Introduction

One of the joys of living in the 21st Century is that I keep coming across entire areas of research whose existence I never suspected. This month, I write about one of these, Level-of-Detail AI, a technique used to simulate large numbers of background characters in virtual worlds and computer games. There’s an article on good style in AI programming, and, following up an email from one of our readers, a look at the new book On Intelligence. First, two announcements.

Stottler Henke’s teachable search program, Aware

Keith Weinberger from Stottler Henke mailed to tell us about a new product released this year, Aware. Stottler Henke uses AI techniques to solve problems that defy traditional solutions. Aware is a technology that learns the user’s context while they search the Internet, using that context to increase the relevancy of the results. You can learn more about Aware at www.AwareSearch.com/prodinfo.htm. Future plans for Aware are to make it an intelligent research assistant that will perform tasks independently and in parallel with its user’s online research.

www.AwareSearch.com/prodinfo.htm - Product information for Aware.

www.StottlerHenke.com - Stottler Henke’s main site. Its news features some impressive awards and contracts.

Logic Programming Associates article on The Visual Development of Rule-Based Systems

Clive Spenser of Logic Programming Associates (LPA) sent me a link to his feature on The Visual Development of Rule-Based Systems: Part II. The feature, written for PC AI, presents a method of depicting rules using graphically-oriented decision charts, comparing them to knowledge representation using decision tables and decision rules. It illustrates this using LPA’s VisiRule graphical business rules and decision support system; however, the feature is more general, and applies to rules developed with any system.

www.pcai.com/web/45654/4.5.6.7/Visual_Rules.htm - The Visual Development of Rule-Based Systems: Part II, by Clive Spenser.

www.pcai.com/web/V2ER8N/TH.02.52/Visual_Rules.htm - The Visual Development of Rule-Based Systems, also for PC AI, by Charles Langley and Clive Spenser. The later article follows on from this.

www.lpa.co.uk - Logic Programming Associates. There are links to the PC AI article, as well as other work done using LPA products, at www.lpa.co.uk/new_lin.htm. The VisiRule page is www.lpa.co.uk/vsr.htm.

On Intelligence - Palm Pilot creator tackles the neocortex

Torbjörn Wikström mailed me about www.onintelligence.org/, companion site for the new book On Intelligence. The book has attracted much attention, and looking at an excerpt from its prologue, I can see why:

I am crazy about brains. I want to understand how the brain works, not just from a philosophical perspective, not just in a general way, but in a detailed nuts and bolts engineering way. My desire is not only to understand what intelligence is and how the brain works, but how to build machines that work the same way. I want to build truly intelligent machines. …

Our generation has access to a mountain of data about the brain, collected over hundreds of years, and the rate we are gathering more data is accelerating. The United States alone has thousands of neuroscientists. Yet we have no productive theories about what intelligence is or how the brain works as a whole. Most neurobiologists don’t think much about overall theories of the brain because they’re engrossed in doing experiments to collect more data about the brain’s many subsystems. And although legions of computer programmers have tried to make computers intelligent, they have failed. I believe they will continue to fail as long as they keep ignoring the differences between computers and brains.

On Intelligence is written by Jeff Hawkins, the founder of Palm Computing, and Sandra Blakeslee, a New York Times science writer. Hawkins has impressive achievements:

For twenty-five years I have been passionate about mobile computing. In the high-tech world of Silicon Valley, I am known for starting two companies, Palm Computing and Handspring, and as the architect of many handheld computers and cell phones such as the PalmPilot and the Treo.

What’s relevant to this article, however, is that other interest with which I started: Hawkins is crazy about brains. For much of his life, he has tried to understand the nature of intelligence. He explains his ideas in On Intelligence.

Intelligence is prediction, not behaviour

He states his theory in the prologue, in very general terms: AI researchers have tried programming computers to act like humans without first answering what intelligence is. Only when we understand this, and we also understand how the brain thinks, can we build intelligent machines. We have failed because our assumptions have been wrong. The most wrong was that intelligence is defined by behavior:

What is intelligence if it isn’t defined by behavior? The brain uses vast amounts of memory to create a model of the world. Everything you know and have learned is stored in this model. The brain uses this memory-based model to make continuous predictions of future events. It is the ability to make predictions about the future that is the crux of intelligence. I will describe the brain’s predictive ability in depth; it is the core idea in the book.

This predictive intelligence is seated in the neocortex, that walnut-convoluted “rind” which one sees from above and which is the most evolutionarily recent part of the brain. It’s the part that Michael Miller, editor of PC Magazine, describes in his review of On Intelligence as “a napkin six business cards thick with 30 trillion synapses”. One of its functions is perception - this may help locate it, since as well as the visual and auditory cortices, it includes the somatosensory homunculus. This is the distorted mannequin with huge lips and hands that one sees in pictures showing which areas of the brain respond to sensations in which parts of the body.

According to Hawkins, all parts of the neocortex work on the same principles. The key to understanding the neocortex is understanding these common principles and, in particular, its hierarchical structure.

All the above is very unexplicit. Perhaps I missed some links, but I could find nothing more detailed on the site. How do these grand claims fare when we try to embody them in weights, nodes and update-dynamics, in a clunky, irritating, bug-ridden, real-world neural net? Has Hawkins embodied his theory in a computational model? If so, how does it work, how well does it predict psychological phenomena, and how does it improve on previous researchers' models? The nearest the site comes to answering these questions is its additional resources page, which links to Hawkins’s 1986 PhD proposal An Investigation of Adaptive Behavior Towards a Theory of Neocortical Function, and to coworker Dileep George’s reports and simulations on visual invariance - how our visual system is able to recognize the same object from different points of view.

The neocortex as an unsupervised learner

Such questions are not answered either by the reviews linked from the site. However, I found others that told me more. Matt Keller’s Weblog gives a concise summary:

Hawkins’s theory is really a theory of how the neocortex works; the author largely dismisses the contributions of the ‘primitive’ brain to intelligence. The gist of the theory is that the neocortex implements a hierarchical memory-prediction machine; it tries to predict the future based on the patterns it has stored (memory). As sensory input flows up into the various layers of the neocortex, the neocortex tries to match the input to previously seen patterns. Matched patterns at lower levels create abstractions to be used by higher levels. The flow of information proceeds not just up, but down the hierarchy: when a pattern matches, lower levels are biased about what to expect next. Hawkins makes heavy use of the anatomical fact that there are more ‘down’ pathways than ‘up’ pathways to support this argument. Information is processed into higher and higher abstractions as the signals propagate upward, predictive information trickles downward as a result.

Going further, I found a review by Peter Dayan of the Gatsby Computational Neuroscience Unit at University College London. He starts with this titillating paragraph:

Is Michael Moore liberal America’s Rush Limbaugh? If so, is he filling a much needed, or a much lamented, gap in turning issues that are really cast in pastel shades into Day-Glo relief? In this hale monograph, Jeff Hawkins (rendered by Sandra Blakeslee) plays exactly this role for theoretical neuroscience. As a pastel practitioner myself, but furtively sharing many of Hawkins' prejudices and hunches about computational modelling in neuroscience, I am caught between commendation and consternation.

To summarise Dayan’s review - which has useful links to past work, and looks to a non-neuroscientist like me to be an excellent starting point if you’re interested in cortical modelling - Hawkins models the neocortex as unsupervised learning; that is, it learns without being told what structure and patterns exist in its inputs, extracting them by using general statistical properties. From these, to quote from On Intelligence:

To make predictions of future events, your neocortex has to store sequences of patterns. To recall appropriate memories, it has to retrieve patterns by their similarity to past patterns (auto-associative recall). And finally, memories have to be stored in an invariant form so that the knowledge of past events can be applied to new situations that are similar but not identical to the past.

Criticisms of Hawkins’s model

Noting that Hawkins is not the first researcher to model the cortex, Dayan compares his model with previous work. One of his criticisms is that Hawkins neglects probability theory and statistics. As researchers developed unsupervised learning, they increasingly realised that rather than devising their own ad-hoc methods for recognising structure in inputs, it was better to use the methods and mathematics of probability theory and statistics. Compared with these, Hawkins’s auto-associative recall is theoretically and practically restricted. (Regardless of how it applies to Hawkins, this has been a common theme in AI. Probability theorists and statisticians have often complained that neural-network researchers ignore their subject. One sign their research was maturing was when books started appearing that brought the two together, enabling the long-developed mathematics of statistics to be applied to neural nets.)

A different kind of problem with a model that ignores everything but the neocortex is that emotion is likely to be important to reasoning, for example in evaluating desirable versus undesirable outcomes. Since brain structures outside the neocortex - the amygdala, for example - contribute to our emotions, no model of intelligence should neglect them.

Yet another difficulty is the “carpet problem”. For almost all tasks other than haggling in a tourist-trap souk, the complicated visual texture of carpets is likely to be irrelevant. It would be a waste of resources for our brains to try representing it; and evolution is good at ensuring that brains don’t waste resources on computations that don’t matter. (It has been suggested that this is why we lose the childhood ability to easily learn languages; it was unnecessary once we’d learnt our parents' language, and the neural circuitry involved could be better redeployed for something else.) Carpets are just one example; in general, it’s likely that our brains will have elaborate methods for identifying and throwing away patterns that are not relevant to a particular task. On Intelligence ignores this.

Dayan’s review is not all critical: he describes with favour Hawkins’s solution to a point mentioned earlier: how the neocortex uses patterns already encountered to tell areas nearer the sensory inputs what kinds of information they should expect next:

This part has interesting suggestions, such as a neat solution for a persistent dilemma for proponents of hierarchical models. The battle comes between cases in which information in a higher cortical area, acting as prior information, boosts activities in a lower cortical area, and cases of predictive coding, in which the higher cortical area informs the lower cortical area about what it already knows and therefore suppresses the information that the lower area would otherwise just repeat up the hierarchy. The proposed solution involves the invention (or rather prediction) of two different sorts of neurons in a particular layer of cortex.

Quite apart from brain modelling, On Intelligence contains some autobiography. As Dayan says,

The history of modern computing is very brief and (at least judging by the sales) very glorious, and this story is most entertaining. Don’t miss the wonderfully faux naive letter from Hawkins to Gordon Moore asking, in 1980, to set up a research group within Intel devoted to the brain.

Web pages and postings demonstrate that On Intelligence has inspired lots of people. The best advice seems to be: read it; enjoy the biographical parts; treat Hawkins’s theory with caution and check it against expert criticism; be inspired about computing and AI. Always be inspired.

On Intelligence, by Jeff Hawkins and Sandra Blakeslee. Available as an e-book as well as on paper.

www.onintelligence.org/ - The On Intelligence site. This feature’s opening quote comes from the excerpt at www.onintelligence.org/excerpt.php. The “Additional Resources” page is at www.onintelligence.org/resources.php, and links to Hawkins’s 1986 paper An Investigation of Adaptive Behavior Towards a Theory of Neocortical Function.

www.pencomputing.com/palm/Pen33/hawkins1.html - Jeff Hawkins: The man who almost single-handedly revived the handheld computer industry, by Shawn Barnett, Pen Computing. Three-page account of Hawkins’s life and work, from the crazy air-cushion craft he helped build on the family boatyard in the mid-1960s, through the GRiDPAD handheld computer and his programming language GRiDTask, to his work at Handspring.

www.technologyreview.com/articles/99/07/qa0799.asp - That’s Not How My Brain Works… Interview with Hawkins for MIT Technology Review, by Charles Mann.

www.stanford.edu/~dil/invariance/ - Dileep George’s page on his visual-invariance research.

www.rni.org/ - The Redwood Neuroscience Institute, founded by Hawkins.

www.pcmag.com/article2/0,1759,1646196,00.asp - Video: Taking It with You. Michael Miller, editor of PC Magazine, reviews On Intelligence.

www.littleredbat.net/mk/blog/archive/2005/3/11/ - Matt Keller’s concise account of On Intelligence.

www.pubmedcentral.nih.gov/articlerender.fcgi?artid=526780 - Palmistry. Peter Dayan, Gatsby Computational Neuroscience Unit, University College London, reviews On Intelligence.

nba.uth.tmc.edu/homepage/eagleman/10Q/book/Ch6_Intelligence_EaglemanChurchland_10Questions.pdf - Draft Chapter 6 of the forthcoming book Ten Unsolved Questions of Neuroscience by David Eagleman and Patricia Churchland. The section on Pro Prediction discusses Hawkins’s model. An interesting point is made earlier, under Stronger association, that too-strong associative learning is not a good thing - it leans towards schizophrenia.

www.le.ac.uk/bs/resources/bs3033/cortexlecture.pdf - Functional Organization of the Neocortex, from Leicester University School of Biological Sciences. Twenty-four slides showing pictures and diagrams of the neocortex, its divisions and functions, and its neurological makeup, including the cortical columns. The first slide points out the cortical areas which respond to beer, football, TV, curry, and email.

www.macalester.edu/~psych/whathap/UBNRP/Phantom/homunculus.html - He Ain’t No Marvel Comic. Diagram and description of the somatosensory homunculus, from Macalester College Minnesota. The page also links to theories on the phantom limb phenomenon mentioned below.

peace.saumag.edu/faculty/Kardas/Courses/GPWeiten/C3BioBases/Cerebrum.html - Short page on the neocortex, which I like for the sentences:

The analysis and interpretation of vision is extremely complex and accounts for the largest percentage of the brains’s activity. If you were to receive a monthly bill from your brain, the largest portion of that bill would be for vision.

www.edge.org/3rd_culture/bios/blakeslee.html - Edge page on Sandra Blakeslee and her other books. One of these, coauthored with neuroscientist Vilayanur Ramachandran, is Phantoms in the Brain. I recommend this book. It probes the brain by examining cases where consciousness goes wrong: the phantom limb experienced by amputees; people who are convinced that their parents have been replaced by identical-looking impostors; the man who hallucinated cartoons and - if I remember correctly - could see a circus in the palm of his hand.

www.bbc.co.uk/radio4/reith2003/ - Ramachandran’s The Emerging Mind, the BBC Reith Lectures for 2003. Links to transcripts and audio versions of the lectures.

Level-of-Detail Artificial Intelligence

In one of my books - probably a Gary Larson Farside collection - there’s a cartoon entitled The Fake McCoys. It shows Mom, Pop and two kids. A fake Mom, Pop and two kids: wooden cutouts, with nothing behind but dust and tumbleweeds, like the plywood street frontages in an old-fashioned Western. When making a film, you need build no more than the viewer will see; and if your script is sufficiently wooden, you can save a lot of expense by also letting your actors be wooden.

This idea doesn’t only apply to films. Books on cartooning teach that you should render the background in less detail than the middleground, and the middleground in less detail than the foreground. Similarly, in a computer game, to render a dragon that’s far away and occupies only a few pixels, you need less detail than if the player is staring down the dragon’s flameducts, its mouth gaping over the entire screen. This - adapting an image’s resolution to the detail needed - is “Level-of-Detail graphics”. What I discovered by accident when browsing through Brian Schwab’s AI Game Engine Programming is that there is a related field, “Level-of-Detail AI”, or LOD AI. This tries to economise on the resources needed to implement artificially-intelligent characters in computer games, using a lot of AI where its effects are most needed and less where they’re less important. It must be particularly useful for a game like Elixir Studio’s Republic, which aims to simulate an entire city and could have hundreds of non-player characters interacting with the player during a game.

Although details can be complicated, LOD graphics is conceptually simple (and I suspect was around under such names as “multiple resolution meshes” before the phrase “Level-of-Detail graphics” was coined). Resolution is a well-understood concept, and easy to handle mathematically, being a numeric, continuously variable, quantity. It’s clear what it means for resolution to depend on the detail needed in an image, varying continuously with, say, its distance from the viewer. It’s not so clear what it means to vary the intelligence of a non-player character with their importance to the player.

So far, there seems not to be very much about LOD AI on the Web. The best reference I found (apart from one which required a paid subscription) is the paper Men Behaving Appropriately: Integrating the Role Passing Technique into the ALOHA System. ALOHA stands for Adaptive Level of Detail of Human Animation; its aim is to animate and render virtual humans in real-time. It uses LOD to reduce the accuracy of the simulated characters when the viewer is unlikely to notice. As the authors say, “if a user views a crowd from a distance, there is no need to have computationally expensive models and sophisticated animations, as the user will not perceive the difference. However if the user zooms up closer, the realism of the virtual human’s model and its motion should be improved”.

The paper starts with three types of LOD: geometric, animation, and gesture and conversational. Geometric LOD is LOD graphics, applied to a character’s skin. Animation LOD allows the game engine to request different levels of detail for animation: to decide how to resolve joint angles with inverse kinematics, to decide how many frames a movement should receive, and whether to simulate some motion using a simple kinematic interpolation or a dynamic technique. The most important characters get the smoothest and most realistic animation.

The third item, gesture and conversational LOD, is conceptually similar to animation LOD, although its implementation is probably very different, in that it uses the Behaviour Expression Animation Toolkit developed by the Gesture and Narrative Language group at the MIT Media Lab. This takes text to be spoken by a character, and computes nonverbal behaviours - gestures and facial expressions - together with synthesised speech, including intonation and pauses. The authors suggest incorporating an LOD version of the toolkit into ALOHA. This would mean that the LOD resolver - that part of the system which decides on the detail necessary for a particular object - could render realistic social interaction between important characters, using a lower level of detail for characters socialising in the background.

Impressive though these techniques are, they’re not what many people would call AI. However, after introducing them, the authors go on to something more conventionally AI-ish. Imagine a game where the player is spending a lot of time moving in and out of a bar. To add realism, the game’s designers might populate it with background characters: the darts players; the young couple having a bit of nookie in a corner; the maudlin drunks. To make them act realistically, each character could be made to obey a preset script whenever the player enters the bar. A script can be implemented in various ways: as rules, a finite-state machine, or whatever. As it runs, it will call for the character it controls to perform various actions: throwing a dart, chalking up a score, getting another beer. The symbols representing these actions can be fed to the animation and gesture-and-conversation systems, which can then display the appropriate movements, appropriately synchronised.

Although this may look realistic the first time the player enters the bar, it will look completely unrealistic on future entrances, because the background characters will act exactly the same way each time. To overcome this, say the authors of Men Behaving Appropriately, such background characters need to be made persistent: modelled all the time at least to some extent, regardless of their location relative to that of the player. But - to save on computation - they should be modelled in less detail when not visible to the player. This is done by splitting the characters' AI into separate abilities, one ability for each kind of situation the character will encounter. Thus we might have a bartending ability, a darts-playing ability, a café-visiting ability, a shopping-at-newsagents ability, and so on.

The authors call these abilities “roles”. The game engine only activates a role when the character to which it belongs gets placed in the appropriate situation - thus each character’s bartending role is inactive except when the character finds themselves bartending. The authors call this “role-passing”. Roles have memory, so that information persists from one invocation of a role to the next, otherwise the technique would be no use. But there’s no need to pass information between different roles, so resources will only ever be needed for a small fraction of any background character’s total intelligence. As the authors say,

The main advantage of role-passing is the simplicity it lends to populating a virtual world with agents. Placing agents within a novel situation involves simply defining a new role. This eases some of the complications involved in attempting to design very general agents capable of behaving realistically in many situations and avoids having to write completely separate agents for different roles within a single scene. For this kind of LOD AI to work, one probably needs to craft the virtual world carefully, so that the abilities needed to cope with different situations can be made distinct from one another.

In Men Behaving Appropriately, roles are implemented as motivation networks. The idea will be familar to students of animal behaviour: animal psychology is often modelled in terms of motivations like “seek food”, “seek shelter”, “eat”, “sleep”, whose levels increase over time (usually as a function of phsyiology and sensory input) until one motivation exceeds a threshold and triggers appropriate behaviour. Men Behaving Appropriately give a motivation network for a drinker visiting a bar; the motivations are “get drink”, “sit down” and “chat”.

Roles and motivation networks are only one way to implement LOD AI. There are many other possible implementations; with them, games can be populated with many more actors than otherwise possible, and so enhance the illusion of reality.

AI Game Engine Programming by Brian Schwab, published by Charles River Media. The publisher’s description of the book is at www.charlesriver.com/Books/BookDetail.aspx?productID=86952.

www.stefan-krause.com/ - OpenGL LOD Demo, by Stefan Krause. Shows LOD graphics in action, with pictures of a fighter and pilot rendered with various triangle counts.

www.devmaster.net/articles/graphics_alg/ - Advanced Graphics Algorithms. Henri Hakl’s page, with information about some LOD graphics techniques. www.cs.tcd.ie/publications/tech-reports/reports.02/TCD-CS-2002-12.pdf - Men Behaving Appropriately: Integrating the Role Passing Technique into the ALOHA System, by Brian MacNamee, Simon Dobbyn, Padraig Cunningham, and Carol O’Sullivan, Trinity College Dublin.

www.naccq.ac.nz/conference04/proceedings_03/pdf/291.pdf - Modelling layers of artificial intelligence within a virtual world, by John Jamieson, Eastern Institute of Technology, New Zealand.

www.bcs-sgai.org/ai2004/appstream_abstract.htm - Artificial Intelligence for Computer Games, by Peter Astheimer, University of Abertay Dundee. One-page abstract for a keynote lecture, listing some areas - LOD included - where AI can improve games.

www.aiwisdom.com/bygenre_rpg.html - AIWisdom.com’s page on adventure and role-playing games. Links to various books on game AI, including a chapter by Mark Brockington of BioWare in AI Game Programming Wisdom:

With thousands of objects demanding AI time slices in Neverwinter Nights, it would be difficult to satisfy all creatures and maintain a playable frame rate. The level-of-detail AI schemes used allowed the game to achieve the perception of thousands of actors thinking simultaneously. The article discusses how to subdivide your game objects into categories, and how certain time-intensive actions (such as pathfinding and combat) can be reduced to make more efficient use of the time available to AI.

ai.eecs.umich.edu/people/laird/gamesresearch.html - Links to papers on AI and computer games research, by John Laird, University of Michigan AI Laboratory. Looks like a useful resource on games AI in general, and includes Laird’s own work on Haunt 2, an adventure-style game where human-level AI characters really make a difference. I don’t know whether any of the papers mention LOD.

www.cse.lehigh.edu/~munoz/CSE497/classes/Patrick1.ppt - Slide presentation on Multi-tiered AI, by Patrick Schmid. Describes an RTS (Real-Time Strategy) game, which implements differing levels of AI corresponding to units of an army’s organisational structure: soldier AI, squad AI, platoon AI, and so on up to army AI. It’s a different use of levels of detail from those I’ve discussed, but an interesting presentation, with good screen shots, and nice examples of why the game needs different levels of terrain mapping.

www.robotwisdom.com/ai/stories.html - A page on Jorn Barger’s “fractal thickets”, his idiosyncratic version of Schank-style scripts for interactive fiction. Fractal data-structures ought to have some uses in modelling events at different levels of detail - this is the only attempt I’ve found. There’s more on it at www.robotwisdom.com/ai/thicketfaq.html.

www.acmqueue.org/modules.php?name=Content&pa=showpage&pid=117 - An “ACM Queue” short feature on game AI by Alexander Nareyek of Carnegie Mellon, looking at techniques such as A* search and finite-state machines. Links to other features such as *Being a jerk may not be against game rules, but developers should do more to stop it*. ACM Queue have some tempting content, but the design is distracting: page-navigation links are buried amongst the undergrowth, and animated images flicker away in one’s peripheral vision. The Association For Computing Machinery, of all people, should be able to design a more ergonomic site. A PDF version of what looks like the same feature is at www.ai-center.com/publications/nareyek-acmqueue04.pdf.

www.elixir-studios.co.uk/nonflash/republic/republic.htm - Elixir Studio’s Republic page

Tennyson and Popular Misapprehensions of Technology

In his poem Locksley Hall, Tennyson wrote

Let the great world spin for ever down the ringing grooves of change. Using the railways as a metaphor for the driving force of technological change, he had failed to realise that trains run on rails, not in grooves. Can you think of similarly succinct misunderstandings of AI and computing?

A Handful of Style Guides

Googling for Prolog utilities the other day, I came across a Prolog style guide by Michael Covington, author of Prolog Programming in Depth and Natural Language Processing for Prolog Programmers. I thought it would be interesting to see what style guides I could find for the other AI languages. Not just mainstream languages like Lisp, but relatively obscure ones such as OPS5, SOAR, and Poplog. What’s the best way to structure an OPS5 knowledge base, which can contain hundreds or thousands of forward-chaining rules but has no module system? How can you best decompose a problem into SOAR’s multiple problem spaces? How should you write for Poplog, which allows you to mix Pop-11, Lisp, Prolog, ML and the SimAgent production-rule language in a single source file, gives you access to the call-stack, and provides closures as first-class values?

As it turned out, I found almost nothing outside Prolog and Lisp - it surprised me not to find guides by expert-systems implementors on good practice for their knowledge-representation languages - although the Cyc Style Guide for #$comments gives good advice on describing complicated predicates in English, as well as offering an intriguing view of what can be written in the Cyc ontology language:

(#$likesObject AGT OBJ) means that when the sentient agent AGT is interacting in some way with OBJ, that agent feels some measure of #$Enjoyment --- that is, (#$feelsEmotion AGT #$Enjoyment). The kinds of interactions that produce #$Enjoyment depend largely on what kind of thing OBJ is. Thus, `Joe likes the Mona Lisa' implies that Joe feels #$Enjoyment when viewing the Mona Lisa. But `Joe likes pizza' implies that Joe feels #$Enjoyment when eating that kind of food. Note: There are some specialized predicates of #$likesObject that give more information about the kind of interaction between AGT and OBJ that results in #$Enjoyment; see, e.g., #$likesSensorially and #$likesAsFriend.

and

Note that an application of #$GroupFn denotes a _collection_ that has groups as instances, rather than an individual group. For example, (#$GroupFn #$BallisticMissile) denotes the collection of all groups of ballistic missiles, which includes Russia's ballistic missiles, China's ballistic missiles, the US's ballistic missiles, etc.

However, I did find some good advice, not only on Prolog and Lisp, but also on human-computer interaction and writing. Let’s start with Prolog.

Prolog

The only guide I found for Prolog was Michael Covington’s Some Coding Guidelines for Prolog. This is a draft guide (dated 2002), and Covington invites comments. Some of his advice applies to any programming language - for example, avoid embedding magic numbers in code, and give informative error messages:

mysort/3: 'x(y,z,w)' cannot be sorted because it is not a list.

rather than

error: illegal data, wrong type

Other, Prolog-specific, advice includes good use of the cut, layout of predicate definitions, and when to use the if-then-else structure (a -> b ; c) (answer: avoid it, because it’s an un-Prolog-like way of thinking).

Lisp

John Foderaro of Franz Lisp has written a short style guide, Lisp Coding Standards v1.0. A big difference between Lisp and Prolog is that Lisp has far fewer visual cues. Control structures don’t stand out from the rest of the code because they share the same bracketed prefix structure as everything else. Hence, says Foderaro,

I’ve found that the key to readability in Lisp functions is an obvious structure or framework to the code. With a glance you should be able to see where objects are bound, iteration is done and most importantly where conditional branching is done. The conditionals are the most important since this is where the program is deciding what to do next.

To help with conditionals, Foderaro defines an if* macro, (if* a then b else c). The keywords make it more immediately noticeable as a conditional. Moreover, if* can be used with any number of conditions and consequents, making it unnecessary to change from when to if to cond as you make your conditional more complicated.

More comprehensive than Foderaro’s short guide is the Tutorial on Good Lisp Programming Style by Peter Norvig and Kent Pitman. Norvig has written AI: A Modern Approach and Paradigms of AI Programming, which a quote on his site describes as “possibly the best hardcore programming book ever”. It’s therefore not surprising that the tutorial is packed with good advice. Much, obviously, is language-dependent - good use of unwind-protect, defsystem, and handler-case, to pick a few at random. However, this doesn’t mean only Lisp programmers should read the tutorial. Many sections - error messages, data abstraction, algorithm design (describe the algorithm in English, write the code, translate this back into English, compare with the original) - apply to any language.

There are some nice quotes, this warning to managers by Butler Lampson amongst them:

Some people are good programmers because they can handle many more details than most people. But there are a lot of disadvantages in selecting programmers for that reason - it can result in programs no one else can maintain.

There is also a recommendation to “Worry less about what to believe and more about why. Know where your “Style Rules” come from”:

  • Religion, Good vs. Evil “This way is better.”
  • Philosophy “This is consistent with other things.”
  • Robustness, Liability, Safety, Ethics “I’ll put in redundant checks to avoid something horrible.”
  • Legality “Our lawyers say do it this way.”
  • Personality, Opinion “I like it this way.”
  • Compatibility “Another tool expects this way.”
  • Portability “Other compilers prefer this way.”
  • Cooperation, Convention “It has to be done some uniform way, so we agreed on this one.”
  • Habit, Tradition “We’ve always done it this way.”
  • Ability “My programmers aren’t sophisticated enough.”
  • Memory “Knowing how I would do it means I don’t have to remember how I did do it.”
  • Superstition “I’m scared to do it differently.”
  • Practicality “This makes other things easier.”

And finally, if this Newsletter arrived in your email an hour later then normal, it’s because I’ve just been for a walk in the woods above where I’m staying. In doing so, I was merely obeying another piece of advice in the guide, this time from John Page:

Also, while you’re working hard on a complicated program, it’s important to exercise. The lack of physical exercise does most programmers in. It causes a loss of mental acuity.

How to name things

Both Covington and Norvig devote a lot of space to good naming. For example, Covington prescribes which parts of speech - verb phrase, noun phrase, adjective - to make predicate names, and how to construct names which make the order of the predicate’s arguments obvious. He also recommends making names pronounceable, and remembering that we remember pronunciations, not spellings. Do not, as one regrettable program did, have menutwo, menutoo, menu2, and mneu2 (this last was probably accidental). And:

Do not mix up to, two, and too. At one time it was fashionable to abbreviate to as 2, thereby saving one character. That’s how computer programs got names such as afm2tfm (for “AFM-to-TFM,” a TEX utility) and DOS’s exe2bin. However, this practice creates too much confusion. Remembering how to spell words correctly is hard enough; now you ask me to remember your creative misspellings too? Spellings like l8tr (or l8r?) and w1r3d do not facilitate communication; they just make the reader suspect that you are still in high school.

An interesting study in the sociology of programming would be to investigate whether text-messaging has made such misspellings more common.

How to advance by not thinking

Good naming makes programs easier to read, but I believe there’s another reason to have comprehensive naming rules, one I first came across in a quote from philosopher Alfred North Whitehead:

By relieving the brain of all unnecessary work, a good notation sets it free to concentrate on more advanced problems, and, in effect, increases the mental power of the race. Before the introduction of the Arabic notation, multiplication was difficult, and the division even of integers called into play the highest mathematical faculties. Probably nothing in the modern world would have more astonished a Greek mathematician than to learn that … a large proportion of the population of Western Europe could perform the operation of division for the largest numbers. This fact would have seemed to him a sheer impossibility … Our modern power of easy reckoning with decimal fractions is the almost miraculous result of the gradual discovery of a perfect notation. […] By the aid of symbolism, we can make transitions in reasoning almost mechanically, by the eye, which otherwise would call into play the higher faculties of the brain. […] It is a profoundly erroneous truism, repeated by all copy-books and by eminent people when they are making speeches, that we should cultivate the habit of thinking of what we are doing. The precise opposite is the case.

What brought the quote to mind was Covington’s advice to name auxiliary predicates by appending _aux or _x to the name of the predicate calling them. (He says that although his books contain many examples of _aux, _x is more concise and lends itself to making sequences of names as in _xx, _xxx and so on.) The point is that by obeying such advice, you save your mental energy for more important decisions. As Whitehead continues:

Civilisation advances by extending the number of important operations which we can perform without thinking about them. Operations of thought are like cavalry charges in a battle - they are strictly limited in number, they require fresh horses, and must only be made at decisive moments.

How not to name things

One naming style I shall not be using is Hungarian notation, so-called partly because its inventor, Charles Simonyi, was Hungarian, and partly as an allusion to Polish notation. It’s important to strike a balance between names that say too little, and names that try to say far too much. Hungarian-style names have to contain a tag indicating their base type, a prefix saying whether the name is a pointer, array, or other derived type, and a qualifier giving other information, such as whether the item named is temporary or permanent. Although these components avoid the need to search for comments and type-declarations, I find they obscure the rest of the name. As Bill Davis says in Hungarian Notation - The Good, The Bad and The Ugly they also work against data abstraction, since the type tag has to be changed whenever the variable’s type is. These are personal views of course; you may like Hungarian notation.

Here is some more advice on naming. Read it carefully:

Use abbreviations wherever possible, particularly where there would be space enough for the complete term. Why? Because abbreviations make an application look more professional, particularly if you create abbreviations that are new or replace commonly used ones.

Example: Use abbreviations for field labels, column headings, button texts even if space restrictions do not require this.

Examples: Use “dat.” instead of “date,” “TolKy” instead of “Tolerance Key,” “NxOb” instead of “Next Object,” and many more…

This comes from a spoof guide to user-interface design, Gerd Waloszek’s Golden Rules for Bad User Interfaces. However, it appears on a serious Web site, the SAP Design Guild. As an example of bad practice, it applies as much to the choice of names in programs as to names in user-interface components.

Human-computer interaction

The Golden Rules bring me to human-computer interaction. This doesn’t just apply to AI programming, but it’s as important there as elsewhere. One HCI-design guide that I’ve found useful is Sun’s Java Look and Feel Guidelines. Although written for Java programmers, most of the advice applies to any graphical user interface. Some, such as when to capitalise words in window titles and other components, may seem pernickety, but it’s surprising how messy applications can look when they’re inconsistent about these matters.

Another, found completely by accident, is the U.S. Army Weapon Systems Human-Computer Interface Style Guide. Although the average AI Expert reader is unlikely to be designing head-up displays or worrying about how to minimise the risk of mid-air collisions and unintercepted missiles, the guide contains a wealth of advice which would apply as much - say - to electronic bus-arrival indicators and touch-screen jukeboxes as it does to laptops and desktops. General advice includes minimising the use of colour except when it enhances performance (something many Web designers have never learnt), minimising the need for users to switch visual focus between windows, and minimising their need to adjust window positions and sizes. More specific advice includes the calculation of appropriate character sizes from a user’s viewing distance, and the best size of touch zone to place around touch-screen controls.

Writing well, and the Gettysburg Power Point Presentation

Programmers need to write. Not just internal comments, but also external documentation such as user manuals and on-line help, as well as promotional copy: product press releases, funding applications, and articles for AI Expert… Good writing is hard, but becomes easier if you modularise it. Peter Norvig explains how in his How to Write More Clearly, Think More Clearly, and Learn Difficult Material More Easily.

Finally, we often need not just to write about our programs, but also to get up in front of an audience and speak about them. Once more from Norvig, and reinforced by graphic design professor Edward Tufte’s feature PowerPoint Is Evil, here’s an example of how not to do so, The Gettysburg Powerpoint Presentation:

Good morning. Just a second while I get this connection to work. Do I press this button here? Function-F7? No, that’s not right. Hmmm. Maybe I’ll have to reboot. Hold on a minute. Um, my name is Abe Lincoln and I’m your president. While we’re waiting, I want to thank Judge David Wills, chairman of the committee supervising the dedication of the Gettysburg cemetery. It’s great to be here, Dave, and you and the committee are doing a great job. Gee, sometimes this new technology does have glitches, but we couldn’t live without it, could we? Oh - is it ready? OK, here we go:

www.ai.uga.edu/mc/plcoding.pdf - Some Coding Guidelines for Prolog by Michael Covington, Artificial Intelligence Center, University of Georgia.

www.franz.com/~jkf/coding_standards.html - Lisp Coding Standards v1.0, by John Foderaro. The page has links to the source and documentation for his if* macro.

www.norvig.com/luv-slides.ps - Tutorial on Good Lisp Programming Style, by Peter Norvig and Kent Pitman.

www.worldwidewords.org/articles/hungary.htm - A page about Hungarian notation for Michael Quinion’s World Wide Words site.

ootips.org/hungarian-notation.html - Hungarian Notation - The Good, The Bad and The Ugly.

www.darkweb.com/~beng/exchange/book/codestd.htm - An example guide to Hungarian coding, for the book Developing Applications for Microsoft® Exchange with C++.

www-users.cs.york.ac.uk/~susan/cyc/q/quotes.htm - This page of quotations, by Susan Stepney, University of York, contains the Alfred North Whitehead quote on notation.

www.cyc.com/cycdoc/ref/style-guide.html - Style guide for #$comments in Cyc.

java.sun.com/products/jlf/ed2/book/ - Java Look and Feel Guidelines, 2nd edition.

www.deepsloweasy.com/HFE%20resources/Army%20WS-HCI%20Style%20Guide%201999.pdf - U.S. Army Weapon Systems Human-Computer Interface Style Guide.

www.sapdesignguild.org/community/design/golden_rules.asp - Golden Rules for Bad User Interfaces, by Gerd Waloszek.

www.ai.uga.edu/mc/WriteThinkLearn_files/frame.htm and www.ai.uga.edu/mc/WriteThinkLearn.pdf - How to Write More Clearly, Think More Clearly, and Learn Difficult Material More Easily, by Peter Norvig.

www.wired.com/wired/archive/11.09/ppt2.html - PowerPoint Is Evil. Power Corrupts. PowerPoint Corrupts Absolutely. Wired feature by Edward Tufte, professor emeritus of political science, computer science and statistics, and graphic design at Yale. Just about anything Tufte writes on visual presentation of data is worth reading.

www.norvig.com/Gettysburg/ - The Gettysburg Powerpoint Presentation by Peter Norvig.

Until next month.