Artificial Intelligence - Special volume on qualitative reasoning about physical systems
Some philosophical problems from the standpoint of artificial intelligence
Readings in nonmonotonic reasoning
Analogical representations of naive physics
Artificial Intelligence
A new kind of science
Comparative n-gram analysis of whole-genome protein sequences
HLT '02 Proceedings of the second international conference on Human Language Technology Research
Proceedings of the Working Conference on Advanced Visual Interfaces
Interactive Visualization of Network Anomalous Events
ICCS '09 Proceedings of the 9th International Conference on Computational Science: Part I
Automatic extraction of face contours in images and videos
Future Generation Computer Systems
Hi-index | 0.00 |
Understanding and solving biomedical problems requires insight into the complex interactions between the components of biomedical systems by domain and non-domain experts. This is challenging because of the enormous amount of data and knowledge in this domain. Therefore, non-traditional educational tools have been developed such as a biological storytelling system, animations of biomedical processes and concepts, and interactive virtual laboratories. The next-generation problem solving tools need to be more interactive to include users with any background, while remaining sufficiently flexible to target open research problems at any level of abstraction, from the conformational changes of a protein to the interaction of the various biochemical pathways in our body. Here, we present an interactive and visual problem solving environment for the biomedical domain. We designed a biological world model, in which users can explore biological interactions by role-playing ''characters'' such as cells and molecules or as an observer in a ''shielded vessel'', both with the option of networked collaboration between simultaneous users. The system architecture of these ''characters'' contains four main components: (1) bio-behavior is modeled using cellular automata; (2) bio-morphing uses vision-based shape tracking techniques to learn from recordings of real biological dynamics; (3) bio-sensing is based on molecular principles of recognition to identify objects, environmental conditions and progression in a process; (4) bio-dynamics implements mathematical models of cell growth and fluid-dynamic properties of biological solutions. The principles are implemented in a simple world model of the human vascular system and a biomedical problem that involves an infection by Neisseria meningitides where the biological characters are white and red blood cells and Neisseria cells. Our case studies show that the problem solving environment can inspire user's strategic, creative and innovative thinking.