386 research outputs found
Optimal Factory Scheduling using Stochastic Dominance A*
We examine a standard factory scheduling problem with stochastic processing
and setup times, minimizing the expectation of the weighted number of tardy
jobs. Because the costs of operators in the schedule are stochastic and
sequence dependent, standard dynamic programming algorithms such as A* may fail
to find the optimal schedule. The SDA* (Stochastic Dominance A*) algorithm
remedies this difficulty by relaxing the pruning condition. We present an
improved state-space search formulation for these problems and discuss the
conditions under which stochastic scheduling problems can be solved optimally
using SDA*. In empirical testing on randomly generated problems, we found that
in 70%, the expected cost of the optimal stochastic solution is lower than that
of the solution derived using a deterministic approximation, with comparable
search effort.Comment: Appears in Proceedings of the Twelfth Conference on Uncertainty in
Artificial Intelligence (UAI1996
Incremental Tradeoff Resolution in Qualitative Probabilistic Networks
Qualitative probabilistic reasoning in a Bayesian network often reveals
tradeoffs: relationships that are ambiguous due to competing qualitative
influences. We present two techniques that combine qualitative and numeric
probabilistic reasoning to resolve such tradeoffs, inferring the qualitative
relationship between nodes in a Bayesian network. The first approach
incrementally marginalizes nodes that contribute to the ambiguous qualitative
relationships. The second approach evaluates approximate Bayesian networks for
bounds of probability distributions, and uses these bounds to determinate
qualitative relationships in question. This approach is also incremental in
that the algorithm refines the state spaces of random variables for tighter
bounds until the qualitative relationships are resolved. Both approaches
provide systematic methods for tradeoff resolution at potentially lower
computational cost than application of purely numeric methods.Comment: Appears in Proceedings of the Fourteenth Conference on Uncertainty in
Artificial Intelligence (UAI1998
Graphical Representations of Consensus Belief
Graphical models based on conditional independence support concise encodings
of the subjective belief of a single agent. A natural question is whether the
consensus belief of a group of agents can be represented with equal parsimony.
We prove, under relatively mild assumptions, that even if everyone agrees on a
common graph topology, no method of combining beliefs can maintain that
structure. Even weaker conditions rule out local aggregation within conditional
probability tables. On a more positive note, we show that if probabilities are
combined with the logarithmic opinion pool (LogOP), then commonly held Markov
independencies are maintained. This suggests a straightforward procedure for
constructing a consensus Markov network. We describe an algorithm for computing
the LogOP with time complexity comparable to that of exact Bayesian inference.Comment: Appears in Proceedings of the Fifteenth Conference on Uncertainty in
Artificial Intelligence (UAI1999
Toward a Market Model for Bayesian Inference
We present a methodology for representing probabilistic relationships in a
general-equilibrium economic model. Specifically, we define a precise mapping
from a Bayesian network with binary nodes to a market price system where
consumers and producers trade in uncertain propositions. We demonstrate the
correspondence between the equilibrium prices of goods in this economy and the
probabilities represented by the Bayesian network. A computational market model
such as this may provide a useful framework for investigations of belief
aggregation, distributed probabilistic inference, resource allocation under
uncertainty, and other problems of decentralized uncertainty.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
Probabilistic State-Dependent Grammars for Plan Recognition
Techniques for plan recognition under uncertainty require a stochastic model
of the plan-generation process. We introduce Probabilistic State-Dependent
Grammars (PSDGs) to represent an agent's plan-generation process. The PSDG
language model extends probabilistic context-free grammars (PCFGs) by allowing
production probabilities to depend on an explicit model of the planning agent's
internal and external state. Given a PSDG description of the plan-generation
process, we can then use inference algorithms that exploit the particular
independence properties of the PSDG language to efficiently answer
plan-recognition queries. The combination of the PSDG language model and
inference algorithms extends the range of plan-recognition domains for which
practical probabilistic inference is possible, as illustrated by applications
in traffic monitoring and air combat.Comment: Appears in Proceedings of the Sixteenth Conference on Uncertainty in
Artificial Intelligence (UAI2000
Accounting for Context in Plan Recognition, with Application to Traffic Monitoring
Typical approaches to plan recognition start from a representation of an
agent's possible plans, and reason evidentially from observations of the
agent's actions to assess the plausibility of the various candidates. A more
expansive view of the task (consistent with some prior work) accounts for the
context in which the plan was generated, the mental state and planning process
of the agent, and consequences of the agent's actions in the world. We present
a general Bayesian framework encompassing this view, and focus on how context
can be exploited in plan recognition. We demonstrate the approach on a problem
in traffic monitoring, where the objective is to induce the plan of the driver
from observation of vehicle movements. Starting from a model of how the driver
generates plans, we show how the highway context can appropriately influence
the recognizer's interpretation of observed driver behavior.Comment: Appears in Proceedings of the Eleventh Conference on Uncertainty in
Artificial Intelligence (UAI1995
Self-Confirming Price Prediction Strategies for Simultaneous One-Shot Auctions
Bidding in simultaneous auctions is challenging because an agent's value for
a good in one auction may depend on the uncertain outcome of other auctions:
the so-called exposure problem. Given the gap in understanding of general
simultaneous auction games, previous works have tackled this problem with
heuristic strategies that employ probabilistic price predictions. We define a
concept of self-confirming prices, and show that within an independent private
value model, Bayes-Nash equilibrium can be fully characterized as a profile of
optimal price prediction strategies with self-confirming predictions. We
exhibit practical procedures to compute approximately optimal bids given a
probabilistic price prediction, and near self-confirming price predictions
given a price-prediction strategy. An extensive empirical game-theoretic
analysis demonstrates that self-confirming price prediction strategies are
effective in simultaneous auction games with both complementary and
substitutable preference structures.Comment: Appears in Proceedings of the Twenty-Eighth Conference on Uncertainty
in Artificial Intelligence (UAI2012
Path Planning under Time-Dependent Uncertainty
Standard algorithms for finding the shortest path in a graph require that the
cost of a path be additive in edge costs, and typically assume that costs are
deterministic. We consider the problem of uncertain edge costs, with potential
probabilistic dependencies among the costs. Although these dependencies violate
the standard dynamic-programming decomposition, we identify a weaker stochastic
consistency condition that justifies a generalized dynamic-programming approach
based on stochastic dominance. We present a revised path-planning algorithm and
prove that it produces optimal paths under time-dependent uncertain costs. We
test the algorithm by applying it to a model of stochastic bus networks, and
present empirical performance results comparing it to some alternatives.
Finally, we consider extensions of these concepts to a more general class of
problems of heuristic search under uncertainty.Comment: Appears in Proceedings of the Eleventh Conference on Uncertainty in
Artificial Intelligence (UAI1995
Knowledge Combination in Graphical Multiagent Model
A graphical multiagent model (GMM) represents a joint distribution over the
behavior of a set of agents. One source of knowledge about agents' behavior may
come from gametheoretic analysis, as captured by several graphical game
representations developed in recent years. GMMs generalize this approach to
express arbitrary distributions, based on game descriptions or other sources of
knowledge bearing on beliefs about agent behavior. To illustrate the
flexibility of GMMs, we exhibit game-derived models that allow probabilistic
deviation from equilibrium, as well as models based on heuristic action choice.
We investigate three different methods of integrating these models into a
single model representing the combined knowledge sources. To evaluate the
predictive performance of the combined model, we treat as actual outcome the
behavior produced by a reinforcement learning process. We find that combining
the two knowledge sources, using any of the methods, provides better
predictions than either source alone. Among the combination methods, mixing
data outperforms the opinion pool and direct update methods investigated in
this empirical trial.Comment: Appears in Proceedings of the Twenty-Fourth Conference on Uncertainty
in Artificial Intelligence (UAI2008
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