The art of comics – Understanding ontologies better?!

Comics always were a part of my life. I still love comics, even though my time for them is scarce. I grew up reading tons of comics at my cousin’s place, which was easily reachable via our backyard. I spent a lot of time there with Batman, Superman, The Fantastic Four, and many more super-heroes. Of course, at that time comics were seen as a very bad influence on impressionable young minds; at least as bad as computer games today, I suppose. As my cousin also drew comics herself I learned already back then a lot about the structure of comics and what makes super-heroes tick. But it took me over 25 years to actually learn some more about comics. Scott McLoud opened my eyes in his book Understanding Comics: The Invisible Art about a lot of things. This book shows and not only tells, as it uses the same medium it talks about: it itself is a comic book.

Reading Understanding Comics I was astonished how many similarities I found between comics and ontologies, i.e., formal representations of the world. Comics and ontologies represent, each in their own way, views of the world. Comics exaggerate physical and mental abilities, distort laws of nature, and portray extreme characters. Granted. But all is grounded in our own experience and would not work without our experience. For example, often action in comic book scenes takes place only in between the panels, i.e., in the mind of the reader. The artist draws the scene before and after the action takes place, and the reader fills in the blanks. And a kid surely fills in the blanks differently than an adult. I was not aware of my own interaction with the comic.

Comics abstract from the world. They are most of the time intentionally not as photo-realistic as they could be. By stripping away details the artist can let the reader focus on what is important. Scott McLoud presents several techniques used in comics, of which I knew only a few. I was not even aware of most of the others even though I would have argued that I have read comics carefully. Read, yes, but, obviously, not studied carefully.

In another chapter, Scott McLoud addresses upcoming comic artists. He presents a six step process of how to become a comic artist. Again, I was reminded of how to become a knowledge engineer. One needs to master a craft as well as have some talent. Any kind of artist has to decide in which form an idea will be expressed, following some school of art (idiom). Then the artist composes the work, leaving some things in and others out. For crafting the art, the necessary skills and knowledge need to be available. Finally, without some production skills the surface of the art work will not be appealing enough to sell the art work as the surface is the first thing a customer will base the buying decision on. Well, how come that ontologies do not sell, one could wonder.

There are a lot more interesting things to say about this book. Time can flow quite differently in comic books. The triangle of reality, picture plane, and language is discussed in detail. And much more …

After reading Understanding Comics I am able to enjoy comics on an additional level, now having more knowledge about the craftsmanship of comic artists.

I now wonder whether and how some of the techniques could be put to use in developing explanation-aware systems. Any suggestions?

Learning to present professionally and effectively

Giving presentations is part of my job, striving for memorable presentations is part of my profession’s as well as my personal ethos.

I started out preparing slides in the typical way, looking at other presentations and trying to figure out how it is professionally done. I assume my presentations then were as boring as those of most others, contributing to death by PowerPoint, by Apple Keynote, or any other slideware. All the tools can be used for putting the audience to sleep without any effort, but the tools are not the source of trouble.

Over the years I found some interesting news feeds, for example Les Posen’s CyberPsych Blog, with—among other topics—insightful analyses of Steve Jobs’ keynote speeches. I started to mimic the style of his slides with a less-is-more-attitude. My presentations got noticably better (according to positive remarks from attendees) but I still did not really know what I was doing.

Last year I began to work on my skills in earnest. A seminar at the University of Mainz set the starting point. A media training for scientists provided me with valuable feedback on my own performance in various situations and on a television screen besides lots of other media related information (see earlier post, in German).

But the most influential source about presentation-related knowledge was and is Garr Reynolds’ blog Presentation Zen. On this blog I found material what I was looking for. Garr Reynolds writes about my personal situation. That is the how I perceive it—and that is, by the way, why I love the Web so much. You can find kindred spirits there and people having similar problems / life situations … well … I get side-tracked—Presentation Zen is a valuable collection every presenter should check out. Fortunately, Garr Reynolds made also a book of it: Presentation Zen: Simple Ideas on Presentation Design and Delivery. It contains lots of helpful hints on improving your presentations. What I like most about this book is that it is case-based. Lots of examples simply show you the better way of presenting ideas.

From the many books he recommends (scroll down a bit on his blog) I found two very interesting and would like to recommend them to you, too:

• Daniel H. Pink’s A Whole New Mind: While Right-Brainers will Rule the Future
• Stephen M. Kosslyn’s Clear and to the Point: Eight Psychological Principles for Compelling PowerPoint Presentations

“Show, don’t tell!” is an important rule in story telling. In a way I got the feeling that Garr Reynolds shows whereas Stephen Kosslyn tells. Garr also got lucky that he could provide all examples in colour whereas Stephen Kosslyn’s book suffers from grayscale examples. The books are not really comparable. I like all the psychological explanations Stephen Kosslyn presents. They are important to deal with complex stuff and helps portioning what you want to convey. You will also find some of this in Presentation Zen, but I think both books could be seen as two sides of a coin.

Much has been written in the last years about the differences of our brain’s left and right hemispheres. Daniel Pink reminds us of our given creativity in A Whole New Mind. Have a look at the “six high-concept, high-touch senses” (see an overview here). The book is, as he says himself somewhere in it, quite US-centered and I do not agree with all of his political views, but it is definitely worth a read.

3rd Workshop on Explanation-aware Computing ExaCt 2008

I am organising another workshop on Explanation-aware Computing ExaCt 2008, supported by my colleagues and friends David B. Leake, Stefan Schulz, and Daniel Bahls. This time ExaCt takes place 21–21 July as part of ECAI 2008 in Patras, Greece.

Here is the main part of the call for papers:

OBJECTIVES

The increasing complexity of current knowledge-based systems requires improved explanation capabilities. During the height of expert systems research many workshops (including at ECAI) addressed the issue of explanation capabilities. However, with the decrease in expert systems research, AI explanation research dwindled as well. Consequently, the time is ripe for renewed investigations of explanation in AI.

Other disciplines such as cognitive science, linguistics, philosophy of science, psychology, and education have investigated explanation as well. They consider varying aspects, making it clear that there are many different views of the nature of explanation and facets of explanation to explore. Within the field of knowledge-based systems, explanations have been considered as an important link between humans and machines. There, their main purpose has been to increase the confidence of the user in the system’s result, by providing evidence of how it was derived. Additional AI research has focused on how computer systems can themselves use explanations, for example to guide learning.

Both within AI systems and in interactive systems, the ability to explain reasoning processes and results can have substantial impact. Current interest in mixed-initiative systems provides a new context in which explanation issues may play a crucial role. When knowledge-based systems are partners in an interactive socio-technical process, with incomplete and changing problem descriptions, communication between human and software systems is a central part. Explanations exchanged between human agents and software agents may play an important role in mixed-initiative problem solving.

This workshop series aims to draw on multiple perspectives on explanation, to examine how explanation can be applied to further the development of robust and dependable systems and to illuminate system processes to increase user acceptance and feeling of control.

If you would like to participate in discussions on this topic or like to receive further information about this workshop you might consider joining the Yahoo!-group http://groups.yahoo.com/group/explanation-research.

Information on explanation research is also collected at http://on-explanation.net.

GOALS AND AUDIENCE

The main goal of the workshop is to bring researchers, scientists from both industry and academics, and representatives from different communities and areas such as those mentioned above, together to study, understand, and explore explanation in IT-applications. In addition to presentations and discussions of invited contributions and invited talks, this workshop will offer organised and open spaces for targeted discussions and creating an interdisciplinary community. Demonstration sessions will provide the opportunity to showcase explanation-enabled/-aware applications.

TOPICS OF INTEREST

Suggested topics for contributions (not restricted on IT views):

• Models for explanations
• Integrating application and explanation knowledge
• Explanation-awareness in applications
• Methodologies for developing explanation-aware systems
• Learning to explain
• Context-aware explanation vs. explanation-aware context
• Confidence and explanations
• Security, trust, and explanation
• Requirements and needs for explanations to support human understanding
• Explanation of complex, autonomous systems
• Co-operative explanation

IMPORTANT DATES

• Submission deadline: April 10, 2008
• Notification of acceptance: May 10, 2008
• Camera-ready versions of papers: May 26, 2008

Read more at the workshop website.

On explanation-awareness

I use the term explanation-aware now for quite a while, but noticed only today that I have not written about it here. Well, it’s about time, isn’t it?

Complex information / knowledge-based systems require more intelligent handling of communication between system and user. The user needs to have the opportunity to ask about unknown concepts or, generally, what is going on in the system. My current research work is aiming at improving system responses by working towards explanation-awareness of such systems. Systems that intend to exhibit explanation-awareness must be more than simple reactive systems.

The basic explanation scenario (see figure) has three participants: user, originator, and explainer. The originator is the problem solver. It achieves the main goal of the system, i.e., providing decision support of some kind. The explainer’s task is to provide the answers to questions the user has about concepts used by the originator or about how the originator came to a conclusion or why it presented some decision. Explainer and originator need to be coupled quite tightly in order to provide detailed explanations, but the explainer also needs to have its own knowledge base, which goes beyond the problem solving knowledge of the orginator.

When the term awareness is used in conjunction with the term explanation it implies consciousness about explanation. When a system exhibits explanation-awareness, it is capable of reasoning about explanations. When we use the word aware we are making a strong statement about the capabilities of the entity described. As being knowledgeable is central to being aware, some kind of reasoning capabilities, or intelligence, is implied. Thus, a computer system that aims at becoming explanation-aware must regard explanations from the knowledge level.

There are lots of open questions to phrase and solve in order to come up with a lightweight framework and a methodology for constructing explanation-aware information systems.