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Christopher Bailey-Kellogg

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4 papers

2 author rows

ICRA Conference 2001 Conference Paper

Physical Geometric Algorithms for Structural Molecular Biology

Bruce Randall Donald
Christopher Bailey-Kellogg
Jack Kelley
Ryan H. Lilien

This paper surveys our recent work in three key areas, using a physical geometric algorithm approach to data interpretation, experiment planning, and drug design: 1) data-directed computational protocols for high-throughput protein structure determination; 2) an experiment planning and data interpretation algorithms for reducing mass degeneracy in mass spectrometry; and 3) computer-aided drug design tools and applying them to the design of an inhibitor for the core-binding factor-/spl beta/ on-coprotein (CBF/spl beta/-MYII11), a fusion protein involved in some forms of acute myclomonocytic leukemia. Our long-range goal is the structural and functional understanding of biopolymer interactions in systems of significant biochemical as well as pharmacological interest. The research overviewed here represents a set of important steps towards that goal.

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AAAI Conference 1999 Conference Paper

Influence-Based Model Decomposition

Christopher Bailey-Kellogg
Dartmouth College
Feng Zhao
Xerox Palo Alto Research Center

Recent rapid advances in MEMS and information processing technologyhaveenableda newgeneration of AI robotic systems -- so-called SmartMatter systems -that are sensor rich and physically embedded. These systems range from decentralized control systems that regulate building temperature(smart buildings) to vehicle on-boarddiagnostic and control systemsthat interrogate large amounts of sensor data. Oneof the core tasks in the construction and operation of these SmartMatter systems is to synthesize optimal control policies using data rich modelsfor the systemsandenvironment. Unfortunately, these modelsmaycontain thousandsof coupledreal-valued variables and are prohibitively expensiveto reason about using traditional optimizationtechniques such as neural nets and genetic algorithms. This paper introduces a general mechanism for automatically decomposinga large model into smaller subparts so that these subparts can be separately optimized and then combined. The mechanism decomposesa model using an influence graph that records the coupling strengths among constituents of the model. This paper demonstrates the mechanismin an application of decentralizedoptimizationfor a temperature regulation problem. Performancedata has shownthat the approach is muchmoreefficient than the standard discrete optimization algorithms and achieves comparableaccuracy.

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AAAI Conference 1998 Conference Paper

Qualitative Analysis of Distributed Physical Systems with Applications to Control Synthesis

Christopher Bailey-Kellogg

Many important physical phenomena, such as temperature distribution, air flow, and acoustic waves, are describedas continuous, distributed parameterfields. Analyzing andcontrolling these physical processesand systemsare common tasks in manyscientific and engineering domains. However, the challenges are multifold: distributed fields are conceptually harderto reason about than lumpedparameter models; computational methods are prohibitively expensivefor complex spatial domains; the underlyingphysicsimposessevere constraints onobservabilityandcontrollability. This paper develops an ontological abstraction and a structure-based design mechanism, in a framework collectively known as spatial aggregation (SA), for reasoning about and synthesizing distributed control schemesfor physical fields. Theontological abstraction modelsa physical field as a hierarchyof networks of spatial objects. SAapplies a smallnumber of generic operators to a field to compute concise structural descriptions suchas iso-contours, gradienttrajectories, and influence graphs. Thedesign mechanism uses these representationsto find feasible control configurations. Weillustrate the mechanism using a thermal control problem from industrial heat treatment and demonstrate that the active exploitationof structural knowledgein physical fields yields a significant computational advantage.

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AAAI Conference 1996 Conference Paper

Spatial Aggregation: Language and Applications

Christopher Bailey-Kellogg

Spatial aggregation is a framework for organizing computations around image-like, analogue representations of physical processes in data interpretation and control tasks. It conceptualizes common computational structures in a class of implemented problem solvers for difficult scientific and engineering problems. It comprises a mechanism, a language, and a programming style. The spatial aggregation mechanism transforms a numerical input field to successively higher-level descriptions by applying a small, identical set of operators to each layer given a metric, neighborhood relation and equivalence relation. This paper describes the spatial aggregation language and its applications. The spatial aggregation language provides two abstract data types - neighborhood graph and field and a set of interface operators for constructing the transformations of the field, together with a library of component implementations from which a user can mix-and-match and specialize for a particular application. The language allows users to isolate and express important computational ideas in different problem domains while hiding low-level details. We illustrate the use of the language with examples ranging from trajectory grouping in dynamics interpretation to region growing in image analysis. Programs for these different task domains can be written in a modular, concise fashion in the spatial aggregation language.

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