The current paper focuses on modeling actions of high school students with a mathematics tutoring system with Hidden Markov Models. The results indicated that including a hidden state estimate of learner engagement increased the accuracy and predictive power of the models, both within and across tutoring sessions. Groups of students with distinct engagement trajectories were identified, and fin...

We consider the problem of performing inverse reinforcement learning when the trajectory of the expert is not perfectly observed by the learner. Instead, noisy observations of the trajectory are available. We generalize the previous method of expectation-maximization for inverse reinforcement learning, which allows the trajectory of the expert to be partially hidden from the learner, to incorpo...

|We propose a new method of classiication built as a cascade of a distributed learner and a local learner. The distributed learner generalizes to learn the \rule" and the local learner learns the \exceptions" not covered by the \rule." We show how such a system can be trained using cross-validation. We use a multi-layer perceptron with sigmoidal hidden units as the rule-learner and a k-nearest ...

The Indian demonetizations of November 2016 came at an entirely new scale to those January 1946 and 1978. This time around, the Narendra Modi government's measures applied 86.4% banknotes coins in circulation, equivalent 11% GDP. Much analysis has been undertaken on demonetization. largely critical because its disastrous consequences local populations most deprived among them. Our paper adopts ...

Recent work in the area of probabilistic user simulation for training statistical dialogue managers has investigated a new agenda-based user model and presented preliminary experiments with a handcrafted model parameter set. Training the model on dialogue data is an important next step, but non-trivial since the user agenda states are not observable in data and the space of possible states and ...

We propose a simple model of turn-taking in an information state based approach to dialogue using TTR (Type Theory with Records). The information state (dialogue gameboard) contains an agenda formulated as a list of speech event types that the dialogue participant plans to realize. A novel aspect of the proposal is that the agenda also includes types of events that intuitively should be carried...

We use a two-issue bargaining model with asymmetric information to study agent choice of how to structure bargaining. We uncover the settings in which di¤erent agenda structures are chosen in equilibrium, how the order in which issues are bargained over matters, and what impact the rules for implementing agreements have. If agreements are implemented as they are reached, “easy” issues are negot...

A revolutionary entrepreneur strategically chooses the revolutionary agenda to maximize the likelihood of revolution. Citizens have different preferences and can contribute varying degrees of support. This presents the revolutionary entrepreneur with a tradeoff between extensive and intensive margins of support. We show: (1) Extremists exert a disproportionate influence over the revolutionary a...

While the network loading problem for 2-layer threshold nets is NP-hard when learning from examples alone (as with backpropagation), (Baum, 91) has now proved that a learner can employ queries to evade the hidden unit credit assignment problem and PAC-load nets with up to four hidden units in polynomial time. Empirical tests show that the method can also learn far more complicated functions suc...