (The mouse pointer is a black arrow.)
The dog may have to create a new plan when something changes, or modify its existing plan, or modify how it executes the plan, e.g. taking a short cut that either did not exist when the plan was created or was not noticed when the plan was created, because the plan-creation process does not aim for an optimal plan, just a reasonable one.
Searching for an optimal plan would take very much longer. Notice that in some sense the dog is conscious of where the sheep are, where the trees are, whether it has been moved, whether a short cut is available. Its range of visibility extends over the whole terrain (as if it could see through trees), but that does not mean it notices every relationship between things it sees.
Another demonstration of the same program, this time showing the 'probabilistic waypoints' used by the sheepdog to make its plans: their use hugely reduces the space of possible plans. As a result the dog can find a plan very quickly by searching the graph of non-obstructed connections between the waypoints. However the plans thus found are often non-optimal and the sheepdog reactively discovers opportunities for improving them by smoothing them as it acts on the plans.
The plan construction is very fast, using the technique of 'probabilistic waypoints'[*], (also known as 'Probabilistic Road Maps').
The potential search space for paths is astronomically large because the sheepdog has a location and heading expressed in 'real' numbers (i.e. decimals -- the exact precision depending on the machine architecture used). The image displayed is about 660x660, but the possible x and y coordinate values along a horizontal or vertical line will be very much larger than that, resulting in a truly huge space of possible paths.
But instead of searching in that huge space of all complete paths in the terrain, the dog randomly generates a collection of 'waypoints' rejecting all except those that are close to but not too close to other objects, and searches for paths from the current location to the goal location going only through the waypoints, using non-obstructed straight lines joining the waypoints. (The number of waypoints selected is a parameter set by the programmer. So the program randomly generates a pair of coordinates in the permitted range, and discards the pair if the location is too close to one of the objects in the scene, or too far from the nearest one. So it might do that until it has found 250 points, a parameter set by the programmer. It then considers paths linking all pairs of points and rejects those that pass through or too close to obstacles. The remaining set of points determines a not very large graph of possible motion paths in which a planning program an search for an optimal route.
This dramatically reduces the search space for the planner, compared with considering all possible routes in the terrain. As a result, on a 1Ghz PC running linux poplog most of the plans shown are found almost instantaneously -- though it is not guaranteed to find the shortest plan and may miss some narrow gaps.
The movie shows the waypoints generated just before each plan is formed, in addition to showing the selected plan. The plan may be somewhat jagged because the waypoints are not necessarily optimal because they are randomly generated. These detours are smoothed as the plans are followed. The movie shows how, while executing a plan the sheepdog can reactively detect an unexpected obstacle or problem produced by the sheep moving in an unexpected way. It can also detect a new opportunity for short cuts e.g. because a tree has been moved out of the way.
In 5.3 the mouse movements are not visible, though the effects are.
In this movie some of the objects were moved using the mouse, sometimes making things worse for the sheepdog, sometimes allowing new short-cuts. However, the mouse is not shown.
As before, some of the newly detected problems and opportunities require only local modifications to the current plan, whereas in some cases an unexpected obstacle (in one case another sheep!) causes global re-planning.
Program code for the hybrid sheepdog
The code for the hybrid sheepdog, used to produce the video, is now included as a demonstration library in the SimAgent package.
The program file is browsable separately here.
The toolkit, and the hybrid-sheepdog should work in any version of poplog running on a linux/unix machine with the X window system, and also on a windows PC with Vmware,, or using Poplog in combination with Slackware7 on a bootable USB stick.
A report on the mini-project is available here.
Are any of these simulated sheepdogs conscious?
(Added 25 Jul 2018)
I have argued that our ordinary concept of "consciousness" exhibits "parametric polymorphism", as explained in
This implies that the question "Is X conscious?" without any further specification, is unanswerable, like "Is X efficient?" or "Is X good?".
If the question about the sheepdog is expanded to specify what sort of consciousness with what sorts of content is being asked about then I would argue that with some expansions, the answer would be "Yes it is" (e.g. in some circumstances it is conscious that the route it is following is now blocked, or that there is a new shortcut that provides a better route). As for what sorts of "qualia" that entails, I suspect our concept of "qualia" in addition to lacking precision, presupposes richer forms of consciousness than this sheepdog simulation has. In a different demo, by Dean Petters, the sheep also have a (simple) form of consciousness, e.g. of the proximity of the dog.
[*]Matthias S. Benkmann (2001) Motion Planning Using Random Networks
The sixth example shows a rather old 'Feelings' demo which is one of the early tutorial examples in the SimAgent toolkit. (I think the original version was available around 1996, or possibly a little later.)
Further examples come from Dean Petters' MSc project in 2001 (sheepdog herding several sheep, one of which has to 'learn' to get away from the sheepdog -- not shown here) and some exploratory programs written during his PhD modelling 'attachment' processes in infants.
This movie shows two "toy" emotional agents partly moving of their own accord (each keeps trying to get to its 'target' of the same colour while avoiding obstacles), and partly being moved with a mouse (not shown in the movies).
They each show, in faces at the bottom of the display, a set of 'emotional' states, teasingly labelled 'glum', 'surprised', 'neutral', and 'happy'. Each agent also prints out in the text stream below its face the results of its introspections as to what it feels and why.
The details may be easier to take in if you can slow down the video player (e.g. VLC allows this, at least on linux). Otherwise it may be worth repeatedly using the pause button to check on what has just happened that could explain the changes in emotional state. Unfortunately this recording was made without displaying the mouse pointer that occasionally moves one of the agents, obstacles, or targets, triggering a 'surprise' reaction.
Of course, this is just a shallow toy developed for tutorial purposes rather than anything that could be said to have human-like emotions -- except insofar as some (but not all) human emotions involve reacting to things that achieve or hinder goals or desires. However the notion of 'surprise' in this demonstration is pure fake, since these simple creatures have no expectations and therefore cannot be surprised. But the change in facial expression is fun.
If you fetch and install the package on a machine on which poplog can run, e.g. PC with linux, then you can run the program at varying speeds and play with it -- frustrating or helping the little movers. For a student learning programming, enriching the behaviours, e.g. by giving the movers richer minds, could be a useful educational experience.
These agents differ from many so-called emotion simulations in that these creatures have some self-knowledge so that they not only show their state (in facial expressions and in movements) but also describe them (which humans cannot always do!) This demo uses, in a simple way, features of the SimAgent toolkit that allow 'self monitoring' activities to run in parallel with others.
Some of the events are caused by objects moved with the mouse while the demonstration was recorded. Unfortunately you will not be able to interact in the same way with the movie of the program running.
This demo uses a slightly modified version of the SimAgent 'Feelings' tutorial file
which is included in the SimAgent toolkit:
Like the sheepdog this simple demonstration is potentially indefinitely extendable to include more complex and sophisticated kinds of processes involving learning, reasoning, communicating, etc. The whole package, including poplog is freely available for anyone who wishes to use it for teaching, research or anything else.
There are many "more serious" papers and presentations concerning emotions, other kinds of affect, and architectures in the Cognition and Affect web site, e.g.
After spending a year here in Birmingham in 2000-2001, Matthias Scheutz used the SimAgent toolkit for teaching and research while he was at The University of Notre Dame (USA). (Now at Tufts University).
His Simworld package is available here as a gzipped tar file that can be run in a poplog environment. A paper on a later development based on SimWorld is
A short MPG movie on simworld was available but has been lost. (If requested I may be able to generate another.)
Two movies show the sheepdog herding several sheep simultaneously, using a program produced by Dean Petters in 2001, his MSc year. One of the sheep has to learn to move away from the dog in order to avoid 'unpleasant emotions' that occur when it is close to the dog. The other sheep instinctively move away from the dog. The dog is not able to herd all the sheep until the "emotionally impoverished" one becomes normal! (This is not shown in the movies here.)
This is (as far as I know) the only sheepdog program produced so far with this toolkit that enables the dog to move several sheep simultaneously.
In these recordings the mouse is used to move the sheep and the dog, sometimes helping the dog, sometimes making its job harder.
The new model helps to explain why one shepherd and a single dog can herd an unruly flock of more than 100 sheep.
It could be used to help develop "shepherd robots", for controlling crowds or
cleaning up an oil spill.
The Guardian reported the news thus:
"Obvious cases are robot-assisted herding of livestock, and keeping animals away from sensitive areas, but applications range from control of flocking robots, cleaning up of environments and human crowd control."
Dean stayed on to do a PhD, jointly supervised by a clinical-developmental psychologist, Dr Gill Harris, which included the work reported in the next section.
The next two movies (added 24th April 2004) made use of a still incomplete program being developed by Dean Petters as part of his PhD work on modelling 'attachment' processes in infancy. (Petters, 2006)
The movies show fragments of a scenario with a carer and a baby, both moving around in an environment containing food and other things, viewed by from above by a downward-facing (simulated) camera. There is a dynamic histogram showing the changing measured states of the baby. Everything is highly simplified of course.
The mother needs food from time to time, and the baby needs to explore, and to get attention from the mother. Sometimes the mother oscillates between going for food and attending to the baby. Different 'personalities' can be given to the mother to show how that affects the development of the baby.
More information about the project is on Dean's web site and in a paper presented at the AAAI 2004 Spring symposium in March 2004.
Both movies show several aspects of the baby's internal state going up and down. The column labelled 'a_sec' corresponds to insecurity, produced by the mother being too far away or not looking at the baby. At the time this was recorded this was still work in progress towards the PhD thesis completed (part time) a few years later. The work was recognised by a leading attachment researcher in the USA, who began a collaboration with Dean as a result.
NOTE ADDED Jan 2007:
Dean's PhD thesis was completed in 2006 and can be downloaded from:
This was originally proposed around 1986 by A.Sloman (while still at Sussex University) as a framework for investigating architectural issues in complex multi-functional intelligent agents with multiple, changing sources of motivation, embedded in rich and dynamic, only partly known environments.
Since about 1991, when he came to Birmingham, the scenario, modified in various ways at various times, has inspired much of the work in the Cognition and Affect project, starting with the PhD theses of Luc Beaudoin and later Ian Wright.
In 1994, while working towards his PhD, Ian Wright implemented some of the ideas developed by Luc Beaudoin in a version of the nursemaid scenario referred to as 'Minder Version 0'. The program was shown in a BBC2 Television interview with A.Sloman, broadcast on February 1997. Since then the program has had its 'cosmetics' improved by A.Sloman, and the new version is used for the movie demos below.
The movies show the 'minder' indicated by a capital 'M' looking after variable numbers of 'babies' labelled 'a', 'b', 'c', etc. whose charge state is indicated by an '*' when fully charged and a decreasing single digit number as the charge decreases. The babies move around at random, using up energy and reducing their charge level. There is a recharge point (represented as two curved arrows) to which M can carry babies when their charge gets dangerously low. They die if their charge gets to 0. At the top and bottom edges of the nursery are ditches and if babies get too close they risk falling in and dying. M notices when a baby's charge level is low, when a baby is near a ditch, and when a baby is dead, acquiring new motives in each case. Noticing that a baby is dead generates a motive to carry it to the 'disposal' location, marked with a skull and crossbones! A further motive can be triggered in M whenever there are three or more individuals in a room: that makes the 'room too crowded' and M acquires the motive to carry a baby to another room. M's motives are prioritised (e.g. how low a baby's charge-level is, how close it is to a ditch, etc., with disposal of dead babies as having lowest priority, followed by reducing crowding).
The nursery has several rooms and M's knowledge of the contents of the rooms is constantly updated by a camera that scans the rooms in turn. It is possible for M to act on information about what is happening in a room that is out of date because the camera has not refreshed M's view of the room.
Thus as babies move around and their energy levels change, M's motives keep changing and sometimes M's work on a motive has to be abandoned in favour of a new higher priority motive. M has very little intelligence, and completely lacks deliberative capabilities. Thus all its plans are stored reactive plans, activated by motive generators, and reactively executed or over-ridden. M also has very little knowledge about itself, and often behaves in stupid ways, e.g. attending to a distant baby before a nearby one. There is no learning. When there are only 5 or 6 babies M may be lucky and keep them alive for a long time, though sometimes even that situation proves too much. The more babies there are, the more harassed M becomes and because it has no way of filtering new motives, they all grab its attention and can cause it to behave in a far from optimal fashion. Later work by Ian Wright developed the suggestion, in Luc Beaudoin's thesis, of using a dynamically varying threshold for an attention filter. (A movie of his later program will be added to this site eventually.)
Here are several runs of the Minder program, the core of which is unchanged since 1994, though the graphics have been altered. (The original showed both the 'real' world and the world as perceived by the minder, and used only the editor buffer for textual output, whereas this version shows only the real world and uses graphical 'posters' in RCLIB to display changes in motivation.)
This program does not use SimAgent, as it is implemented directly in Pop-11. However the experiences and problems arising out of this work helped to define requirements both for the RCLIB package, and the SimAgent toolkit, both developed later. Ian Wright's implementation of Minder 1 for his thesis used the toolkit.
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The Poplog 'RC_BLOCKS' package is used to produce the saved image called 'gblocks', demonstrated below. This makes use of a 'toy' demonstration program, assembled for AI teaching purposes by a collection of lecturers at Sussex University in the 1980s, designed to be extendable by students -- though not absolute beginners.
The package includes a core that makes use of the Pop-11 GRAMMAR library, used here only in input mode. (Other tutorials use it for generating sentences.) It also uses the Pop-11 2-D interactive graphics package RCLIB.
The package was inspired by the SHRDLU program presented in Terry Winograd's MIT PhD thesis (1971). More information about SHRDLU is available here: http://hci.stanford.edu/~winograd/shrdlu/
RC_BLOCKS is very much simpler than Winograd's program as it uses a very simple grammar and parser, but it does illustrate how a system can use the combination of syntactic, semantic, and 'world' knowledge in understanding potentially ambiguous sentences and also shows what some of the libraries that come with Poplog (and therefore with SimAgent) can do.
The two movies show how the package deals with only two different input sentences, combining knowledge of grammar and a lexicon along with a database of information about the current situation, in order to resolve ambiguities in input sentences.
The package can handle questions and instructions (constrained by a very simple grammar, vocabulary and ontology). However its current resources are much too simple for use in any practical robot, though the general ideas could be useful. The tools used can also be useful for rapid prototyping of a simple but usable natural language interface to a robot (or other machine).
Two formats are provided for each of the two movies, mpeg and webm. The latter is more likely to work on modern machines.
These two demonstrations use only the RCLIB subset of the SimAgent Toolkit, along with the GRAMMAR library and its semantic extension FACETS. They also show how the XVed editor can be used as part of an interface for textual interaction. There is no use of POPRULEBASE or SIM_AGENT, though extensions of the demonstration would benefit from their use. However, the Pop-11 DATABASE package is used.
The program corresponds to this tutorial, originally developed for teaching AI at Sussex University: http://www.cs.bham.ac.uk/research/poplog/teach/msblocks
Additional information about the grammar library, the Pop-11 database package, and the matcher used in the grammar library, can be found in video tutorials available in: http://www.cs.bham.ac.uk/research/projects/poplog/cas-ai/video-tutorials.html
His papers in that period can be found on his publications page. He not only contributed to the design of the SimAgent toolkit and also use it for teaching, and in a multi-agent planning project in collaboration with Jeremy Baxter and colleagues at DERA, Malvern. Brian and Jeremy both made significant contributions, as expert users, to the design of SimAgent, and helped to make it bug-free (insofar as it is!). Unfortunately we have no videos of Brian's multi-level planning program (partly because it used 3D terrain maps with copyright restrictions).
31 Aug 2014 (re-organised, including new table of contents);
28 May 2009; 17 Nov 2013; 27 Aug 2014;
Maintained by Aaron Sloman