Adam J. Grove

From Statistics to Beliefs

Fahiem Bacchus, Adam J. Grove, Joseph Y. Halpern

An intelligent agent uses known facts, including statistical knowledge, to assign degrees of belief to assertions it is uncertain about. We investigate three principled techniques for doing this. All three are applications of the principle of indifference, because they assign equal degree …

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Logarithmic-Time Updates and Queries in Probabilistic Networks

Arthur L. Delcher, Adam J. Grove, Simon Kasif, Judea Pearl

In this paper we propose a dynamic data structure that supports efficient algorithms for updating and querying singly connected Bayesian networks (causal trees and polytrees). In the conventional algorithms, new evidence in absorbed in time O(1) and queries are processed in time …

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Probability Estimation in Face of Irrelevant Information

Adam J. Grove, Daphne Koller

In this paper, we consider one aspect of the problem of applying decision theory to the design of agents that learn how to make decisions under uncertainty. This aspect concerns how an agent can estimate probabilities for the possible states of the …

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Generating New Beliefs From Old

Fahiem Bacchus, Adam J. Grove, Joseph Y. Halpern, Daphne Koller

In previous work [BGHK92, BGHK93], we have studied the random-worlds approach -- a particular (and quite powerful) method for generating degrees of belief (i.e., subjective probabilities) from a knowledge base consisting of objective (first-order, statistical, and default) information. But allowing a knowledge …

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Graphical Models for Preference and Utility

Fahiem Bacchus, Adam J. Grove

Probabilistic independence can dramatically simplify the task of eliciting, representing, and computing with probabilities in large domains. A key technique in achieving these benefits is the idea of graphical modeling. We survey existing notions of independence for utility functions in a multi-attribute …

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Probability Update: Conditioning vs. Cross-Entropy

Adam J. Grove, Joseph Y. Halpern

Conditioning is the generally agreed-upon method for updating probability distributions when one learns that an event is certainly true. But it has been argued that we need other rules, in particular the rule of cross-entropy minimization, to handle updates that involve uncertain …

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Learning Bayesian Nets that Perform Well

Russell Greiner, Adam J. Grove, Dale Schuurmans

A Bayesian net (BN) is more than a succinct way to encode a probabilistic distribution; it also corresponds to a function used to answer queries. A BN can therefore be evaluated by the accuracy of the answers it returns. Many algorithms for …

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Learning Bayesian Nets that Perform Well

Russell Greiner, Adam J. Grove, Dale Schuurmans

A Bayesian net (BN) is more than a succinct way to encode a probabilistic distribution; it also corresponds to a function used to answer queries. A BN can therefore be evaluated by the accuracy of the answers it returns. Many algorithms for …

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Updating Sets of Probabilities

Adam J. Grove, Joseph Y. Halpern

There are several well-known justifications for conditioning as the appropriate method for updating a single probability measure, given an observation. However, there is a significant body of work arguing for sets of probability measures, rather than single measures, as a more realistic …

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From Statistical Knowledge Bases to Degrees of Belief

Fahiem Bacchus, Adam J. Grove, Joseph Y. Halpern, Daphne Koller

An intelligent agent will often be uncertain about various properties of its environment, and when acting in that environment it will frequently need to quantify its uncertainty. For example, if the agent wishes to employ the expected-utility paradigm of decision theory to …

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Random worlds and maximum entropy

Adam J. Grove, Joseph Y. Halpern, Daphne Koller

Given a knowledge base theta containing first-order and statistical facts, a principled method, called the random-worlds method, for computing a degree of belief that some phi holds given theta is considered. If the domain has size N, then one can consider all …

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Learning cost-sensitive active classifiers☆☆This extends the short conference paper [19].

Russell Greiner, Adam J. Grove, Dan Roth

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