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327 results on '"KORB, KEVIN B."'

1. Causal KL: Evaluating Causal Discovery

Author

O'Donnell, Rodney T., Korb, Kevin B., and Allison, Lloyd

Subjects
Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

The two most commonly used criteria for assessing causal model discovery with artificial data are edit-distance and Kullback-Leibler divergence, measured from the true model to the learned model. Both of these metrics maximally reward the true model. However, we argue that they are both insufficiently discriminating in judging the relative merits of false models. Edit distance, for example, fails to distinguish between strong and weak probabilistic dependencies. KL divergence, on the other hand, rewards equally all statistically equivalent models, regardless of their different causal claims. We propose an augmented KL divergence, which we call Causal KL (CKL), which takes into account causal relationships which distinguish between observationally equivalent models. Results are presented for three variants of CKL, showing that Causal KL works well in practice., Comment: 26 pages

Published
2021

2. Markov Blanket Discovery using Minimum Message Length

Author

Li, Yang, Korb, Kevin B, and Allison, Lloyd

Subjects
Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

Causal discovery automates the learning of causal Bayesian networks from data and has been of active interest from their beginning. With the sourcing of large data sets off the internet, interest in scaling up to very large data sets has grown. One approach to this is to parallelize search using Markov Blanket (MB) discovery as a first step, followed by a process of combining MBs in a global causal model. We develop and explore three new methods of MB discovery using Minimum Message Length (MML) and compare them empirically to the best existing methods, whether developed specifically as MB discovery or as feature selection. Our best MML method is consistently competitive and has some advantageous features.

Published
2021

3. BARD: A structured technique for group elicitation of Bayesian networks to support analytic reasoning

Author

Nicholson, Ann E., Korb, Kevin B., Nyberg, Erik P., Wybrow, Michael, Zukerman, Ingrid, Mascaro, Steven, Thakur, Shreshth, Alvandi, Abraham Oshni, Riley, Jeff, Pearson, Ross, Morris, Shane, Herrmann, Matthieu, Azad, A. K. M., Bolger, Fergus, Hahn, Ulrike, and Lagnado, David

Subjects
Computer Science - Artificial Intelligence
Abstract

In many complex, real-world situations, problem solving and decision making require effective reasoning about causation and uncertainty. However, human reasoning in these cases is prone to confusion and error. Bayesian networks (BNs) are an artificial intelligence technology that models uncertain situations, supporting probabilistic and causal reasoning and decision making. However, to date, BN methodologies and software require significant upfront training, do not provide much guidance on the model building process, and do not support collaboratively building BNs. BARD (Bayesian ARgumentation via Delphi) is both a methodology and an expert system that utilises (1) BNs as the underlying structured representations for better argument analysis, (2) a multi-user web-based software platform and Delphi-style social processes to assist with collaboration, and (3) short, high-quality e-courses on demand, a highly structured process to guide BN construction, and a variety of helpful tools to assist in building and reasoning with BNs, including an automated explanation tool to assist effective report writing. The result is an end-to-end online platform, with associated online training, for groups without prior BN expertise to understand and analyse a problem, build a model of its underlying probabilistic causal structure, validate and reason with the causal model, and use it to produce a written analytic report. Initial experimental results demonstrate that BARD aids in problem solving, reasoning and collaboration.

Published
2020
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6. Latent Variable Discovery Using Dependency Patterns

Author

Zhang, Xuhui, Korb, Kevin B., Nicholson, Ann E., and Mascaro, Steven

Subjects
Computer Science - Artificial Intelligence, Statistics - Machine Learning
Abstract

The causal discovery of Bayesian networks is an active and important research area, and it is based upon searching the space of causal models for those which can best explain a pattern of probabilistic dependencies shown in the data. However, some of those dependencies are generated by causal structures involving variables which have not been measured, i.e., latent variables. Some such patterns of dependency "reveal" themselves, in that no model based solely upon the observed variables can explain them as well as a model using a latent variable. That is what latent variable discovery is based upon. Here we did a search for finding them systematically, so that they may be applied in latent variable discovery in a more rigorous fashion.

Published
2016

8. Learning Bayesian Networks with Restricted Causal Interactions

Author

Neil, Julian R., Wallace, Chris S., and Korb, Kevin B.

Subjects
Computer Science - Artificial Intelligence, Computer Science - Learning, Statistics - Machine Learning
Abstract

A major problem for the learning of Bayesian networks (BNs) is the exponential number of parameters needed for conditional probability tables. Recent research reduces this complexity by modeling local structure in the probability tables. We examine the use of log-linear local models. While log-linear models in this context are not new (Whittaker, 1990; Buntine, 1991; Neal, 1992; Heckerman and Meek, 1997), for structure learning they are generally subsumed under a naive Bayes model. We describe an alternative interpretation, and use a Minimum Message Length (MML) (Wallace, 1987) metric for structure learning of networks exhibiting causal independence, which we term first-order networks (FONs). We also investigate local model selection on a node-by-node basis., Comment: Appears in Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence (UAI1999)

Published
2013

9. Bayesian Poker

Author

Korb, Kevin B., Nicholson, Ann, and Jitnah, Nathalie

Subjects
Computer Science - Artificial Intelligence, Computer Science - Computer Science and Game Theory
Abstract

Poker is ideal for testing automated reasoning under uncertainty. It introduces uncertainty both by physical randomization and by incomplete information about opponents hands.Another source OF uncertainty IS the limited information available TO construct psychological models OF opponents, their tendencies TO bluff, play conservatively, reveal weakness, etc. AND the relation BETWEEN their hand strengths AND betting behaviour. ALL OF these uncertainties must be assessed accurately AND combined effectively FOR ANY reasonable LEVEL OF skill IN the game TO be achieved, since good decision making IS highly sensitive TO those tasks.We describe our Bayesian Poker Program(BPP), which uses a Bayesian network TO model the programs poker hand, the opponents hand AND the opponents playing behaviour conditioned upon the hand, and betting curves which govern play given a probability of winning. The history of play with opponents is used to improve BPPs understanding OF their behaviour.We compare BPP experimentally WITH : a simple RULE - based system; a program which depends exclusively ON hand probabilities(i.e., without opponent modeling); AND WITH human players.BPP has shown itself TO be an effective player against ALL these opponents, barring the better humans.We also sketch out SOME likely ways OF improving play., Comment: Appears in Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence (UAI1999)

Published
2013

11. Applying Dependency Patterns in Causal Discovery of Latent Variable Models

Author

Zhang, Xuhui, Korb, Kevin B., Nicholson, Ann E., Mascaro, Steven, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Wagner, Markus, editor, Li, Xiaodong, editor, and Hendtlass, Tim, editor

Published
2017
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14. Intrinsic Learning of Dynamic Bayesian Networks

Author

Black, Alex, Korb, Kevin B., Nicholson, Ann E., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Kobsa, Alfred, Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Goebel, Randy, Series editor, Tanaka, Yuzuru, Series editor, Wahlster, Wolfgang, Series editor, Siekmann, Jörg, Series editor, Pham, Duc-Nghia, editor, and Park, Seong-Bae, editor

Published
2014
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15. Deciding What Not to Say: An Attentional-Probabilistic Approach to Argument Presentation

Author

McConachy, Richard, Korb, Kevin B., and Zukerman, Ingrid

Abstract

Effective arguments must be presented in a cohesive manner: simple collections of believed premises and connecting inferences supporting a goal may not persuade the recipient if they are not well ordered. We use semantic activation and Bayesian propagation in a user model to simulate the effect of presenting an argument generated by our system, NAG, to the user. This simulation is used to select a strategy for presenting the argument to the user. The simulation also identifies superfluous lines of reasoning that may be removed, and enables NAG to determine how multiple subarguments for points should be presented, e.g., as multiple individual supports or collectively. A greedy algorithm is then used to apply probabilistic pruning and semantic suppression to further simplify the argument. Probabilistic pruning removes unnecessary premises from the argument. Semantic suppression is used to select portions of the argument which are within the user's focus of attention, and which are also readily inferred, and hence can be left implicit without damaging the effectiveness of the argument.

Published
1998

16. A Cognitive Model of Argumentation

Author

Korb, Kevin B., McConachy, Richard, and Zukerman, Ingrid

Abstract

In order to argue effectively one must have a grasp of both the normative strength of the inferences that come into play and the effect that the proposed inferences will have on the audience. In this paper we describe a program, NAG (Nice Argument Generator), that attempts to generate arguments that are both persuasive and correct. To do so NAG incorporates two models: a normative model, forjudging the normative correctness of an argument, and a user model, for judging the persuasive effect of the same argument upon the user. The user model incorporates some of the common errors humans make when reasoning. In order to limit the scope of its reasoning during argument evaluation and generation NAG explicitly simulates attentional processes in both the user and the normative models.

Published
1997

19. Causal Discovery of Dynamic Bayesian Networks

Author

Pérez-Ariza, Cora Beatriz, Nicholson, Ann E., Korb, Kevin B., Mascaro, Steven, Hu, Chao Heng, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Thielscher, Michael, editor, and Zhang, Dongmo, editor

Published
2012
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23. Using Misperception to Counteract Noise in the Iterated Prisoner’s Dilemma

Author

Brumley, Lachlan, Korb, Kevin B., Kopp, Carlo, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Korb, Kevin, editor, Randall, Marcus, editor, and Hendtlass, Tim, editor

Published
2009
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24. A New Definition of Creativity

Author

Dorin, Alan, Korb, Kevin B., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Korb, Kevin, editor, Randall, Marcus, editor, and Hendtlass, Tim, editor

Published
2009
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29. Learning Hybrid Bayesian Networks by MML

Author

O’Donnell, Rodney T., Allison, Lloyd, Korb, Kevin B., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, Sattar, Abdul, editor, and Kang, Byeong-ho, editor

Published
2006
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30. An Information-Theoretic Causal Power Theory

Author

Hope, Lucas R., Korb, Kevin B., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, Zhang, Shichao, editor, and Jarvis, Ray, editor

Published
2005
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31. A Bayesian Metric for Evaluating Machine Learning Algorithms

Author

Hope, Lucas R., Korb, Kevin B., Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, Webb, Geoffrey I., editor, and Yu, Xinghuo, editor

Published
2005
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32. Parameterising Bayesian Networks

Author

Woodberry, Owen, Nicholson, Ann E., Korb, Kevin B., Pollino, Carmel, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, Webb, Geoffrey I., editor, and Yu, Xinghuo, editor

Published
2005
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33. Varieties of Causal Intervention

Author

Korb, Kevin B., Hope, Lucas R., Nicholson, Ann E., Axnick, Karl, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, Zhang, Chengqi, editor, W. Guesgen, Hans, editor, and Yeap, Wai-Kiang, editor

Published
2004
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34. Bayesian Information Reward

Author

Hope, Lucas R., Korb, Kevin B., Goos, G., editor, Hartmanis, J., editor, van Leeuwen, J., editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, McKay, Bob, editor, and Slaney, John, editor

Published
2002
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35. Seabreeze Prediction Using Bayesian Networks

Author

Kennett, Russell J., Korb, Kevin B., Nicholson, Ann E., Goos, G., editor, Hartmanis, J., editor, van Leeuwen, J., editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, Cheung, David, editor, Williams, Graham J., editor, and Li, Qing, editor

Published
2001
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36. Suicide as an Evolutionarily Stable Strategy

Author

Mascaro, Steven, Korb, Kevin B., Nicholson, Ann E., Goos, G., editor, Hartmanis, J., editor, van Leeuwen, J., editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, Kelemen, Jozef, editor, and Sosík, Petr, editor

Published
2001
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46. Stephen Jay Gould on Intelligence.

Author

Korb, Kevin B.

Abstract

Critiques ideas expressed by Gould in "The Mismeasure of Man." Agrees with Gould that many scientists who studied human intelligence were racist, but disagrees that their work must therefore necessarily be dismissed. Disputes Gould's claim that factor analysts who study human intelligence have reified their factors and that factor analysis is therefore worthless in the study of intelligence. (BC)

Published
1994

50. BARD: A Structured Technique for Group Elicitation of Bayesian Networks to Support Analytic Reasoning

Author

Nyberg, Erik P., primary, Nicholson, Ann E., additional, Korb, Kevin B., additional, Wybrow, Michael, additional, Zukerman, Ingrid, additional, Mascaro, Steven, additional, Thakur, Shreshth, additional, Oshni Alvandi, Abraham, additional, Riley, Jeff, additional, Pearson, Ross, additional, Morris, Shane, additional, Herrmann, Matthieu, additional, Azad, A.K.M., additional, Bolger, Fergus, additional, Hahn, Ulrike, additional, and Lagnado, David, additional

Published
2021
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