Author name cluster

Douglas L. Vail

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4 papers

2 author rows

AAAI Conference 2008 Conference Paper

Feature Selection for Activity Recognition in Multi-Robot Domains

Douglas L. Vail

In multi-robot settings, activity recognition allows a robot to respond intelligently to the other robots in its environment. Conditional random fields are temporal models that are well suited for activity recognition because they can robustly incorporate rich, non-independent features computed from sensory data. In this work, we explore feature selection in conditional random fields for activity recognition to choose which features should be included in the final model. We compare two feature selection methods, grafting, a greedy forwardselection strategy, and `1 regularization, which simultaneously smoothes the model and selects a subset of the features. We use robot data recorded during four games of the Small Size League of the RoboCup’07 robot soccer world championship to empirically compare the performance of the two feature selection algorithms in terms of accuracy of the final model, the number of features selected in the final model, and the time required to train the final model.

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AAMAS Conference 2007 Conference Paper

Conditional Random Fields for Activity Recognition

Douglas L. Vail
Manuela M. Veloso
John D. Lafferty

Activity recognition is a key component for creating intelligent, multi-agent systems. Intrinsically, activity recognition is a temporal classification problem. In this paper, we compare two models for temporal classification: hidden Markov models (HMMs), which have long been applied to the activity recognition problem, and conditional random fields (CRFs). CRFs are discriminative models for labeling sequences. They condition on the entire observation sequence, which avoids the need for independence assumptions between observations. Conditioning on the observations vastly expands the set of features that can be incorporated into the model without violating its assumptions. Using data from a simulated robot tag domain, chosen because it is multi-agent and produces complex interactions between observations, we explore the differences in performance between the discriminatively trained CRF and the generative HMM. Additionally, we examine the effect of incorporating features which violate independence assumptions between observations; such features are typically necessary for high classification accuracy. We find that the discriminatively trained CRF performs as well as or better than an HMM even when the model features do not violate the independence assumptions of the HMM. In cases where features depend on observations from many time steps, we confirm that CRFs are robust against any degradation in performance.

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IROS Conference 2007 Conference Paper

Feature selection in conditional random fields for activity recognition

Douglas L. Vail
John D. Lafferty
Manuela Veloso

Temporal classification, such as activity recognition, is a key component for creating intelligent robot systems. In the case of robots, classification algorithms must robustly incorporate complex, non-independent features extracted from streams of sensor data. Conditional random fields are discriminatively trained temporal models that can easily incorporate such features. However, robots have few computational resources to spare for computing a large number of features from high bandwidth sensor data, which creates opportunities for feature selection. Creating models that contain only the most relevant features reduces the computational burden of temporal classification. In this paper, we show that lscr 1 regularization is an effective technique for feature selection in conditional random fields. We present results from a multi-robot tag domain with data from both real and simulated robots that compare the classification accuracy of models trained with lscr 1 regularization, which simultaneously smoothes the model and selects features; lscr 2 regularization, which smoothes to avoid over-fitting, but performs no feature selection; and models trained with no smoothing.

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IROS Conference 2003 Conference Paper

A real-time world model for multi-robot teams with high-latency communication

Maayan Roth
Douglas L. Vail
Manuela Veloso

In this paper, we present in detail our approach to constructing a world model in a multi-robot team. We introduce two separate world models, namely an individual world model that stores one robot's state, and a shared world model that stores the state of the team. We present procedures to effectively merge information in these two world models in real-time. We overcome the problem of high communication latency by using shared information on an as-needed basis. The success of our world model approach is validated by experimentation in the robot soccer domain. The results show that a team using a world model that incorporates shared information is more successful at tracking a dynamic object in its environment than a team that does not use shared information.

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