C4.5: programs for machine learning
C4.5: programs for machine learning
Maintaining knowledge about temporal intervals
Communications of the ACM
Training for Crisis Decision-Making: Psychological Issues and Computer-Based Solutions
Journal of Management Information Systems
Gender-Specific Approaches to Developing Emotionally Intelligent Learning Companions
IEEE Intelligent Systems
COMET: A Collaborative Tutoring System for Medical Problem-Based Learning
IEEE Intelligent Systems
Planning and scheduling in an e-learning environment. A constraint-programming-based approach
Engineering Applications of Artificial Intelligence
Planning with sharable resource constraints
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Automatic generation of temporal planning domains for e-learning problems
Journal of Scheduling
Modeling users of crisis training environments by integrating psychological and physiological data
IEA/AIE'11 Proceedings of the 24th international conference on Industrial engineering and other applications of applied intelligent systems conference on Modern approaches in applied intelligence - Volume Part II
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The human ability to take the right decisions is very important in real world critical situations. An interesting problem always worth being investigated concerns how to teach decision making skills to humans. The real nature of taking decisions is extremely difficult to describe in detail and, as a consequence, training it according to fixed protocols is also challenging. This is because it comes out as a combination of natural talent, competence from previous experience, ability to quick reasoning, leadership, resilience to stress, and so on. We have addressed this problem while building a new learning environment to train crisis decision makers. The environment, called Pandora, is grounded on Artificial Intelligence planning techniques known as ''timeline-based''. This technology is used to create and manipulate segments of lesson's content over time. Planning a lesson corresponds to logically organize events over time that are then rendered in front of trainees during the lesson's actual enactment. This paper shows how the machinery of continuous plan adaptation is functional to create variety and novelty in the lessons thus engaging the trainees during the teaching interaction. In particular, it shows the different uses of plan adaptation to take into account the basic reactivity of the trainees, the background deductions from user modeling, and the mixed-initiative interactions guided by the trainer.