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Isbell, Charles L.

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Object Focused Q-Learning for Autonomous Agents

2013 , Cobo, Luis C. , Isbell, Charles L. , Thomaz, Andrea L.

We present Object Focused Q-learning (OF-Q), a novel reinforcement learning algorithm that can offer exponential speed-ups over classic Q-learning on domains composed of independent objects. An OF-Q agent treats the state space as a collection of objects organized into different object classes. Our key contribution is a control policy that uses non-optimal Q-functions to estimate the risk of ignoring parts of the state space. We compare our algorithm to traditional Q-learning and previous arbitration algorithms in two domains, including a version of Space Invaders.

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Batch versus Interactive Learning by Demonstration

2010 , Zang, Peng , Tian, Runhe , Thomaz, Andrea L. , Isbell, Charles L.

Agents that operate in human environments will need to be able to learn new skills from everyday people. Learning from demonstration (LfD) is a popular paradigm for this. Drawing from our interest in Socially Guided Machine Learning, we explore the impact of interactivity on learning from demonstration. We present findings from a study with human subjects showing people who are able to interact with the learning agent provide better demonstrations (in part) by adapting based on learner performance which results in improved learning performance. We also find that interactivity increases a sense of engagement and may encourage players to participate longer. Our exploration of interactivity sheds light on how best to obtain demonstrations for LfD applications.

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How A.I. and multi-robot systems research will accelerate our understanding of social animal behavior

2006-07 , Balch, Tucker , Dellaert, Frank , Feldman, Adam , Guillory, Andrew , Isbell, Charles L. , Khan, Zia , Pratt, Stephen , Stein, Andrew , Wilde, Hank

Our understanding of social insect behavior has significantly influenced A.I. and multi-robot systems’ research (e.g. ant algorithms and swarm robotics). In this work, however, we focus on the opposite question, namely: “how can multi-robot systems research contribute to the understanding of social animal behavior?.” As we show, we are able to contribute at several levels: First, using algorithms that originated in the robotics community, we can track animals under observation to provide essential quantitative data for animal behavior research. Second, by developing and applying algorithms originating in speech recognition and computer vision, we can automatically label the behavior of animals under observation. Our ultimate goal, however, is to automatically create, from observation, executable models of behavior. An executable model is a control program for an agent that can run in simulation (or on a robot). The representation for these executable models is drawn from research in multi-robot systems programming. In this paper we present the algorithms we have developed for tracking, recognizing, and learning models of social animal behavior, details of their implementation, and quantitative experimental results using them to study social insects.

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