As spoken natural language dialog systems technology continues to make great strides, numerous issues regarding dialog processing still need to be resolved. This book presents an exciting new dialog processing architecture that allows for a number of behaviors required for effective human-machine interactions, including: problem-solving to help the user carry out a task, coherent subdialog movement during the problem-solving process, user model usage, expectation usage for contextual interpretation and error correction, and variable initiative behavior for interacting with users of differing expertise. The book also details how different dialog problems in processing can be handled simultaneously, and provides instructions and in-depth result from pertinent experiments. Researchers and professionals in natural language systems will find this important new book an invaluable addition to their libraries.

A pragmatic architecture for voice dialog machines aimed at the equipment repair problem has been implemented which exhibits a number of behaviors required for efficient humanmachine dialog.

In the two decades following its initial release, SQLite has become the most widely deployed database engine in existence. Today, SQLite is found in nearly every smartphone, computer, web browser, television, and automobile. Several factors are likely responsible for its ubiquity, including its in-process design, standalone codebase, extensive test suite, and cross-platform file format. While it supports complex analytical queries, SQLite is primarily designed for fast online transaction processing (OLTP), employing row-oriented execution and a B-tree storage format. However, fueled by the rise of edge computing and data science, there is a growing need for efficient in-process online analytical processing (OLAP). DuckDB, a database engine nicknamed "the SQLite for analytics", has recently emerged to meet this demand. While DuckDB has shown strong performance on OLAP benchmarks, it is unclear how SQLite compares. Furthermore, we are aware of no work that attempts to identify root causes for SQLite's performance behavior on OLAP workloads. In this paper, we discuss SQLite in the context of this changing workload landscape. We describe how SQLite evolved from its humble beginnings to the full-featured database engine it is today. We evaluate the performance of modern SQLite on three benchmarks, each representing a different flavor of in-process data management, including transactional, analytical, and blob processing. We delve into analytical data processing on SQLite, identifying key bottlenecks and weighing potential solutions. As a result of our optimizations, SQLite is now up to 4.2X faster on SSB. Finally, we discuss the future of SQLite, envisioning how it will evolve to meet new demands and challenges.

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Consider the following dialog situation. The computer is providing a human user with assistance in fixing an electronic circuit that causes a Light Emitting Diode (LED) to display in a certain way. The current focus of the task and dialog is to determine the status of a wire between labeled connectors 84 and 99, a wire needed for the circuit that is absent. Figures 3.1 and 3.2 show two possible dialog interactions that could occur at this point. In figure 3.1, the computer has total dialog control, and a total of 29 utterances are needed to guide the user through the rest of the dialog. In figure 3.2, the human user has overall dialog control, but the computer is allowed to provide direct assistance as needed (i.e. in helping add the wire). Only 11 utterances are needed for the experienced user to complete the dialog. These samples are from interactions with a working spoken natural language dialog system. To engage in such dialog interactions, a system must exhibit the behaviors mentioned at the beginning of chapter 1: (1) problem solving for providing task assistance, (2) conducting subdialogs to achieve appropriate subgoals, (3) exploiting user model to enable useful interactions, (4) exploiting context dependent expectations when interpreting user inputs, and (5) engaging in variable initiative dialogs. Achieving these behaviors while facilitating the measurement of system performance via experimental interaction requires a theory of dialog processing that integrates the following subtheories. • An abstract model of interactive task processing. • A theory about the purpose of language within the interactive task processing environment. • A theory of user model usage. • A theory of contextual interpretation. • A theory of variable initiative dialog. This chapter presents such a theory of dialog processing. Frequent reference to the dialog examples in figures 3.1 and 3.2 will guide the discussion. The first section discusses the overall system architecture that facilitates integrated dialog processing. The remainder of the chapter addresses each subtheory in turn, emphasizing how each fits into the overall architecture. The chapter concludes with a summary description of the integrated model.