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Henry Kautz

Other affiliations: University of Washington, Alcatel-Lucent, AT&T Labs  ...read more
Bio: Henry Kautz is an academic researcher from University of Rochester. The author has contributed to research in topics: Activity recognition & Inference. The author has an hindex of 76, co-authored 245 publications receiving 26396 citations. Previous affiliations of Henry Kautz include University of Washington & Alcatel-Lucent.


Papers
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Journal ArticleDOI
TL;DR: ReferralWeb as mentioned in this paper is an interactive system for reconstructing, visualizing, and searching social networks on the World Wide Web, which is based on the six degrees of separation phenomenon.
Abstract: Part of the success of social networks can be attributed to the “six degrees of separation’’ phenomena that means the distance between any two individuals in terms of direct personal relationships is relatively small. An equally important factor is there are limits to the amount and kinds of information a person is able or willing to make available to the public at large. For example, an expert in a particular field is almost certainly unable to write down all he knows about the topic, and is likely to be unwilling to make letters of recommendation he or she has written for various people publicly available. Thus, searching for a piece of information in this situation becomes a matter of searching the social network for an expert on the topic together with a chain of personal referrals from the searcher to the expert. The referral chain serves two key functions: It provides a reason for the expert to agree to respond to the requester by making their relationship explicit (for example, they have a mutual collaborator), and it provides a criteria for the searcher to use in evaluating the trustworthiness of the expert. Nonetheless, manually searching for a referral chain can be a frustrating and time-consuming task. One is faced with the trade-off of contacting a large number of individuals at each step, and thus straining both the time and goodwill of the possible respondents, or of contacting a smaller, more focused set, and being more likely to fail to locate an appropriate expert. In response to these problems we are building ReferralWeb, an interactive system for reconstructing, visualizing, and searching social networks on the World-Wide Web. Simulation experiments we ran before we began construction of ReferralWeb showed that automatically generated referrals can be highly

1,094 citations

Proceedings Article
30 Aug 1992
TL;DR: SATPLAN04 is a updated version of the planning as satisfiability approach originally proposed in (Kautz & Selman 1992) using hand-generated translations, and implemented for PDDL input in the blackbox system.
Abstract: SATPLAN04 is a updated version of the planning as satisfiability approach originally proposed in (Kautz & Selman 1992; 1996) using hand-generated translations, and implemented for PDDL input in the blackbox system (Kautz & Selman 1999). Like blackbox, SATPLAN04 accepts the STRIPS subset of PDDL and finds solutions with minimal parallel length: that is, many (non-interferring) actions may occur in parallel at each time step, and the total number of time steps in guaranteed to be as small as possible. Also like blackbox, SATPLAN works by:

1,018 citations

Proceedings Article
Henry Kautz1, Bart Selman1
04 Aug 1996
TL;DR: Stochastic methods are shown to be very effective on a wide range of scheduling problems, but this is the first demonstration of its power on truly challenging classical planning instances.
Abstract: Planning is a notoriously hard combinatorial search problem. In many interesting domains, current planning algorithms fail to scale up gracefully. By combining a general, stochastic search algorithm and appropriate problem encodings based on propositional logic, we are able to solve hard planning problems many times faster than the best current planning systems. Although stochastic methods have been shown to be very effective on a wide range of scheduling problems, this is the first demonstration of its power on truly challenging classical planning instances. This work also provides a new perspective on representational issues in planning.

968 citations

Proceedings Article
05 Oct 1994
TL;DR: It is shown that mixed random walk is the superior strategy for solving MAX-SAT problems, and results demonstrating the effectiveness of local search with walk for solving circuit synthesis and circuit diagnosis problems are presented.
Abstract: It has recently been shown that local search is surprisingly good at finding satisfying assignments for certain computationally hard classes of CNF formulas. The performance of basic local search methods can be further enhanced by introducing mechanisms for escaping from local minima in the search space. We will compare three such mechanisms: simulated annealing, random noise, and a strategy called "mixed random walk". We show that mixed random walk is the superior strategy. We also present results demonstrating the effectiveness of local search with walk for solving circuit synthesis and circuit diagnosis problems. Finally, we demonstrate that mixed random walk improves upon the best known methods for solving MAX-SAT problems.

908 citations

Journal ArticleDOI
TL;DR: The key observation is that the sequence of objects a person uses while performing an ADL robustly characterizes both the ADL's identity and the quality of its execution.
Abstract: A key aspect of pervasive computing is using computers and sensor networks to effectively and unobtrusively infer users' behavior in their environment. This includes inferring which activity users are performing, how they're performing it, and its current stage. Recognizing and recording activities of daily living is a significant problem in elder care. A new paradigm for ADL inferencing leverages radio-frequency-identification technology, data mining, and a probabilistic inference engine to recognize ADLs, based on the objects people use. We propose an approach that addresses these challenges and shows promise in automating some types of ADL monitoring. Our key observation is that the sequence of objects a person uses while performing an ADL robustly characterizes both the ADL's identity and the quality of its execution. So, we have developed Proactive Activity Toolkit (PROACT).

887 citations


Cited by
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Journal ArticleDOI
TL;DR: Developments in this field are reviewed, including such concepts as the small-world effect, degree distributions, clustering, network correlations, random graph models, models of network growth and preferential attachment, and dynamical processes taking place on networks.
Abstract: Inspired by empirical studies of networked systems such as the Internet, social networks, and biological networks, researchers have in recent years developed a variety of techniques and models to help us understand or predict the behavior of these systems. Here we review developments in this field, including such concepts as the small-world effect, degree distributions, clustering, network correlations, random graph models, models of network growth and preferential attachment, and dynamical processes taking place on networks.

17,647 citations

Journal ArticleDOI
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

13,246 citations

Christopher M. Bishop1
01 Jan 2006
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

10,141 citations