Dana Wilkinson

IJCAI Conference 2007 Conference Paper

• Ali Ghodsi
• Dana Wilkinson
• Finnegan Southey

Dimensionality reduction is a much-studied task in machine learning in which high-dimensional data is mapped, possibly via a non-linear transformation, onto a low-dimensional manifold. The resulting embeddings, however, may fail to capture features of interest. One solution is to learn a distance metric which prefers embeddings that capture the salient features. We propose a novel approach to learning a metric from side information to guide the embedding process. Our approach admits the use of two kinds of side information. The first kind is class-equivalence information, where some limited number of pairwise "same/different class" statements are known. The second form of side information is a limited set of distances between pairs of points in the target metric space. We demonstrate the effectiveness of the method by producing embeddings that capture features of interest.

IJCAI Conference 2007 Conference Paper

• Finnegan Southey
• Wesley Loh
• Dana Wilkinson

Tracking the movements of a target based on limited observations plays a role in many interesting applications. Existing probabilistic tracking techniques have shown considerable success but the majority assume simplistic motion models suitable for short-term, local motion prediction. Agent movements are often governed by more sophisticated mechanisms such as a goal-directed path-planning algorithm. In such contexts we must go beyond estimating a target's current location to consider its future path and ultimate goal. We show how to use complex, "black box" motion models to infer distributions over a target's current position, origin, and destination, using only limited observations of the full path. Our approach accommodates motion models defined over a graph, including complex pathing algorithms such as A*. Robust and practical inference is achieved by using hidden semi-Markov models (HSMMs) and graph abstraction. The method has also been extended to effectively track multiple, indistinguishable agents via a greedy heuristic.

IJCAI Conference 2005 Conference Paper

Learning Subjective Representations for Planning

• Dana Wilkinson
• Michael Bowling
• Ali

Planning involves using a model of an agent’s actions to find a sequence of decisions which achieve a desired goal. It is usually assumed that the models are given, and such models often require expert knowledge of the domain. This paper explores subjective representations for planning that are learned directly from agent observations and actions (requiring no initial domain knowledge). A non-linear embedding technique called Action Respecting Embedding is used to construct such a representation. It is then shown how to extract the effects of the agent’s actions as operators in this learned representation. Finally, the learned representation and operators are combined with search to find sequences of actions that achieve given goals. The efficacy of this technique is demonstrated in a challenging robot-vision-inspired image domain.