Anind K. Dey

AAMAS Conference 2011 Conference Paper

Maximum Causal Entropy Correlated Equilibria for Markov Games

• Brian D. Ziebart
• J. Andrew Bagnell
• Anind K. Dey

Motivated by a machine learning perspective-that game-theoretic equilibria constraints should serve as guidelines for predicting agents' strategies, we introduce maximum causal entropy correlated equilibria (MCECE), a novel solution concept for general-sum Markov games. In line with this perspective, a MCECE strategy profile is a uniquely-defined joint probability distribution over actions for each game state that minimizes the worst-case prediction of agents' actions under log-loss. Equivalently, it maximizes the worst-case growth rate for gambling on the sequences of agents' joint actions under uniform odds. We present a convex optimization technique for obtaining MCECE strategy profiles that resembles value iteration in finite-horizon games. We assess the predictive benefits of our approach by predicting the strategies generated by previously proposed correlated equilibria solution concepts, and compare against those previous approaches on that same prediction task.

ICML Conference 2010 Conference Paper

Modeling Interaction via the Principle of Maximum Causal Entropy

• Brian D. Ziebart
• J. Andrew Bagnell
• Anind K. Dey

IROS Conference 2009 Conference Paper

Planning-based prediction for pedestrians

• Brian D. Ziebart
• Nathan D. Ratliff
• Garratt Gallagher
• Christoph Mertz
• Kevin M. Peterson
• J. Andrew Bagnell
• Martial Hebert
• Anind K. Dey

We present a novel approach for determining robot movements that efficiently accomplish the robot's tasks while not hindering the movements of people within the environment. Our approach models the goal-directed trajectories of pedestrians using maximum entropy inverse optimal control. The advantage of this modeling approach is the generality of its learned cost function to changes in the environment and to entirely different environments. We employ the predictions of this model of pedestrian trajectories in a novel incremental planner and quantitatively show the improvement in hindrance-sensitive robot trajectory planning provided by our approach.

ICAPS Conference 2008 Conference Paper

Fast Planning for Dynamic Preferences

• Brian D. Ziebart
• Anind K. Dey
• J. Andrew Bagnell

We present an algorithm that quickly finds optimal plans for unforeseen agent preferences within graph-based planning domains where actions have deterministic outcomes and action costs are linearly parameterized by preference parameters. We focus on vehicle route planning for drivers with personal trade-offs for different types of roads, and specifically on settings where these preferences are not known until planning time. We employ novel bounds (based on the triangle inequality and on the the concavity of the optimal plan cost in the space of preferences) to enable the reuse of previously computed optimal plans that are similar to the new plan preferences. The resulting lower bounds are employed to guide the search for the optimal plan up to 60 times more efficiently than previous methods.

UAI Conference 2007 Conference Paper

Learning Selectively Conditioned Forest Structures with Applications to DBNs and Classification

• Brian D. Ziebart
• Anind K. Dey
• J. Andrew Bagnell

Dealing with uncertainty in Bayesian Network structures using maximum a posteriori (MAP) estimation or Bayesian Model Averaging (BMA) is often intractable due to the superexponential number of possible directed, acyclic graphs. When the prior is decomposable, two classes of graphs where efficient learning can take place are tree structures, and fixed-orderings with limited in-degree. We show how MAP estimates and BMA for selectively conditioned forests (SCF), a combination of these two classes, can be computed efficiently for ordered sets of variables. We apply SCFs to temporal data to learn Dynamic Bayesian Networks having an intra-timestep forest and inter-timestep limited in-degree structure, improving model accuracy over DBNs without the combination of structures. We also apply SCFs to Bayes Net classification to learn selective forest augmented Naıve Bayes classifiers. We argue that the built-in feature selection of selective augmented Bayes classifiers makes them preferable to similar non-selective classifiers based on empirical evidence.