To register to a tutorial, please select it when you will be registering for the main conference.
Both tutorials will take place at UPMC on June 27. The Prosecco tutorial will take place in the morning and the Engagement-reflection model tutorial will take place in the afternoon.
The PROSECCO network is offering 11 scholarship grants to defray the cost of attending the PROSECCO tutorial. Instructions on how to apply can be found here.
Deadline for applications is 19 May, midnight, Paris time
Tutorial #1: The Engagement-Reflection Model
The Engagement-Reflection Model (ER-Model) has been developed to study the creative process using computers. Originally, it was implemented in MEXICA, an agent that develops plots about the ancient inhabitants of what today is México City. However, some variations of the model have been employed to develop agents in areas like interior-design, visual composition, developmental agents, geometry, and so on.
The purpose of this meeting is to introduce the participants into the core ideas behind the ER-Model, contrast such a model with traditional approaches, reflect on the potentialities and limitations of this approach, and then have fun with some of our prototypes: our plot-generator agent known as MEXICA-libre and our visual-composer agent called Tlahcuilo-VC. We expect a very dynamic tutorial with an intensive and productive exchange of ideas between participants.
We hope to gather together an interdisciplinary group of at least 10 participants. Although it would be useful to have some basic knowledge on cognitive science and some experience in programming, these are not essential requirements. Please, bring your own computer.
If you are interested contact:
Responsible: Dr. Rafael Pérez y Pérez
Assistant: Iván Guerrero
The tutorial will take place in the afternoon of Monday the 27th of June. Details coming soon.
Tutorial #2: PROSECCO Tutorial about Computational Creativity
A flavour of Computational Creativity (CC) research will be provided by introductory lectures from Geraint Wiggins (http://www.eecs.qmul.ac.uk/people/view/4932), Hannu Toivonen (http://www.cs.helsinki.fi/u/htoivone/) and Graeme Ritchie (http://homepages.abdn.ac.uk/g.ritchie/pages/). The tutorial is open to all, and should contain something for everyone. However, researchers wanting to start exploring the field, or students starting a research program in a CC related matter, may find it especially useful.
- Characterising Computational Creativity, Geraint A. Wiggins, Queen Mary University of London, UK
- Assessing the Performance of a Creative System, Graeme Ritchie, University of Aberdeen, UK
- Roles of Data Mining and Machine Learning in Computational Creativity, Hannu Toivonen, University of Helsinki, Finland
The PROSECCO network will offer a number of scholarship grants especially aimed at young researchers, of up to €1,000 each, to defray the cost of attending the tutorial. Each grant also includes registration at the ICCC conference, allowing attendees to immerse themselves in Computational Creativity research for the whole week. Details about how to apply will be announce in this page soon.
Characterising Computational Creativity – Download the slides
Geraint A. Wiggins, Queen Mary University of London, UK
I introduce some fundamental notions necessary to describe creative systems, clarifying some concepts presented in and arising from Margaret Boden’s (1990) descriptive hierarchy of creativity, by beginning to formalise the ideas she proposes. The aim is to move towards a model which allows detailed comparison, and hence better understanding, of systems which exhibit behaviour which would be called ‘‘creative’’ in humans.
I demonstrate some simple reasoning about creative behaviour based on the new framework, to show how it might be useful for the analysis and study of creative systems. The same mechanisms potentially allow reflection.
I suggest that Boden’s descriptive framework, once elaborated in detail, is more uniform and more powerful than it first appears.
Assessing the Performance of a Creative System – Download the slides
Graeme Ritchie, University of Aberdeen, UK
When any computer program has been constructed, it is natural to ask: how well does it perform? For a potentially creative computer system, this involves various tasks, such as defining the question more precisely (which is not trivial), and devising workable methods of evaluating the performance of such a system. In making the question more precise, it is essential to consider the goals of the research activity, and how the notion of “creative” is related to other concepts which might be easier to measure directly. Evaluation studies typically rely on ratings by human judges, but this leads to various methodological matters, such as how natural the setting should be, what the judges should be asked to do, and whether comparisons should be made with human-generated items. These matters have attracted much attention within computational creativity over the past 15 years or so, and the ICCC-16 sessions will no doubt include some papers on the topic of evaluation. This talk gives an introductory overview, intended to make it easier see where individual projects and particular debates fit into the wider picture.
Roles of Data Mining and Machine Learning in Computational Creativity – Download the slides
Hannu Toivonen, University of Helsinki, Finland
Data mining and machine learning can be used in a number of ways to help computers learn how to be creative, such as learning to generate new artefacts or to evaluate various qualities of newly generated artefacts. In this tutorial we give an overview of the roles that data mining and machine learning have had and could have in creative systems (but we will not go into the methods).