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ACM Doctoral Dissertation Award

This book addresses a fundamental software engineering issue, applying formal techniques and rigorous analysis to a practical problem of great current interest: the incorporation of language-specific knowledge in interactive programming environments. It makes a basic contribution in this area by proposing an attribute-grammar framework for incremental semantic analysis and establishing its algorithmic foundations. The results are theoretically important while having immediate practical utility for implementing environment-generating systems.

Randomization is an important tool in the design of algorithms, and the ability of randomization to provide enhanced power is a major research topic in complexity theory. Noam Nisan continues the investigation into the power of randomization and the relationships between randomized and deterministic complexity classes by pursuing the idea of emulating randomness, or pseudorandom generation.

Causal and Conditional Theories

Defaults in commonsense reasoning permit the generation of useful predictions in the absence of complete information. However, attempts to represent and reason with defaults in Al run into the problem of spurious arguments, arguments that rely on acceptable defaults but which support unacceptable conclusions. Geffner addresses this problem by analyzing the causal and conditional aspects of default, establishing clear theoretical limits on the capabilities of probabilistic approaches.

A New Approach to Circuit Depth

Communication Complexity describes a new intuitive model for studying circuit networks that captures the essence of circuit depth. Although the complexity of boolean functions has been studied for almost 4 decades, the main problems the inability to show a separation of any two classes, or to obtain nontrivial lower bounds remain unsolved. The communication complexity approach provides clues as to where to took for the heart of complexity and also sheds light on how to get around the difficulty of proving lower bounds.

The Complexity of Robot Motion Planning makes original contributions both to robotics and to the analysis of algorithms. In this groundbreaking monograph John Canny resolves long-standing problems concerning the complexity of motion planning and, for the central problem of finding a collision free path for a jointed robot in the presence of obstacles, obtains exponential speedups over existing algorithms by applying high-powered new mathematical techniques.