47 results on '"Horvitz, Eric J."'
Search Results
2. The Myth of Modularity in Rule-Based Systems
- Author
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Heckerman, David and Horvitz, Eric J.
- Subjects
Computer Science - Artificial Intelligence - Abstract
In this paper, we examine the concept of modularity, an often cited advantage of the ruled-based representation methodology. We argue that the notion of modularity consists of two distinct concepts which we call syntactic modularity and semantic modularity. We argue that when reasoning under certainty, it is reasonable to regard the rule-based approach as both syntactically and semantically modular. However, we argue that in the case of plausible reasoning, rules are syntactically modular but are rarely semantically modular. To illustrate this point, we examine a particular approach for managing uncertainty in rule-based systems called the MYCIN certainty factor model. We formally define the concept of semantic modularity with respect to the certainty factor model and discuss logical consequences of the definition. We show that the assumption of semantic modularity imposes strong restrictions on rules in a knowledge base. We argue that such restrictions are rarely valid in practical applications. Finally, we suggest how the concept of semantic modularity can be relaxed in a manner that makes it appropriate for plausible reasoning., Comment: Appears in Proceedings of the Second Conference on Uncertainty in Artificial Intelligence (UAI1986)
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- 2013
3. Reasoning About Beliefs and Actions Under Computational Resource Constraints
- Author
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Horvitz, Eric J.
- Subjects
Computer Science - Artificial Intelligence - Abstract
Although many investigators affirm a desire to build reasoning systems that behave consistently with the axiomatic basis defined by probability theory and utility theory, limited resources for engineering and computation can make a complete normative analysis impossible. We attempt to move discussion beyond the debate over the scope of problems that can be handled effectively to cases where it is clear that there are insufficient computational resources to perform an analysis deemed as complete. Under these conditions, we stress the importance of considering the expected costs and benefits of applying alternative approximation procedures and heuristics for computation and knowledge acquisition. We discuss how knowledge about the structure of user utility can be used to control value tradeoffs for tailoring inference to alternative contexts. We address the notion of real-time rationality, focusing on the application of knowledge about the expected timewise-refinement abilities of reasoning strategies to balance the benefits of additional computation with the costs of acting with a partial result. We discuss the benefits of applying decision theory to control the solution of difficult problems given limitations and uncertainty in reasoning resources., Comment: Appears in Proceedings of the Third Conference on Uncertainty in Artificial Intelligence (UAI1987)
- Published
- 2013
4. Bounded Conditioning: Flexible Inference for Decisions under Scarce Resources
- Author
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Horvitz, Eric J., Suermondt, Jaap, and Cooper, Gregory F.
- Subjects
Computer Science - Artificial Intelligence - Abstract
We introduce a graceful approach to probabilistic inference called bounded conditioning. Bounded conditioning monotonically refines the bounds on posterior probabilities in a belief network with computation, and converges on final probabilities of interest with the allocation of a complete resource fraction. The approach allows a reasoner to exchange arbitrary quantities of computational resource for incremental gains in inference quality. As such, bounded conditioning holds promise as a useful inference technique for reasoning under the general conditions of uncertain and varying reasoning resources. The algorithm solves a probabilistic bounding problem in complex belief networks by breaking the problem into a set of mutually exclusive, tractable subproblems and ordering their solution by the expected effect that each subproblem will have on the final answer. We introduce the algorithm, discuss its characterization, and present its performance on several belief networks, including a complex model for reasoning about problems in intensive-care medicine., Comment: Appears in Proceedings of the Fifth Conference on Uncertainty in Artificial Intelligence (UAI1989)
- Published
- 2013
5. The Compilation of Decision Models
- Author
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Heckerman, David, Breese, John S., and Horvitz, Eric J.
- Subjects
Computer Science - Artificial Intelligence - Abstract
We introduce and analyze the problem of the compilation of decision models from a decision-theoretic perspective. The techniques described allow us to evaluate various configurations of compiled knowledge given the nature of evidential relationships in a domain, the utilities associated with alternative actions, the costs of run-time delays, and the costs of memory. We describe procedures for selecting a subset of the total observations available to be incorporated into a compiled situation-action mapping, in the context of a binary decision with conditional independence of evidence. The methods allow us to incrementally select the best pieces of evidence to add to the set of compiled knowledge in an engineering setting. After presenting several approaches to compilation, we exercise one of the methods to provide insight into the relationship between the distribution over weights of evidence and the preferred degree of compilation., Comment: Appears in Proceedings of the Fifth Conference on Uncertainty in Artificial Intelligence (UAI1989)
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- 2013
6. Ideal Reformulation of Belief Networks
- Author
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Breese, John S. and Horvitz, Eric J.
- Subjects
Computer Science - Artificial Intelligence - Abstract
The intelligent reformulation or restructuring of a belief network can greatly increase the efficiency of inference. However, time expended for reformulation is not available for performing inference. Thus, under time pressure, there is a tradeoff between the time dedicated to reformulating the network and the time applied to the implementation of a solution. We investigate this partition of resources into time applied to reformulation and time used for inference. We shall describe first general principles for computing the ideal partition of resources under uncertainty. These principles have applicability to a wide variety of problems that can be divided into interdependent phases of problem solving. After, we shall present results of our empirical study of the problem of determining the ideal amount of time to devote to searching for clusters in belief networks. In this work, we acquired and made use of probability distributions that characterize (1) the performance of alternative heuristic search methods for reformulating a network instance into a set of cliques, and (2) the time for executing inference procedures on various belief networks. Given a preference model describing the value of a solution as a function of the delay required for its computation, the system selects an ideal time to devote to reformulation., Comment: Appears in Proceedings of the Sixth Conference on Uncertainty in Artificial Intelligence (UAI1990)
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- 2013
7. Problem Formulation as the Reduction of a Decision Model
- Author
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Heckerman, David and Horvitz, Eric J.
- Subjects
Computer Science - Artificial Intelligence - Abstract
In this paper, we extend the QMRDT probabilistic model for the domain of internal medicine to include decisions about treatments. In addition, we describe how we can use the comprehensive decision model to construct a simpler decision model for a specific patient. In so doing, we transform the task of problem formulation to that of narrowing of a larger problem., Comment: Appears in Proceedings of the Sixth Conference on Uncertainty in Artificial Intelligence (UAI1990)
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- 2013
8. An Approximate Nonmyopic Computation for Value of Information
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Heckerman, David, Horvitz, Eric J., and Middleton, Blackford
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Computer Science - Artificial Intelligence - Abstract
Value-of-information analyses provide a straightforward means for selecting the best next observation to make, and for determining whether it is better to gather additional information or to act immediately. Determining the next best test to perform, given a state of uncertainty about the world, requires a consideration of the value of making all possible sequences of observations. In practice, decision analysts and expert-system designers have avoided the intractability of exact computation of the value of information by relying on a myopic approximation. Myopic analyses are based on the assumption that only one additional test will be performed, even when there is an opportunity to make a large number of observations. We present a nonmyopic approximation for value of information that bypasses the traditional myopic analyses by exploiting the statistical properties of large samples., Comment: Appears in Proceedings of the Seventh Conference on Uncertainty in Artificial Intelligence (UAI1991)
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- 2013
9. Time-Dependent Utility and Action Under Uncertainty
- Author
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Horvitz, Eric J. and Rutledge, Geoffrey
- Subjects
Computer Science - Artificial Intelligence - Abstract
We discuss representing and reasoning with knowledge about the time-dependent utility of an agent's actions. Time-dependent utility plays a crucial role in the interaction between computation and action under bounded resources. We present a semantics for time-dependent utility and describe the use of time-dependent information in decision contexts. We illustrate our discussion with examples of time-pressured reasoning in Protos, a system constructed to explore the ideal control of inference by reasoners with limit abilities., Comment: Appears in Proceedings of the Seventh Conference on Uncertainty in Artificial Intelligence (UAI1991)
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- 2013
10. Dynamic Network Models for Forecasting
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Dagum, Paul, Galper, Adam, and Horvitz, Eric J.
- Subjects
Computer Science - Artificial Intelligence - Abstract
We have developed a probabilistic forecasting methodology through a synthesis of belief network models and classical time-series analysis. We present the dynamic network model (DNM) and describe methods for constructing, refining, and performing inference with this representation of temporal probabilistic knowledge. The DNM representation extends static belief-network models to more general dynamic forecasting models by integrating and iteratively refining contemporaneous and time-lagged dependencies. We discuss key concepts in terms of a model for forecasting U.S. car sales in Japan., Comment: Appears in Proceedings of the Eighth Conference on Uncertainty in Artificial Intelligence (UAI1992)
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- 2013
11. Reformulating Inference Problems Through Selective Conditioning
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Dagum, Paul and Horvitz, Eric J.
- Subjects
Computer Science - Artificial Intelligence - Abstract
We describe how we selectively reformulate portions of a belief network that pose difficulties for solution with a stochastic-simulation algorithm. With employ the selective conditioning approach to target specific nodes in a belief network for decomposition, based on the contribution the nodes make to the tractability of stochastic simulation. We review previous work on BNRAS algorithms- randomized approximation algorithms for probabilistic inference. We show how selective conditioning can be employed to reformulate a single BNRAS problem into multiple tractable BNRAS simulation problems. We discuss how we can use another simulation algorithm-logic sampling-to solve a component of the inference problem that provides a means for knitting the solutions of individual subproblems into a final result. Finally, we analyze tradeoffs among the computational subtasks associated with the selective conditioning approach to reformulation., Comment: Appears in Proceedings of the Eighth Conference on Uncertainty in Artificial Intelligence (UAI1992)
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- 2013
12. A Synthesis of Logical and Probabilistic Reasoning for Program Understanding and Debugging
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Burnell, Lisa J. and Horvitz, Eric J.
- Subjects
Computer Science - Artificial Intelligence - Abstract
We describe the integration of logical and uncertain reasoning methods to identify the likely source and location of software problems. To date, software engineers have had few tools for identifying the sources of error in complex software packages. We describe a method for diagnosing software problems through combining logical and uncertain reasoning analyses. Our preliminary results suggest that such methods can be of value in directing the attention of software engineers to paths of an algorithm that have the highest likelihood of harboring a programming error., Comment: Appears in Proceedings of the Ninth Conference on Uncertainty in Artificial Intelligence (UAI1993)
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- 2013
13. Reasoning about the Value of Decision-Model Refinement: Methods and Application
- Author
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Poh, Kim-Leng and Horvitz, Eric J.
- Subjects
Computer Science - Artificial Intelligence - Abstract
We investigate the value of extending the completeness of a decision model along different dimensions of refinement. Specifically, we analyze the expected value of quantitative, conceptual, and structural refinement of decision models. We illustrate the key dimensions of refinement with examples. The analyses of value of model refinement can be used to focus the attention of an analyst or an automated reasoning system on extensions of a decision model associated with the greatest expected value., Comment: Appears in Proceedings of the Ninth Conference on Uncertainty in Artificial Intelligence (UAI1993)
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- 2013
14. Utility-Based Abstraction and Categorization
- Author
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Horvitz, Eric J. and Klein, Adrian
- Subjects
Computer Science - Artificial Intelligence - Abstract
We take a utility-based approach to categorization. We construct generalizations about events and actions by considering losses associated with failing to distinguish among detailed distinctions in a decision model. The utility-based methods transform detailed states of the world into more abstract categories comprised of disjunctions of the states. We show how we can cluster distinctions into groups of distinctions at progressively higher levels of abstraction, and describe rules for decision making with the abstractions. The techniques introduce a utility-based perspective on the nature of concepts, and provide a means of simplifying decision models used in automated reasoning systems. We demonstrate the techniques by describing the capabilities and output of TUBA, a program for utility-based abstraction., Comment: Appears in Proceedings of the Ninth Conference on Uncertainty in Artificial Intelligence (UAI1993)
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- 2013
15. Exploiting System Hierarchy to Compute Repair Plans in Probabilistic Model-based Diagnosis
- Author
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Srinivas, Sampath and Horvitz, Eric J.
- Subjects
Computer Science - Artificial Intelligence - Abstract
The goal of model-based diagnosis is to isolate causes of anomalous system behavior and recommend inexpensive repair actions in response. In general, precomputing optimal repair policies is intractable. To date, investigators addressing this problem have explored approximations that either impose restrictions on the system model (such as a single fault assumption) or compute an immediate best action with limited lookahead. In this paper, we develop a formulation of repair in model-based diagnosis and a repair algorithm that computes optimal sequences of actions. This optimal approach is costly but can be applied to precompute an optimal repair strategy for compact systems. We show how we can exploit a hierarchical system specification to make this approach tractable for large systems. When introducing hierarchy, we also consider the tradeoff between simply replacing a component and decomposing it to repair its subcomponents. The hierarchical repair algorithm is suitable for off-line precomputation of an optimal repair strategy. A modification of the algorithm takes advantage of an iterative deepening scheme to trade off inference time and the quality of the computed strategy., Comment: Appears in Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence (UAI1995)
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- 2013
16. Display of Information for Time-Critical Decision Making
- Author
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Horvitz, Eric J. and Barry, Matthew
- Subjects
Computer Science - Artificial Intelligence - Abstract
We describe methods for managing the complexity of information displayed to people responsible for making high-stakes, time-critical decisions. The techniques provide tools for real-time control of the configuration and quantity of information displayed to a user, and a methodology for designing flexible human-computer interfaces for monitoring applications. After defining a prototypical set of display decision problems, we introduce the expected value of revealed information (EVRI) and the related measure of expected value of displayed information (EVDI). We describe how these measures can be used to enhance computer displays used for monitoring complex systems. We motivate the presentation by discussing our efforts to employ decision-theoretic control of displays for a time-critical monitoring application at the NASA Mission Control Center in Houston., Comment: Appears in Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence (UAI1995)
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- 2013
17. Reasoning, Metareasoning, and Mathematical Truth: Studies of Theorem Proving under Limited Resources
- Author
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Horvitz, Eric J. and Klein, Adrian
- Subjects
Computer Science - Artificial Intelligence - Abstract
In earlier work, we introduced flexible inference and decision-theoretic metareasoning to address the intractability of normative inference. Here, rather than pursuing the task of computing beliefs and actions with decision models composed of distinctions about uncertain events, we examine methods for inferring beliefs about mathematical truth before an automated theorem prover completes a proof. We employ a Bayesian analysis to update belief in truth, given theorem-proving progress, and show how decision-theoretic methods can be used to determine the value of continuing to deliberate versus taking immediate action in time-critical situations., Comment: Appears in Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence (UAI1995)
- Published
- 2013
18. A Graph-Theoretic Analysis of Information Value
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Poh, Kim-Leng and Horvitz, Eric J.
- Subjects
Computer Science - Artificial Intelligence - Abstract
We derive qualitative relationships about the informational relevance of variables in graphical decision models based on a consideration of the topology of the models. Specifically, we identify dominance relations for the expected value of information on chance variables in terms of their position and relationships in influence diagrams. The qualitative relationships can be harnessed to generate nonnumerical procedures for ordering uncertain variables in a decision model by their informational relevance., Comment: Appears in Proceedings of the Twelfth Conference on Uncertainty in Artificial Intelligence (UAI1996)
- Published
- 2013
19. Perception, Attention, and Resources: A Decision-Theoretic Approach to Graphics Rendering
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Horvitz, Eric J. and Lengyel, Jed
- Subjects
Computer Science - Artificial Intelligence ,Computer Science - Graphics - Abstract
We describe work to control graphics rendering under limited computational resources by taking a decision-theoretic perspective on perceptual costs and computational savings of approximations. The work extends earlier work on the control of rendering by introducing methods and models for computing the expected cost associated with degradations of scene components. The expected cost is computed by considering the perceptual cost of degradations and a probability distribution over the attentional focus of viewers. We review the critical literature describing findings on visual search and attention, discuss the implications of the findings, and introduce models of expected perceptual cost. Finally, we discuss policies that harness information about the expected cost of scene components., Comment: Appears in Proceedings of the Thirteenth Conference on Uncertainty in Artificial Intelligence (UAI1997)
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- 2013
20. Time-Critical Reasoning: Representations and Application
- Author
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Horvitz, Eric J. and Seiver, Adam
- Subjects
Computer Science - Artificial Intelligence - Abstract
We review the problem of time-critical action and discuss a reformulation that shifts knowledge acquisition from the assessment of complex temporal probabilistic dependencies to the direct assessment of time-dependent utilities over key outcomes of interest. We dwell on a class of decision problems characterized by the centrality of diagnosing and reacting in a timely manner to pathological processes. We motivate key ideas in the context of trauma-care triage and transportation decisions., Comment: Appears in Proceedings of the Thirteenth Conference on Uncertainty in Artificial Intelligence (UAI1997)
- Published
- 2013
21. The Lumiere Project: Bayesian User Modeling for Inferring the Goals and Needs of Software Users
- Author
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Horvitz, Eric J., Breese, John S., Heckerman, David, Hovel, David, and Rommelse, Koos
- Subjects
Computer Science - Artificial Intelligence ,Computer Science - Human-Computer Interaction - Abstract
The Lumiere Project centers on harnessing probability and utility to provide assistance to computer software users. We review work on Bayesian user models that can be employed to infer a users needs by considering a user's background, actions, and queries. Several problems were tackled in Lumiere research, including (1) the construction of Bayesian models for reasoning about the time-varying goals of computer users from their observed actions and queries, (2) gaining access to a stream of events from software applications, (3) developing a language for transforming system events into observational variables represented in Bayesian user models, (4) developing persistent profiles to capture changes in a user expertise, and (5) the development of an overall architecture for an intelligent user interface. Lumiere prototypes served as the basis for the Office Assistant in the Microsoft Office '97 suite of productivity applications., Comment: Appears in Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence (UAI1998)
- Published
- 2013
22. Inferring Informational Goals from Free-Text Queries: A Bayesian Approach
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Heckerman, David and Horvitz, Eric J.
- Subjects
Computer Science - Information Retrieval ,Computer Science - Artificial Intelligence ,Computer Science - Computation and Language - Abstract
People using consumer software applications typically do not use technical jargon when querying an online database of help topics. Rather, they attempt to communicate their goals with common words and phrases that describe software functionality in terms of structure and objects they understand. We describe a Bayesian approach to modeling the relationship between words in a user's query for assistance and the informational goals of the user. After reviewing the general method, we describe several extensions that center on integrating additional distinctions and structure about language usage and user goals into the Bayesian models., Comment: Appears in Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence (UAI1998)
- Published
- 2013
23. Attention-Sensitive Alerting
- Author
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Horvitz, Eric J., Jacobs, Andy, and Hovel, David
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Computer Science - Artificial Intelligence ,Computer Science - Human-Computer Interaction - Abstract
We introduce utility-directed procedures for mediating the flow of potentially distracting alerts and communications to computer users. We present models and inference procedures that balance the context-sensitive costs of deferring alerts with the cost of interruption. We describe the challenge of reasoning about such costs under uncertainty via an analysis of user activity and the content of notifications. After introducing principles of attention-sensitive alerting, we focus on the problem of guiding alerts about email messages. We dwell on the problem of inferring the expected criticality of email and discuss work on the Priorities system, centering on prioritizing email by criticality and modulating the communication of notifications to users about the presence and nature of incoming email., Comment: Appears in Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence (UAI1999)
- Published
- 2013
24. Conversation as Action Under Uncertainty
- Author
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Paek, Tim and Horvitz, Eric J.
- Subjects
Computer Science - Artificial Intelligence - Abstract
Conversations abound with uncetainties of various kinds. Treating conversation as inference and decision making under uncertainty, we propose a task independent, multimodal architecture for supporting robust continuous spoken dialog called Quartet. We introduce four interdependent levels of analysis, and describe representations, inference procedures, and decision strategies for managing uncertainties within and between the levels. We highlight the approach by reviewing interactions between a user and two spoken dialog systems developed using the Quartet architecture: Prsenter, a prototype system for navigating Microsoft PowerPoint presentations, and the Bayesian Receptionist, a prototype system for dealing with tasks typically handled by front desk receptionists at the Microsoft corporate campus., Comment: Appears in Proceedings of the Sixteenth Conference on Uncertainty in Artificial Intelligence (UAI2000)
- Published
- 2013
25. Collaborative Filtering by Personality Diagnosis: A Hybrid Memory- and Model-Based Approach
- Author
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Pennock, David M., Horvitz, Eric J., Lawrence, Steve, and Giles, C. Lee
- Subjects
Computer Science - Information Retrieval - Abstract
The growth of Internet commerce has stimulated the use of collaborative filtering (CF) algorithms as recommender systems. Such systems leverage knowledge about the known preferences of multiple users to recommend items of interest to other users. CF methods have been harnessed to make recommendations about such items as web pages, movies, books, and toys. Researchers have proposed and evaluated many approaches for generating recommendations. We describe and evaluate a new method called emph{personality diagnosis (PD)}. Given a user's preferences for some items, we compute the probability that he or she is of the same "personality type" as other users, and, in turn, the probability that he or she will like new items. PD retains some of the advantages of traditional similarity-weighting techniques in that all data is brought to bear on each prediction and new data can be added easily and incrementally. Additionally, PD has a meaningful probabilistic interpretation, which may be leveraged to justify, explain, and augment results. We report empirical results on the EachMovie database of movie ratings, and on user profile data collected from the CiteSeer digital library of Computer Science research papers. The probabilistic framework naturally supports a variety of descriptive measurements - in particular, we consider the applicability of a value of information (VOI) computation., Comment: Appears in Proceedings of the Sixteenth Conference on Uncertainty in Artificial Intelligence (UAI2000)
- Published
- 2013
26. A Bayesian Approach to Tackling Hard Computational Problems
- Author
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Horvitz, Eric J., Ruan, Yongshao, Gomes, Carla P., Kautz, Henry, Selman, Bart, and Chickering, David Maxwell
- Subjects
Computer Science - Artificial Intelligence - Abstract
We are developing a general framework for using learned Bayesian models for decision-theoretic control of search and reasoningalgorithms. We illustrate the approach on the specific task of controlling both general and domain-specific solvers on a hard class of structured constraint satisfaction problems. A successful strategyfor reducing the high (and even infinite) variance in running time typically exhibited by backtracking search algorithms is to cut off and restart the search if a solution is not found within a certainamount of time. Previous work on restart strategies have employed fixed cut off values. We show how to create a dynamic cut off strategy by learning a Bayesian model that predicts the ultimate length of a trial based on observing the early behavior of the search algorithm. Furthermore, we describe the general conditions under which a dynamic restart strategy can outperform the theoretically optimal fixed strategy., Comment: Appears in Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence (UAI2001)
- Published
- 2013
27. Coordinates: Probabilistic Forecasting of Presence and Availability
- Author
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Horvitz, Eric J., Koch, Paul, Kadie, Carl, and Jacobs, Andy
- Subjects
Computer Science - Human-Computer Interaction ,Computer Science - Artificial Intelligence - Abstract
We present methods employed in Coordinate, a prototype service that supports collaboration and communication by learning predictive models that provide forecasts of users s AND availability.We describe how data IS collected about USER activity AND proximity FROM multiple devices, IN addition TO analysis OF the content OF users, the time of day, and day of week. We review applications of presence forecasting embedded in the Priorities application and then present details of the Coordinate service that was informed by the earlier efforts., Comment: Appears in Proceedings of the Eighteenth Conference on Uncertainty in Artificial Intelligence (UAI2002)
- Published
- 2012
28. Web-Based Question Answering: A Decision-Making Perspective
- Author
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Azari, David, Horvitz, Eric J., Dumais, Susan, and Brill, Eric
- Subjects
Computer Science - Information Retrieval ,Computer Science - Computation and Language - Abstract
We describe an investigation of the use of probabilistic models and cost-benefit analyses to guide resource-intensive procedures used by a Web-based question answering system. We first provide an overview of research on question-answering systems. Then, we present details on AskMSR, a prototype web-based question answering system. We discuss Bayesian analyses of the quality of answers generated by the system and show how we can endow the system with the ability to make decisions about the number of queries issued to a search engine, given the cost of queries and the expected value of query results in refining an ultimate answer. Finally, we review the results of a set of experiments., Comment: Appears in Proceedings of the Nineteenth Conference on Uncertainty in Artificial Intelligence (UAI2003)
- Published
- 2012
29. Prediction, Expectation, and Surprise: Methods, Designs, and Study of a Deployed Traffic Forecasting Service
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Horvitz, Eric J., Apacible, Johnson, Sarin, Raman, and Liao, Lin
- Subjects
Computer Science - Artificial Intelligence ,Physics - Physics and Society - Abstract
We present research on developing models that forecast traffic flow and congestion in the Greater Seattle area. The research has led to the deployment of a service named JamBayes, that is being actively used by over 2,500 users via smartphones and desktop versions of the system. We review the modeling effort and describe experiments probing the predictive accuracy of the models. Finally, we present research on building models that can identify current and future surprises, via efforts on modeling and forecasting unexpected situations., Comment: Appears in Proceedings of the Twenty-First Conference on Uncertainty in Artificial Intelligence (UAI2005)
- Published
- 2012
30. On Discarding, Caching, and Recalling Samples in Active Learning
- Author
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Kapoor, Ashish and Horvitz, Eric J.
- Subjects
Computer Science - Learning ,Statistics - Machine Learning - Abstract
We address challenges of active learning under scarce informational resources in non-stationary environments. In real-world settings, data labeled and integrated into a predictive model may become invalid over time. However, the data can become informative again with switches in context and such changes may indicate unmodeled cyclic or other temporal dynamics. We explore principles for discarding, caching, and recalling labeled data points in active learning based on computations of value of information. We review key concepts and study the value of the methods via investigations of predictive performance and costs of acquiring data for simulated and real-world data sets., Comment: Appears in Proceedings of the Twenty-Third Conference on Uncertainty in Artificial Intelligence (UAI2007)
- Published
- 2012
31. Computational rationality: A converging paradigm for intelligence in brains, minds, and machines
- Author
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Gershman, Samuel J., Horvitz, Eric J., and Tenenbaum, Joshua B.
- Published
- 2015
32. REVIEW: Computational rationality: A converging paradigm for intelligence in brains, minds, and machines
- Author
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Gershman, Samuel J., Horvitz, Eric J., and Tenenbaum, Joshua B.
- Published
- 2015
- Full Text
- View/download PDF
33. Dynamic construction and refinement of utility-based categorization models
- Author
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Poh, Kim Leng, Fehling, Michael R., and Horvitz, Eric J.
- Subjects
Decision-making -- Models ,Artificial intelligence -- Research ,Decision theory -- Research ,Knowledge-based systems -- Research - Abstract
The actions taken by an automated decision-making agent can be enhanced by including mechanisms that enable the agent to categorize concepts effectively. We pose a utility-based approach to categorization based on the idea that categorization should be carried out in the service of action. The choice of concepts made by a decision maker is critical in the effective selection of actions under resource constraints. This perspective is in contrast to classical and similarity-based approaches which seek completeness in concept description with respect to shared properties rather than the effectiveness of decision making. We propose a decision-theoretic framework for utility-based categorization which involves reasoning about alternative categorization models consisting of sets of interrelated concepts at varying levels of abstraction. Categorization models that are too abstract may overlook details that are critical for selecting the most appropriate actions. Categorization models that are too detailed, however, may be too expensive to process and may contain information that is irrelevant for selecting the best action. Categorization models are therefore evaluated on the basis of the expected value of their recommended action, taking into account the associated resource cost required for their evaluation. A knowledge representation scheme, known as probabilistic conceptual networks, has been developed to support the dynamic construction of models at varying levels of abstraction. This knowledge representation scheme combines the formalisms of influence diagrams from decision analysis and inheritance/abstraction hierarchies from artificial intelligence. We also propose an incremental approach to categorical reasoning which involves the dynamic construction and refinement of categorization models. A model may be improved by making the concepts under consideration either more abstract or more detailed. The expected increase in value of the recommended action may be used to direct and control the direction of model improvements. By applying decision-theoretic control of model refinement, a resource-constrained actor iteratively decides between continuing to improve the current level of abstraction in the model, or to act immediately. Index Terms - Model construction, categorization, knowledge representation, control of reasoning.
- Published
- 1994
34. A Synthesis of Logical and Probabilistic Reasoning for Program Understanding and Debugging
- Author
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Burnell, Lisa J., primary and Horvitz, Eric J., additional
- Published
- 1993
- Full Text
- View/download PDF
35. Reasoning about the Value of Decision-Model Refinement: Methods and Application
- Author
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Poh, Kim Leng, primary and Horvitz, Eric J., additional
- Published
- 1993
- Full Text
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36. Utility-Based Abstraction and Categorization
- Author
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Horvitz, Eric J., primary and Klein, Adrian C., additional
- Published
- 1993
- Full Text
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37. Rise of Concerns about AI: Reflections and Directions.
- Author
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Dietterich, Thomas G. and Horvitz, Eric J.
- Subjects
- *
ARTIFICIAL intelligence & society , *ARTIFICIAL intelligence , *AUTOMATIC pilot (Airplanes) , *DRIVERLESS cars , *HUMAN-machine relationship , *ECONOMICS - Abstract
The article focuses on questions and the author's concerns about Artificial Intelligence (AI) and the impact it may have on human beings and society. Topics discussed include a self-driving car that information technology company Google is developing, human and machine interactions challenges of aircraft autopilot systems, and the possible negative effects of AI technology on wages. Another topic is the possibility of collaboration between AI systems and human beings to solve complex programs. The author expresses the opinion that more study is needed to ensure effectiveness and safety of AI in safety-critical functions and to examine the impact of AI on the economy.
- Published
- 2015
- Full Text
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38. Reflections on Challenges and Promises of Mixed-Initiative Interaction
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Horvitz, Eric J.
- Abstract
Research on mixed-initiative interaction and assistance is still in its infancy but is poised to blossom into a wellspring of innovation that promise to change the way we work with computing systems -- and the way that computing systems work with us. I share reflections about the opportunities ahead for developing computational systems with the ability to engage people in a deeply collaborative manner, founded on their ability to support fluid mixed-initiative problem solving.
- Published
- 2007
39. The Myth of Modularity in Rule-Based Systems for Reasoning with Uncertainty
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Heckerman, David E., primary and Horvitz, Eric J., additional
- Published
- 1988
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40. Characterizing and predicting postpartum depression from shared facebook data
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De Choudhury, Munmun, primary, Counts, Scott, additional, Horvitz, Eric J., additional, and Hoff, Aaron, additional
- Published
- 2014
- Full Text
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41. AAAI 2002 Workshops
- Author
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Blake, Brian, Haigh, Karen, Hexmoor, Henry, Falcone, Rino, Soh, Leen-Kiat, Baral, Chitta, McIlraith, Sheila, Gmytrasiewicz, Piotr, Parsons, Simon, Malaka, Rainer, Krueger, Antonio, Bouquet, Paolo, Smart, Bill, Kurumantani, Koichi, Pease, Adam, Brenner, Michael, desJardins, Marie, Junker, Ulrich, Delgrande, Jim, Doyle, Jon, Rossi, Francesca, Schaub, Torsten, Gomes, Carla, Walsh, Toby, Guo, Haipeng, Horvitz, Eric J., Ide, Nancy, Welty, Chris, Anger, Frank D., Guegen, Hans W., and Ligozat, Gerald
- Abstract
The Association for the Advancement of Artificial Intelligence (AAAI) presented the AAAI-02 Workshop Program on Sunday and Monday, 28-29 July 2002 at the Shaw Convention Center in Edmonton, Alberta, Canada. The AAAI-02 workshop program included 18 workshops covering a wide range of topics in AI. The workshops were Agent-Based Technologies for B2B Electronic-Commerce; Automation as a Caregiver: The Role of Intelligent Technology in Elder Care; Autonomy, Delegation, and Control: From Interagent to Groups; Coalition Formation in Dynamic Multiagent Environments; Cognitive Robotics; Game-Theoretic and Decision-Theoretic Agents; Intelligent Service Integration; Intelligent Situation-Aware Media and Presentations; Meaning Negotiation; Multiagent Modeling and Simulation of Economic Systems; Ontologies and the Semantic Web; Planning with and for Multiagent Systems; Preferences in AI and CP: Symbolic Approaches; Probabilistic Approaches in Search; Real-Time Decision Support and Diagnosis Systems; Semantic Web Meets Language Resources; and Spatial and Temporal Reasoning.
- Published
- 2002
42. Decision Analysis and Expert Systems
- Author
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Henrion, Max, Breese, John S., and Horvitz, Eric J.
- Abstract
Decision analysis and expert systems are technologies intended to support human reasoning and decision making by formalizing expert knowledge so that it is amenable to mechanized reasoning methods. Despite some common goals, these two paradigms have evolved divergently, with fundamental differences in principle and practice. Recent recognition of the deficiencies of traditional AI techniques for treating uncertainty, coupled with the development of belief nets and influence diagrams, is stimulating renewed enthusiasm among AI researchers in probabilistic reasoning and decision analysis. We present the key ideas of decision analysis and review recent research and applications that aim toward a marriage of these two paradigms. This work combines decision-analytic methods for structuring and encoding uncertain knowledge and preferences with computational techniques from AI for knowledge representation, inference, and explanation. We end by outlining remaining research issues to fully develop the potential of this enterprise.
- Published
- 1991
43. Automated Decision-Analytic Diagnosis of Thermal Performance in Gas Turbines
- Author
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Breese, John S., primary, Horvitz, Eric J., additional, Peot, Mark A., additional, Gay, Rodney, additional, and Quentin, George H., additional
- Published
- 1992
- Full Text
- View/download PDF
44. The Pathfinder System
- Author
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Heckerman, David E., Horvitz, Eric J., and Nathwani, Bharat N.
- Subjects
Decision Theory. Applications of Belief Networks - Abstract
We review highlights of our research on Pathfinder, a decision-theoretic expert system for hematopathology diagnosis. We have developed techniques for efficiently acquiring, representing, and reasoning with uncertain biomedical knowledge. Specifically, we have developed a methodology for coping with complex dependencies among findings and disease in pathology. The methodology includes an extension of the belief-network representation called similarity networks. Using this methodology, we have constructed a large probabilistic knowledge base for the domain of lymph-node pathology. We have also developed techniques for improving the clarity of explanations through the use of human-oriented abstractions. Finally, we have conducted a formal evaluation of Pathfinder's diagnostic accuracy.
- Published
- 1989
45. Review of Heuristics: Intelligent Search Strategies for Computer Problem Solving
- Author
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Levitt, Tod S. and Horvitz, Eric J.
- Abstract
As a book about search, it is thorough, at the state of the art, and contains expositions that will delight the expert with their clarity and depth. However, it is not, per se, a book about AI (nor was it intended to be) or about the history, philosophy, or cognitive aspects of heuristic knowledge.
- Published
- 1987
46. Heuristic Abstraction in the Decision-Theoretic Pathfinder System*
- Author
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Horvitz, Eric J., Heckerman, David E., Ng, Keung-Chi, and Nathwani, Bharat N.
- Subjects
Decision Theory. New Theoretic Developments - Abstract
A criticism of diagnostic systems, which are based on the formal foundations of probability and utility, is that their reasoning strategies and recommendations are inflexible and unnatural. We have developed a facility that increases the flexibility of normative reasoning systems by providing multiple human-oriented perspectives on diagnostic problem solving. The method endows a system with the ability to reason about arbitrary classes of diagnostic entities and to control the level of abstraction at which inference occurs. The techniques have been integrated into Pathfinder, an expert system that performs hematopathology diagnosis. We explain the background and approach that we have taken, and describe how we use the techniques in Pathfinder to modulate information- and decision-theoretic reasoning with strategic scripts that are familiar to physicians.
- Published
- 1989
47. Decision theory in expert systems and artificial intelligence
- Author
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Horvitz, Eric J., primary, Breese, John S., additional, and Henrion, Max, additional
- Published
- 1988
- Full Text
- View/download PDF
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