62,195 research outputs found
Most Relevant Explanation in Bayesian Networks
A major inference task in Bayesian networks is explaining why some variables
are observed in their particular states using a set of target variables.
Existing methods for solving this problem often generate explanations that are
either too simple (underspecified) or too complex (overspecified). In this
paper, we introduce a method called Most Relevant Explanation (MRE) which finds
a partial instantiation of the target variables that maximizes the generalized
Bayes factor (GBF) as the best explanation for the given evidence. Our study
shows that GBF has several theoretical properties that enable MRE to
automatically identify the most relevant target variables in forming its
explanation. In particular, conditional Bayes factor (CBF), defined as the GBF
of a new explanation conditioned on an existing explanation, provides a soft
measure on the degree of relevance of the variables in the new explanation in
explaining the evidence given the existing explanation. As a result, MRE is
able to automatically prune less relevant variables from its explanation. We
also show that CBF is able to capture well the explaining-away phenomenon that
is often represented in Bayesian networks. Moreover, we define two dominance
relations between the candidate solutions and use the relations to generalize
MRE to find a set of top explanations that is both diverse and representative.
Case studies on several benchmark diagnostic Bayesian networks show that MRE is
often able to find explanatory hypotheses that are not only precise but also
concise
Most Relevant Explanation: Properties, Algorithms, and Evaluations
Most Relevant Explanation (MRE) is a method for finding multivariate
explanations for given evidence in Bayesian networks [12]. This paper studies
the theoretical properties of MRE and develops an algorithm for finding
multiple top MRE solutions. Our study shows that MRE relies on an implicit soft
relevance measure in automatically identifying the most relevant target
variables and pruning less relevant variables from an explanation. The soft
measure also enables MRE to capture the intuitive phenomenon of explaining away
encoded in Bayesian networks. Furthermore, our study shows that the solution
space of MRE has a special lattice structure which yields interesting dominance
relations among the solutions. A K-MRE algorithm based on these dominance
relations is developed for generating a set of top solutions that are more
representative. Our empirical results show that MRE methods are promising
approaches for explanation in Bayesian networks.Comment: Appears in Proceedings of the Twenty-Fifth Conference on Uncertainty
in Artificial Intelligence (UAI2009
Evaluating computational models of explanation using human judgments
We evaluate four computational models of explanation in Bayesian networks by
comparing model predictions to human judgments. In two experiments, we present
human participants with causal structures for which the models make divergent
predictions and either solicit the best explanation for an observed event
(Experiment 1) or have participants rate provided explanations for an observed
event (Experiment 2). Across two versions of two causal structures and across
both experiments, we find that the Causal Explanation Tree and Most Relevant
Explanation models provide better fits to human data than either Most Probable
Explanation or Explanation Tree models. We identify strengths and shortcomings
of these models and what they can reveal about human explanation. We conclude
by suggesting the value of pursuing computational and psychological
investigations of explanation in parallel.Comment: Appears in Proceedings of the Twenty-Ninth Conference on Uncertainty
in Artificial Intelligence (UAI2013
A Probabilistic Network of Predicates
Bayesian networks are directed acyclic graphs representing independence
relationships among a set of random variables. A random variable can be
regarded as a set of exhaustive and mutually exclusive propositions. We argue
that there are several drawbacks resulting from the propositional nature and
acyclic structure of Bayesian networks. To remedy these shortcomings, we
propose a probabilistic network where nodes represent unary predicates and
which may contain directed cycles. The proposed representation allows us to
represent domain knowledge in a single static network even though we cannot
determine the instantiations of the predicates before hand. The ability to deal
with cycles also enables us to handle cyclic causal tendencies and to recognize
recursive plans.Comment: Appears in Proceedings of the Eighth Conference on Uncertainty in
Artificial Intelligence (UAI1992
Defining Explanation in Probabilistic Systems
As probabilistic systems gain popularity and are coming into wider use, the
need for a mechanism that explains the system's findings and recommendations
becomes more critical. The system will also need a mechanism for ordering
competing explanations. We examine two representative approaches to explanation
in the literature - one due to G\"ardenfors and one due to Pearl - and show
that both suffer from significant problems. We propose an approach to defining
a notion of "better explanation" that combines some of the features of both
together with more recent work by Pearl and others on causality.Comment: Appears in Proceedings of the Thirteenth Conference on Uncertainty in
Artificial Intelligence (UAI1997
Relevant Explanations: Allowing Disjunctive Assignments
Relevance-based explanation is a scheme in which partial assignments to
Bayesian belief network variables are explanations (abductive conclusions). We
allow variables to remain unassigned in explanations as long as they are
irrelevant to the explanation, where irrelevance is defined in terms of
statistical independence. When multiple-valued variables exist in the system,
especially when subsets of values correspond to natural types of events, the
over specification problem, alleviated by independence-based explanation,
resurfaces. As a solution to that, as well as for addressing the question of
explanation specificity, it is desirable to collapse such a subset of values
into a single value on the fly. The equivalent method, which is adopted here,
is to generalize the notion of assignments to allow disjunctive assignments. We
proceed to define generalized independence based explanations as maximum
posterior probability independence based generalized assignments (GIB-MAPs).
GIB assignments are shown to have certain properties that ease the design of
algorithms for computing GIB-MAPs. One such algorithm is discussed here, as
well as suggestions for how other algorithms may be adapted to compute
GIB-MAPs. GIB-MAP explanations still suffer from instability, a problem which
may be addressed using ?approximate? conditional independence as a condition
for irrelevance.Comment: Appears in Proceedings of the Ninth Conference on Uncertainty in
Artificial Intelligence (UAI1993
SMILES2Vec: An Interpretable General-Purpose Deep Neural Network for Predicting Chemical Properties
Chemical databases store information in text representations, and the SMILES
format is a universal standard used in many cheminformatics software. Encoded
in each SMILES string is structural information that can be used to predict
complex chemical properties. In this work, we develop SMILES2vec, a deep RNN
that automatically learns features from SMILES to predict chemical properties,
without the need for additional explicit feature engineering. Using Bayesian
optimization methods to tune the network architecture, we show that an
optimized SMILES2vec model can serve as a general-purpose neural network for
predicting distinct chemical properties including toxicity, activity,
solubility and solvation energy, while also outperforming contemporary MLP
neural networks that uses engineered features. Furthermore, we demonstrate
proof-of-concept of interpretability by developing an explanation mask that
localizes on the most important characters used in making a prediction. When
tested on the solubility dataset, it identified specific parts of a chemical
that is consistent with established first-principles knowledge with an accuracy
of 88%. Our work demonstrates that neural networks can learn technically
accurate chemical concept and provide state-of-the-art accuracy, making
interpretable deep neural networks a useful tool of relevance to the chemical
industry.Comment: Submitted to SIGKDD 201
Finding dissimilar explanations in Bayesian networks: Complexity results
Finding the most probable explanation for observed variables in a Bayesian
network is a notoriously intractable problem, particularly if there are hidden
variables in the network. In this paper we examine the complexity of a related
problem, that is, the problem of finding a set of sufficiently dissimilar, yet
all plausible, explanations. Applications of this problem are, e.g., in search
query results (you won't want 10 results that all link to the same website) or
in decision support systems. We show that the problem of finding a 'good
enough' explanation that differs in structure from the best explanation is at
least as hard as finding the best explanation itself.Comment: Presented at the Benelux AI Conference (BNAIC 2018
Towards Interrogating Discriminative Machine Learning Models
It is oftentimes impossible to understand how machine learning models reach a
decision. While recent research has proposed various technical approaches to
provide some clues as to how a learning model makes individual decisions, they
cannot provide users with ability to inspect a learning model as a complete
entity. In this work, we propose a new technical approach that augments a
Bayesian regression mixture model with multiple elastic nets. Using the
enhanced mixture model, we extract explanations for a target model through
global approximation. To demonstrate the utility of our approach, we evaluate
it on different learning models covering the tasks of text mining and image
recognition. Our results indicate that the proposed approach not only
outperforms the state-of-the-art technique in explaining individual decisions
but also provides users with an ability to discover the vulnerabilities of a
learning model
Using Abduction in Markov Logic Networks for Root Cause Analysis
IT infrastructure is a crucial part in most of today's business operations.
High availability and reliability, and short response times to outages are
essential. Thus a high amount of tool support and automation in risk management
is desirable to decrease outages. We propose a new approach for calculating the
root cause for an observed failure in an IT infrastructure. Our approach is
based on Abduction in Markov Logic Networks. Abduction aims to find an
explanation for a given observation in the light of some background knowledge.
In failure diagnosis, the explanation corresponds to the root cause, the
observation to the failure of a component, and the background knowledge to the
dependency graph extended by potential risks. We apply a method to extend a
Markov Logic Network in order to conduct abductive reasoning, which is not
naturally supported in this formalism. Our approach exhibits a high amount of
reusability and enables users without specific knowledge of a concrete
infrastructure to gain viable insights in the case of an incident. We
implemented the method in a tool and illustrate its suitability for root cause
analysis by applying it to a sample scenario
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