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Charles Parker

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3 papers

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TIST Journal 2012 Journal Article

An Ensemble Architecture for Learning Complex Problem-Solving Techniques from Demonstration

Xiaoqin Shelley Zhang
Bhavesh Shrestha
Sungwook Yoon
Subbarao Kambhampati
Phillip DiBona
Jinhong K. Guo
Daniel McFarlane
Martin O. Hofmann

We present a novel ensemble architecture for learning problem-solving techniques from a very small number of expert solutions and demonstrate its effectiveness in a complex real-world domain. The key feature of our “Generalized Integrated Learning Architecture” (GILA) is a set of heterogeneous independent learning and reasoning (ILR) components, coordinated by a central meta-reasoning executive (MRE). The ILRs are weakly coupled in the sense that all coordination during learning and performance happens through the MRE. Each ILR learns independently from a small number of expert demonstrations of a complex task. During performance, each ILR proposes partial solutions to subproblems posed by the MRE, which are then selected from and pieced together by the MRE to produce a complete solution. The heterogeneity of the learner-reasoners allows both learning and problem solving to be more effective because their abilities and biases are complementary and synergistic. We describe the application of this novel learning and problem solving architecture to the domain of airspace management, where multiple requests for the use of airspaces need to be deconflicted, reconciled, and managed automatically. Formal evaluations show that our system performs as well as or better than humans after learning from the same training data. Furthermore, GILA outperforms any individual ILR run in isolation, thus demonstrating the power of the ensemble architecture for learning and problem solving.

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ICML Conference 2007 Conference Paper

Learning for efficient retrieval of structured data with noisy queries

Charles Parker
Alan Fern
Prasad Tadepalli

Increasingly large collections of structured data necessitate the development of efficient, noise-tolerant retrieval tools. In this work, we consider this issue and describe an approach to learn a similarity function that is not only accurate, but that also increases the effectiveness of retrieval data structures. We present an algorithm that uses functional gradient boosting to maximize both retrieval accuracy and the retrieval efficiency of vantage point trees. We demonstrate the effectiveness of our approach on two datasets, including a moderately sized real-world dataset of folk music.

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AAAI Conference 2006 Conference Paper

Gradient Boosting for Sequence Alignment

Charles Parker

Sequence alignment is a common subtask in many applications such as genetic matching and music information retrieval. Crucial to the performance of any sequence alignment algorithm is an accurate model of the reward of transforming one sequence into another. Using this model, we can find the optimal alignment of two sequences or perform query-based selection from a database of target sequences with a dynamic programming approach. In this paper, we describe a new algorithm to learn the reward models from positive and negative examples of matching sequences. We develop a gradient boosting approach that reduces sequence learning to a series of standard function approximation problems that can be solved by any function approximator. A key advantage of this approach is that it is able to induce complex features using function approximation rather than relying on the user to predefine such features. Our experiments on synthetic data and a fairly complex real-world music retrieval domain demonstrate that our approach can achieve better accuracy and faster learning compared to a state-of-the-art structured SVM approach.

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