Plan recognition for exploratory domains using interleaved temporal search

In exploratory domains, agents' actions map onto logs of behavior that include switching between activities, extraneous actions, and mistakes. These aspects create a challenging plan recognition problem. This paper presents a new algorithm for inferring students' activities in exploratory domains that is evaluated empirically using a new type of flexible and open-ended educational software for science education. Such software has been shown to provide a rich educational environment for students, but challenge teachers to keep track of students' progress and to assess their performance. The algorithm decomposes students complete interaction histories to create hierarchies of interdependent tasks that describe their activities using the software. It matches students' actions to a predefined grammar in a way that reflects that students solve problems in a modular fashion but may still interleave between their activities. The algorithm was empirically evaluated on peoples interaction with two separate software systems for simulating a chemistry laboratory and for statistics education. It was separately compared to the state-of-the-art recognition algorithms for each of the software. The results show that the algorithm was able to correctly infer students' activities significantly more often than the state-of-the-art, and was able to generalize to both of the software systems with no intervention.