Peter Norvig

AAAI Conference 2016 Conference Paper

A Survey of Current Practice and Teaching of AI

• Michael Wollowski
• Robert Selkowitz
• Laura Brown
• Ashok Goel
• George Luger
• Jim Marshall
• Andrew Neel
• Todd Neller

The field of AI has changed significantly in the past couple of years and will likely continue to do so. Driven by a desire to expose our students to relevant and modern materials, we conducted two surveys, one of AI instructors and one of AI practitioners. The surveys were aimed at gathering information about the current state of the art of introducing AI as well as gathering input from practitioners in the field on techniques used in practice. In this paper, we present and briefly discuss the responses to those two surveys.

AIJ Journal 2011 Journal Article

Book review

• Peter Norvig

IJCAI Conference 2009 Conference Paper

• Mark Dredze
• Bill N. Schilit
• Peter Norvig

Growing email volumes cause flooded inboxes and swelled email archives, making search and new email processing difficult. While emails have rich metadata, such as recipients and folders, suitable for creating filtered views, it is often difficult to choose appropriate filters for new inbox messages without first examining messages. In this work, we consider a system that automatically suggests relevant view filters to the user for the currently viewed messages. We propose several ranking algorithms for suggesting useful filters. Our work suggests that such systems quickly filter groups of inbox messages and find messages more easily during search.

IS Journal 2009 Journal Article

The Unreasonable Effectiveness of Data

• Alon Halevy
• Peter Norvig
• Fernando Pereira

Problems that involve interacting with humans, such as natural language understanding, have not proven to be solvable by concise, neat formulas like F = ma. Instead, the best approach appears to be to embrace the complexity of the domain and address it by harnessing the power of data: if other humans engage in the tasks and generate large amounts of unlabeled, noisy data, new algorithms can be used to build high-quality models from the data.

AIJ Journal 2006 Journal Article

Editorial Note

• Peter Norvig
• Don Perlis

AIJ Journal 2005 Journal Article

Introduction to the Special Review Issue

• Don Perlis
• Peter Norvig

AIJ Journal 1994 Journal Article

Text-based intelligent systems

• Peter Norvig

AAAI Conference 1987 Conference Paper

Inference in Text Understanding

• Peter Norvig

The problem of deciding what was implied by a written text, of "reading between the lines" is the problem of inference. To extract proper inferences from a text requires a great deal of general knowledge on the part of the reader. Past approaches have often postulated an algorithm tuned to process a particular kind of knowledge structure (such as a script, or a plan). An alternative, unified approach is proposed. The algorithm recognizes six very general classes of inference, classes that are not dependent on individual knowledge structures, but instead rely on patterns of connectivity between concepts. The complexity has been effectively shifted from the algorithm to the knowledge base; new kinds of knowledge structures can be added without modifying the algorithm.

AIJ Journal 1985 Journal Article

Metaphors we live by

• Peter Norvig

IJCAI Conference 1983 Conference Paper

Frame Activated Inferences in a Story Understanding Program

• Peter Norvig

An effective story undcrstander must be able to reason about characters in the story, their affects, actions, plans, and goals, as well as the settings and important points of the story. In many systems this has been done with separate inference mechanisms for each class of knowledge structure. This paper proposes a story understander with a unified frame-based inference component used on each class of knowledge structure.

AAAI Conference 1983 Conference Paper

Six Problems for Story Understanders

• Peter Norvig

Story understanding programs have been classified as script-based processors, goal-based processors or multi-level processors. Each program introduces a new knowledge structure and invents a mechanism to make inferences and manage memory for that knowledge structure. This can lead to a proliferation of incomplete, incompatible processing mechanisms. The alternative presented here is to concentrate on the processing mechanism. It is suggested that a single inferencing scheme can deal with all knowledge structures in a uniform manner. Six basic problems that such a processor must address are presented and discussed.