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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).(More)
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(More)
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(More)
— 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 dis-criminatively trained temporal models that can easily(More)
University that introduces students to all the concepts needed to create a complete intelligent robot. In particular, the course focuses on the areas of perception , cognition, and action by using the Sony AIBO robot as the focus for the programming assignments. This course shows how an AIBO and its software resources make it possible for students to(More)
— We present our work on creating a team of two humanoid robot commentators for soccer games of teams of four AIBO robots. The two humanoids stand on the side lines of the field, autonomously observe the game, wirelessly listen to a " game computer controller, " and coordinate their annoucements with each other. Given the large degree of uncertainty and(More)
We describe a new loss function, due to Jeon and Lin (2006), for estimating structured log-linear models on arbitrary features. The loss function can be seen as a (generative) alternative to maximum likelihood estimation with an interesting information-theoretic interpretation, and it is statistically consistent. It is substantially faster than maximum(More)
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