Planning & Scheduling: Instructional Materials


Plan Recognition for Exploratory Learning Environments Using Interleaved Temporal Search

AI Magazine

This article presents new algorithms for inferring users’ activities in a class of flexible and open-ended educational software called exploratory learning environments (ELE). Such settings provide a rich educational environment for students, but challenge teachers to keep track of students’ progress and to assess their performance. This article presents techniques for recognizing students activities in ELEs and visualizing these activities to students. It describes a new plan recognition algorithm that takes into account repetition and interleaving of activities. This algorithm was evaluated empirically using two ELEs for teaching chemistry and statistics used by thousands of students in several countries. It was able to outperform the state-of-the-art plan recognition algorithms when compared to a gold-standard that was obtained by a domain-expert. We also show that visualizing students’ plans improves their performance on new problems when compared to an alternative visualization that consists of a step-by-step list of actions.


Plan Recognition for Exploratory Domains Using Interleaved Temporal Search

AAAI Conferences

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 people’s 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.


A Planner for Exploratory Data Analysis

AAAI Conferences

The experiment involved setting a fire at a fixed location and specified tinm, a ld observing the behavior of the fireboss (the planner) and the bulldozers (the agents that put out the fire). VariabiliW between trials is due to randomly changing wind speed ml,.l direction, nonuniform terrain an,1 elevation, aald the varying aanounts of time agents take in executing primitive tasks. In this experiment we collected forty variM)les oww tile course of some 340 Phoenix trials, including measuremenls of the wind speed, the outcome (su('cess or failure).