31,326 research outputs found
Decision Making with Interval Influence Diagrams
In previous work (Fertig and Breese, 1989; Fertig and Breese, 1990) we
defined a mechanism for performing probabilistic reasoning in influence
diagrams using interval rather than point-valued probabilities. In this paper
we extend these procedures to incorporate decision nodes and interval-valued
value functions in the diagram. We derive the procedures for chance node
removal (calculating expected value) and decision node removal (optimization)
in influence diagrams where lower bounds on probabilities are stored at each
chance node and interval bounds are stored on the value function associated
with the diagram's value node. The output of the algorithm are a set of
admissible alternatives for each decision variable and a set of bounds on
expected value based on the imprecision in the input. The procedure can be
viewed as an approximation to a full e-dimensional sensitivity analysis where n
are the number of imprecise probability distributions in the input. We show the
transformations are optimal and sound. The performance of the algorithm on an
influence diagrams is investigated and compared to an exact algorithm.Comment: Appears in Proceedings of the Sixth Conference on Uncertainty in
Artificial Intelligence (UAI1990
A Graph-Theoretic Analysis of Information Value
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
State-space Abstraction for Anytime Evaluation of Probabilistic Networks
One important factor determining the computational complexity of evaluating a
probabilistic network is the cardinality of the state spaces of the nodes. By
varying the granularity of the state spaces, one can trade off accuracy in the
result for computational efficiency. We present an anytime procedure for
approximate evaluation of probabilistic networks based on this idea. On
application to some simple networks, the procedure exhibits a smooth
improvement in approximation quality as computation time increases. This
suggests that state-space abstraction is one more useful control parameter for
designing real-time probabilistic reasoners.Comment: Appears in Proceedings of the Tenth Conference on Uncertainty in
Artificial Intelligence (UAI1994
Conversation as Action Under Uncertainty
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
Bayesian Structure Learning by Recursive Bootstrap
We address the problem of Bayesian structure learning for domains with
hundreds of variables by employing non-parametric bootstrap, recursively. We
propose a method that covers both model averaging and model selection in the
same framework. The proposed method deals with the main weakness of
constraint-based learning---sensitivity to errors in the independence
tests---by a novel way of combining bootstrap with constraint-based learning.
Essentially, we provide an algorithm for learning a tree, in which each node
represents a scored CPDAG for a subset of variables and the level of the node
corresponds to the maximal order of conditional independencies that are encoded
in the graph. As higher order independencies are tested in deeper recursive
calls, they benefit from more bootstrap samples, and therefore more resistant
to the curse-of-dimensionality. Moreover, the re-use of stable low order
independencies allows greater computational efficiency. We also provide an
algorithm for sampling CPDAGs efficiently from their posterior given the
learned tree. We empirically demonstrate that the proposed algorithm scales
well to hundreds of variables, and learns better MAP models and more reliable
causal relationships between variables, than other state-of-the-art-methods
The Automated Mapping of Plans for Plan Recognition
To coordinate with other agents in its environment, an agent needs models of
what the other agents are trying to do. When communication is impossible or
expensive, this information must be acquired indirectly via plan recognition.
Typical approaches to plan recognition start with a specification of the
possible plans the other agents may be following, and develop special
techniques for discriminating among the possibilities. Perhaps more desirable
would be a uniform procedure for mapping plans to general structures supporting
inference based on uncertain and incomplete observations. In this paper, we
describe a set of methods for converting plans represented in a flexible
procedural language to observation models represented as probabilistic belief
networks.Comment: Appears in Proceedings of the Tenth Conference on Uncertainty in
Artificial Intelligence (UAI1994
Time-Dependent Utility and Action Under Uncertainty
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
Independence of Causal Influence and Clique Tree Propagation
This paper explores the role of independence of causal influence (ICI) in
Bayesian network inference. ICI allows one to factorize a conditional
probability table into smaller pieces. We describe a method for exploiting the
factorization in clique tree propagation (CTP) - the state-of-the-art exact
inference algorithm for Bayesian networks. We also present empirical results
showing that the resulting algorithm is significantly more efficient than the
combination of CTP and previous techniques for exploiting ICI.Comment: Appears in Proceedings of the Thirteenth Conference on Uncertainty in
Artificial Intelligence (UAI1997
Multiplicative Factorization of Noisy-Max
The noisy-or and its generalization noisy-max have been utilized to reduce
the complexity of knowledge acquisition. In this paper, we present a new
representation of noisy-max that allows for efficient inference in general
Bayesian networks. Empirical studies show that our method is capable of
computing queries in well-known large medical networks, QMR-DT and CPCS, for
which no previous exact inference method has been shown to perform well.Comment: Appears in Proceedings of the Fifteenth Conference on Uncertainty in
Artificial Intelligence (UAI1999
Management of Uncertainty in the Multi-Level Monitoring and Diagnosis of the Time of Flight Scintillation Array
We present a general architecture for the monitoring and diagnosis of large
scale sensor-based systems with real time diagnostic constraints. This
architecture is multileveled, combining a single monitoring level based on
statistical methods with two model based diagnostic levels. At each level,
sources of uncertainty are identified, and integrated methodologies for
uncertainty management are developed. The general architecture was applied to
the monitoring and diagnosis of a specific nuclear physics detector at Lawrence
Berkeley National Laboratory that contained approximately 5000 components and
produced over 500 channels of output data. The general architecture is
scalable, and work is ongoing to apply it to detector systems one and two
orders of magnitude more complex.Comment: Appears in Proceedings of the Seventh Conference on Uncertainty in
Artificial Intelligence (UAI1991
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