3,409 research outputs found
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
Some Experiments with Real-Time Decision Algorithms
Real-time Decision algorithms are a class of incremental resource-bounded
[Horvitz, 89] or anytime [Dean, 93] algorithms for evaluating influence
diagrams. We present a test domain for real-time decision algorithms, and the
results of experiments with several Real-time Decision Algorithms in this
domain. The results demonstrate high performance for two algorithms, a
decision-evaluation variant of Incremental Probabilisitic Inference [D'Ambrosio
93] and a variant of an algorithm suggested by Goldszmidt, [Goldszmidt, 95],
PK-reduced. We discuss the implications of these experimental results and
explore the broader applicability of these algorithms.Comment: Appears in Proceedings of the Twelfth Conference on Uncertainty in
Artificial Intelligence (UAI1996
An Anytime Algorithm for Task and Motion MDPs
Integrated task and motion planning has emerged as a challenging problem in
sequential decision making, where a robot needs to compute high-level strategy
and low-level motion plans for solving complex tasks. While high-level
strategies require decision making over longer time-horizons and scales, their
feasibility depends on low-level constraints based upon the geometries and
continuous dynamics of the environment. The hybrid nature of this problem makes
it difficult to scale; most existing approaches focus on deterministic, fully
observable scenarios. We present a new approach where the high-level decision
problem occurs in a stochastic setting and can be modeled as a Markov decision
process. In contrast to prior efforts, we show that complete MDP policies, or
contingent behaviors, can be computed effectively in an anytime fashion. Our
algorithm continuously improves the quality of the solution and is guaranteed
to be probabilistically complete. We evaluate the performance of our approach
on a challenging, realistic test problem: autonomous aircraft inspection. Our
results show that we can effectively compute consistent task and motion
policies for the most likely execution-time outcomes using only a fraction of
the computation required to develop the complete task and motion policy.Comment: 7 pages, 4 figure
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
Anytime Integrated Task and Motion Policies for Stochastic Environments
In order to solve complex, long-horizon tasks, intelligent robots need to
carry out high-level, abstract planning and reasoning in conjunction with
motion planning. However, abstract models are typically lossy and plans or
policies computed using them can be unexecutable. These problems are
exacerbated in stochastic situations where the robot needs to reason about, and
plan for multiple contingencies.
We present a new approach for integrated task and motion planning in
stochastic settings. In contrast to prior work in this direction, we show that
our approach can effectively compute integrated task and motion policies whose
branching structures encoding agent behaviors handling multiple execution-time
contingencies. We prove that our algorithm is probabilistically complete and
can compute feasible solution policies in an anytime fashion so that the
probability of encountering an unresolved contingency decreases over time.
Empirical results on a set of challenging problems show the utility and scope
of our methods
Max-Entropy Feed-Forward Clustering Neural Network
The outputs of non-linear feed-forward neural network are positive, which
could be treated as probability when they are normalized to one. If we take
Entropy-Based Principle into consideration, the outputs for each sample could
be represented as the distribution of this sample for different clusters.
Entropy-Based Principle is the principle with which we could estimate the
unknown distribution under some limited conditions. As this paper defines two
processes in Feed-Forward Neural Network, our limited condition is the
abstracted features of samples which are worked out in the abstraction process.
And the final outputs are the probability distribution for different clusters
in the clustering process. As Entropy-Based Principle is considered into the
feed-forward neural network, a clustering method is born. We have conducted
some experiments on six open UCI datasets, comparing with a few baselines and
applied purity as the measurement . The results illustrate that our method
outperforms all the other baselines that are most popular clustering methods.Comment: This paper has been published in ICANN 201
Computational Complexity Reduction for BN2O Networks Using Similarity of States
Although probabilistic inference in a general Bayesian belief network is an
NP-hard problem, computation time for inference can be reduced in most
practical cases by exploiting domain knowledge and by making approximations in
the knowledge representation. In this paper we introduce the property of
similarity of states and a new method for approximate knowledge representation
and inference which is based on this property. We define two or more states of
a node to be similar when the ratio of their probabilities, the likelihood
ratio, does not depend on the instantiations of the other nodes in the network.
We show that the similarity of states exposes redundancies in the joint
probability distribution which can be exploited to reduce the computation time
of probabilistic inference in networks with multiple similar states, and that
the computational complexity in the networks with exponentially many similar
states might be polynomial. We demonstrate our ideas on the example of a BN2O
network -- a two layer network often used in diagnostic problems -- by reducing
it to a very close network with multiple similar states. We show that the
answers to practical queries converge very fast to the answers obtained with
the original network. The maximum error is as low as 5% for models that require
only 10% of the computation time needed by the original BN2O model.Comment: Appears in Proceedings of the Twelfth Conference on Uncertainty in
Artificial Intelligence (UAI1996
Abstraction in Belief Networks: The Role of Intermediate States in Diagnostic Reasoning
Bayesian belief networks are bing increasingly used as a knowledge
representation for diagnostic reasoning. One simple method for conducting
diagnostic reasoning is to represent system faults and observations only. In
this paper, we investigate how having intermediate nodes-nodes other than fault
and observation nodes affects the diagnostic performance of a Bayesian belief
network. We conducted a series of experiments on a set of real belief networks
for medical diagnosis in liver and bile disease. We compared the effects on
diagnostic performance of a two-level network consisting just of disease and
finding nodes with that of a network which models intermediate
pathophysiological disease states as well. We provide some theoretical evidence
for differences observed between the abstracted two-level network and the full
network.Comment: Appears in Proceedings of the Eleventh Conference on Uncertainty in
Artificial Intelligence (UAI1995
Fast Belief Update Using Order-of-Magnitude Probabilities
We present an algorithm, called Predict, for updating beliefs in causal
networks quantified with order-of-magnitude probabilities. The algorithm takes
advantage of both the structure and the quantification of the network and
presents a polynomial asymptotic complexity. Predict exhibits a conservative
behavior in that it is always sound but not always complete. We provide
sufficient conditions for completeness and present algorithms for testing these
conditions and for computing a complete set of plausible values. We propose
Predict as an efficient method to estimate probabilistic values and illustrate
its use in conjunction with two known algorithms for probabilistic inference.
Finally, we describe an application of Predict to plan evaluation, present
experimental results, and discuss issues regarding its use with conditional
logics of belief, and in the characterization of irrelevance.Comment: Appears in Proceedings of the Eleventh Conference on Uncertainty in
Artificial Intelligence (UAI1995
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
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