===============================================================================
cthomp-thesis
Inductive Learning for Abductive Diagnosis
new system for learning by induction, called \lab\/, is presented.
\lab\/ (Learning for ABduction) learns abductive rules based on a
set of training examples. Our goal is to find a small knowledge base which,
when used abductively, diagnoses the training examples correctly, in addition
to generalizing well to unseen examples. This is in contrast to past systems,
which inductively learn rules which are used deductively. Abduction
is particularly well suited to diagnosis, in which we are given a set of
symptoms (manifestations) and we want our output to be a set
of disorders which explain why the manifestations are present. Each training
example is associated with potentially multiple categories, instead of
one, which is the case with typical learning systems.
Building the knowledge base requires a choice between multiple possibilities,
and the number of possibilities grows exponentially with the
number of training examples. One method of choosing the best knowledge
base is described and implemented. The final system is
experimentally evaluated, using data from the domain of diagnosing
brain damage due to stroke. It is compared to other learning systems and
a knowledge base produced by an expert. The results are promising:
the rule base learned is simpler than the expert knowledge base and
rules learned by one of the other systems, and the accuracy of the learned
rule base in predicting which areas are damaged is better than all the other
systems as well as the expert knowledge base.
===============================================================================
baffes-proposal
Learning to Model Students: Using Theory Refinement to Detect Misconceptions
Paul T. Baffes
A new student modeling system called ASSERT is described
which uses domain independent learning algorithms to model
unique student errors and to automatically construct bug
libraries. ASSERT consists of two learning phases. The first
is an application of theory refinement techniques for
constructing student models from a correct theory of the
domain being tutored. The second learning cycle
automatically constructs the bug library by extracting
common refinements from multiple student models which are
then used to bias future modeling efforts. Initial
experimental data will be presented which suggests that
ASSERT is a more effective modeling system than other
induction techniques previously explored for student
modeling, and that the automatic bug library construction
significantly enhances subsequent modeling efforts.
===============================================================================
zelle-proposal
Learning Search-Control Heuristics for Logic Programs: Applications to Speedup
Learning and Language Acquisition
John M. Zelle
This paper presents a general framework, learning search-control
heuristics for logic programs, which can be used to improve both the
efficiency and accuracy of knowledge-based systems expressed as
definite-clause logic programs. The approach combines techniques of
explanation-based learning and recent advances in inductive logic
programming to learn clause-selection heuristics that guide program
execution. Two specific applications of this framework are detailed:
dynamic optimization of Prolog programs (improving efficiency) and
natural language acquisition (improving accuracy). In the area of
program optimization, a prototype system, DOLPHIN, is able to
transform some intractable specifications into polynomial-time
algorithms, and outperforms competing approaches in several benchmark
speedup domains. A prototype language acquisition system, CHILL,
is also described. It is capable of automatically acquiring semantic
grammars, which uniformly incorprate syntactic and semantic constraints
to parse sentences into case-role representations. Initial
experiments show that this approach is able to construct accurate
parsers which generalize well to novel sentences and significantly
outperform previous approaches to learning case-role mapping based on
connectionist techniques. Planned extensions of the general framework
and the specific applications as well as plans for further evaluation
are also discussed.
===============================================================================
speedup-ijcai93
Combining FOIL and EBG to Speed-up Logic Programs
John M. Zelle and Raymond J. Mooney
Proceedings of the Thirteenth International Joint Conference on Artificial
Intelligence, Chambery, France, 1993 (IJCAI-93).
This paper presents an algorithm that combines traditional EBL
techniques and recent developments in inductive logic programming to
learn effective clause selection rules for Prolog programs. When
these control rules are incorporated into the original program,
significant speed-up may be achieved. The algorithm is shown to be an
improvement over competing EBL approaches in several domains.
Additionally, the algorithm is capable of automatically transforming
some intractable algorithms into ones that run in polynomial time.
===============================================================================
neither-ijcai93
Symbolic Revision of Theories with M-of-N Rules
Paul T. Baffes and Raymond J. Mooney
Proceedings of the Thirteenth International Joint Conference on Artificial
Intelligence, Chambery, France, 1993 (IJCAI-93).
This paper presents a major revision of the EITHER propositional theory
refinement system. Two issues are discussed. First, we show how run time
efficiency can be greatly improved by changing from a exhaustive scheme for
computing repairs to an iterative greedy method. Second, we show how to extend
EITHER to refine M-of-N rules. The resulting algorithm, NEITHER (New EITHER),
is more than an order of magnitude faster and produces significantly more
accurate results with theories that fit the M-of-N format. To demonstrate the
advantages of NEITHER, we present preliminary experimental results comparing it
to EITHER and various other systems on refining the DNA promoter domain theory.
===============================================================================
chill-aaai93
Learning Semantic Grammars with Constructive Inductive Logic Programming
John M. Zelle and Raymond J. Mooney
Proceedings of the Eleventh National conference of the American
Association for Artificial Intelligence (AAAI-93).
Automating the construction of semantic grammars is a difficult and
interesting problem for machine learning. This paper shows how the
semantic-grammar acquisition problem can be viewed as the learning of
search-control heuristics in a logic program. Appropriate control
rules are learned using a new first-order induction algorithm that
automatically invents useful syntactic and semantic categories.
Empirical results show that the learned parsers generalize well to
novel sentences and out-perform previous approaches based on
connectionist techniques.
===============================================================================
conn-sci
Combining Connectionist and Symbolic Learning to Refine Certainty-Factor
Rule-Bases
J. Jeffrey Mahoney and Raymond J. Mooney
To appear in Connection Science journal's special issue on Architectures for
Integrating Neural and Symbolic Processing.
This paper describes Rapture --- a system for revising probabilistic knowledge
bases that combines connectionist and symbolic learning methods. Rapture uses
a modified version of backpropagation to refine the certainty factors of a
Mycin-style rule base and it uses ID3's information gain heuristic to add
new rules. Results on refining three actual expert knowledge bases demonstrate
that this combined approach generally performs better than previous methods.
===============================================================================
forte
Refinement of First-Order Horn-Clause Domain Theories
Bradley L. Richards and Raymond J. Mooney
Submitted for journal publication
Knowledge acquisition is a difficult and time-consuming task, and as
error-prone as any human activity. The task of automatically improving an
existing knowledge base using learning methods is addressed by a new class of
systems performing {\it theory refinement}. Until recently, such systems were
limited to propositional theories. This paper presents a system, {\sc Forte}
(First-Order Revision of Theories from Examples), for refining first-order
Horn-clause theories. Moving to a first-order representation opens many new
problem areas, such as logic program debugging and qualitative modelling, that
are beyond the reach of propositional systems. {\sc Forte} uses a
hill-climbing approach to revise theories. It identifies possible errors in
the theory and calls on a library of operators to develop possible revisions.
The best revision is implemented, and the process repeats until no further
revisions are possible. Operators are drawn from a variety of sources,
including propositional theory refinement, first-order induction, and inverse
resolution. {\sc Forte} has been tested in several domains including logic
programming and qualitative modelling.
===============================================================================
cnf
Encouraging Experimental Results on Learning CNF
Raymond J. Mooney
To appear in Machine Learning
This paper presents results comparing three inductive learning systems using
different representations for concepts, namely: CNF formulae, DNF formulae, and
decision trees. The CNF learner performs surprisingly well. Results on five
natural data sets show that it frequently trains faster and produces more
accurate and simpler concepts. The probable explanation for its superior
performance is that the other systems are more susceptible to the replication
problem.
===============================================================================
brace-proposal
Belief Revision in the Context of Abductive Explanation
Siddarth Subramanian
Appears as Technical Report AI92-179, Artificial Intelligence Lab,
University of Texas at Austin, March 1991.
This proposal presents an approach to explanation that incorporates
the paradigms of belief revision and abduction. We present an
algorithm that combines these techniques and a system called {\sc
Brace} that is a preliminary implementation of this algorithm. We show
the applicability of the {\sc Brace} approach to a wide range of
domains including scientific discovery, device diagnosis and plan
recognition. Finally, we describe our proposals for a new
implementation, new application domains for our system and extensions
to this approach.
===============================================================================
accel-journal
A First-Order Horn-Clause Abductive System and Its Use in Plan Recognition
and Diagnosis
Hwee Tou Ng and Raymond J. Mooney
Submitted for journal publication.
A diverse set of intelligent activities, including natural language
understanding and diagnosis, requires the ability to construct
explanations for observed phenomena. In this paper, we view
explanation as {\em abduction}, where an abductive explanation is a
consistent set of assumptions which, together with background
knowledge, logically entails a set of observations. We have
successfully built a domain-independent system, {\sc Accel}, in which
knowledge about a variety of domains is uniformly encoded in
first-order Horn-clause axioms. A general-purpose abduction
algorithm, {\sc AAA}, efficiently constructs explanations in the
various domains by caching partial explanations to avoid redundant
work. Empirical results show that caching of partial explanations can
achieve more than an order of magnitude speedup in run time. We have
applied our abductive system to two general tasks: plan recognition in
text understanding, and diagnosis of medical diseases, logic circuits,
and dynamic systems. The results indicate that {\sc Accel} is a
general-purpose system capable of plan recognition and diagnosis, yet
efficient enough to be of practical utility.
===============================================================================
kr92
Abductive Plan Recognition and Diagnosis: A Comprehensive Empirical Evaluation
Hwee Tou Ng and Raymond J. Mooney
Appears in the Proceedings of the Third International Conference on Principles
of Knowledge Representation and Reasoning, pp. 499-508, Cambridge, MA, October
1992.
While it has been realized for quite some time within AI that abduction
is a general model of explanation for a variety of tasks, there have
been no empirical investigations into the practical feasibility of a
general, logic-based abductive approach to explanation. In this paper
we present extensive empirical results on applying a general abductive
system, {\sc Accel}, to moderately complex problems in plan recognition
and diagnosis. In plan recognition, {\sc Accel} has been tested on 50
short narrative texts, inferring characters' plans from actions
described in a text. In medical diagnosis, {\sc Accel} has diagnosed 50
real-world patient cases involving brain damage due to stroke
(previously addressed by set-covering methods). {\sc Accel} also uses
abduction to accomplish model-based diagnosis of logic circuits (a full
adder) and continuous dynamic systems (a temperature controller and the
water balance system of the human kidney). The results indicate that
general purpose abduction is an effective and efficient mechanism for
solving problems in plan recognition and diagnosis.
===============================================================================
aaai92-misq
Automatic Abduction of Qualitative Models
Bradley L. Richards, Ina Kraan, and Benjamin J. Kuipers
Appears in the Proceedings of the Tenth National Conference on
Artificial Intelligence, San Jose, CA, July 1992.
We describe a method of automatically abducing qualitative models from
descriptions of behaviors. We generate, from either quantitative or
qualitative data, models in the form of qualitative differential equations
suitable for use by QSIM. Constraints are generated and filtered both by
comparison with the input behaviors and by dimensional analysis. If the
user provides complete information on the input behaviors and the
dimensions of the input variables, the resulting model is unique,
maximally constrainted, and guaranteed to reproduce the input behaviors.
If the user provides incomplete information, our method will still
generate a model which reproduces the input behaviors, but the model
may no longer be unique. Incompleteness can take several forms: missing
dimensions, values of variables, or entire variables.
===============================================================================
aaai92-relpath
Learning Relations by Pathfinding
Bradley L. Richards and Raymond J. Mooney
Appears in the Proceedings of the Tenth National Conference on
Artificial Intelligence, pp. 50-55, San Jose, CA, July 1992.
First-order learning systems (e.g., FOIL, FOCL, FORTE) generally rely on
hill-climbing heuristics in order to avoid the combinatorial explosion
inherent in learning first-order concepts. However, hill-climbing leaves
these systems vulnerable to local maxima and local plateaus. We present
a method, called relational pathfinding, which has proven highly effective
in escaping local maxima and crossing local plateaus. We present our
algorithm and provide learning results in two domains: family relationships
and qualitative model building.
===============================================================================
mlw92-speedup
Speeding-up Logic Programs by Combining EBG and FOIL
John M. Zelle and Raymond J. Mooney
Appears in the Proceedings of the 1992 Machine Learning Workshop on Knowledge
Compilation and Speedup Learning, Aberdeen Scotland, July 1992.
This paper presents an algorithm that combines traditional EBL
techniques and recent developments in inductive logic programming to
learn effective clause selection rules for Prolog programs. When these
control rules are incorporated into the original program, significant
speed-up may be achieved. The algorithm produces not only EBL-like
speed up of problem solvers, but is capable of automatically
transforming some intractable algorithms into ones that run in
polynomial time.
===============================================================================
mlw92-integrated
Combining Symbolic and Neural Learning to Revise Probabilistic Theories
J. Jeffrey Mahoney & Raymond J. Mooney
Appears in the Proceedings of the 1992 Machine Learning Workshop on Integrated
Learning in Real Domains, Aberdeen Scotland, July 1992.
This paper describes {\sc Rapture} --- a system for revising probabilistic
theories that combines symbolic and neural-network learning methods. {\sc
Rapture} uses a modified version of backpropagation to refine the certainty
factors of a Mycin-style rule-base and it uses ID3's information gain heuristic
to add new rules. Results on two real-world domains demonstrate that this
combined approach performs as well or better than previous methods.
===============================================================================
ijcnn-92
Growing Layers of Perceptrons: Introducing the Extentron Algorithm
Paul T. Baffes and John M. Zelle
Appears in the Proceedings of the 1992 International Joint Conference on Neural
Networks, Baltimore, Maryland, June 7-11.
The ideas presented here are based on two observations of perceptrons: (1) when
the perceptron learning algorithm cycles among hyperplanes, the hyperplanes may
be compared to select one that gives a best {\em split\/} of the examples, and
(2) it is always possible for the perceptron to build a hyperplane that
separates {\em at least one\/} example from all the rest. We describe the
Extentron, which grows multi-layer networks capable of distinguishing
NON-LINEARLY-SEPARABLE data using the simple perceptron rule for linear
threshold units. The resulting algorithm is simple, very fast, scales well to
large problems, retains the convergence properties of the perceptron, and can
be completely specified using only two parameters. Results are presented
comparing the Extentron to other neural network paradigms and to symbolic
learning systems.
===============================================================================
cogsci-92
Using Theory Revision to Model Students and Acquire Stereotypical Errors
Paul T. Baffes and Raymond J. Mooney
Appears in the Proceedings of the Fourteenth Annual Conference of the Cognitive
Science Society, pp. 617-622, Bloomington, IN, July 1992
Student modeling has been identified as an important component to the long
term development of Intelligent Computer-Aided Instruction (ICAI) systems. Two
basic approaches have evolved to model student misconceptions. One uses a
static, predefined library of user bugs which contains the misconceptions
modeled by the system. The other uses induction to learn student
misconceptions from scratch. Here, we present a third approach that uses a
machine learning technique called theory revision. Using theory revision
allows the system to automatically construct a bug library for use in modeling
while retaining the flexibility to address novel errors.
===============================================================================
threvpac
A Preliminary PAC Analysis of Theory Revision
Raymond J. Mooney
March, 1992
To appear in Computational Learning Theory and Natural Learning Systems, Vol.
3, T. Petsche, S. Judd, and S. Hanson, Eds., MIT Press.
This paper presents a preliminary analysis of the sample complexity of theory
revision within the framework of PAC (Probably Approximately Correct)
learnability theory. By formalizing the notion that the initial theory is
``close'' to the correct theory we show that the sample complexity of an
optimal propositional Horn-clause theory revision algorithm is $O( ( \ln 1 /
\delta + d \ln (s_0 + d + n) ) / \epsilon)$, where $d$ is the {\em syntactic
distance} between the initial and correct theories, $s_0$ is the size of
initial theory, $n$ is the number of observable features, and $\epsilon$ and
$\delta$ are the standard PAC error and probability bounds. The paper also
discusses the problems raised by the computational complexity of theory
revision.
===============================================================================
ilp-92
Automated Debugging of Logic Programs via Theory Revision
Raymond J. Mooney & Bradley L. Richards
February, 1992
Appears in the Proceedings of the Second International Workshop on Inductive
Logic Programming, Tokyo, Japan, June 1992.
This paper presents results on using a theory revision system to automatically
debug logic programs. {\sc Forte} is a recently developed system for revising
function-free Horn-clause theories. Given a theory and a set of training
examples, it performs a hill-climbing search in an attempt to minimally modify
the theory to correctly classify all of the examples. {\sc Forte} makes use of
methods from propositional theory revision, Horn-clause induction ({\sc Foil}),
and inverse resolution. The system has has been successfully used to debug
logic programs written by undergraduate students for a programming languages
course.
===============================================================================
spring-symp-92
Batch versus Incremental Theory Refinement
Raymond J. Mooney
Appears in the Proceedings of AAAI Spring Symposium on Knowledge
Assimilation, Standford, CA, March, 1992.
Most existing theory refinement systems are not incremental. However, any
theory refinement system whose input and output theories are compatible can be
used to incrementally assimilate data into an evolving theory. This is done by
continually feeding its revised theory back in as its input theory. An
incremental batch approach, in which the system assimilates a batch of examples
at each step, seems most appropriate for existing theory revision systems.
Experimental results with the {\sc Either} theory refinement system demonstrate
that this approach frequently increases efficiency without significantly
decreasing the accuracy or the simplicity of the resulting theory. However, if
the system produces bad initial changes to the theory based on only small
amount of data, these bad revisions can ``snowball'' and result in an overall
decrease in performance.
===============================================================================
ml4-chapter
A Multistrategy Approach to Theory Refinement
Raymond J. Mooney & Dirk Ourston
January, 1992
Revised version of a paper appearing in the Proceedings of the International
Workshop on Multistrategy Learning, pp. 207-214, Harpers Ferry, West Virginia,
November, 1991. To appear in Volume 4 of the Machine Learning book series from
Morgan Kaufman editted by R.S. Michalski & G. Teccuci.
This chapter describes a multistrategy system that employs independent modules
for deductive, abductive, and inductive reasoning to revise an arbitrarily
incorrect propositional Horn-clause domain theory to fit a set of preclassified
training instances. By combining such diverse methods, {\sc Either} is able
to handle a wider range of imperfect theories than other theory revision
systems while guaranteeing that the revised theory will be consistent with the
training data. {\sc Either} has successfully revised two actual expert
theories, one in molecular biology and one in plant pathology. The results
confirm the hypothesis that using a multistrategy system to learn from both
theory and data gives better results than using either theory or data alone.
===============================================================================
category-chapter
Integrating Theory and Data in Category Learning
Raymond J. Mooney
December, 1991
Revised version of paper presented at the Interfaces Conference on
Categorization and Category Learning by Humans and Machines, October 11-12,
1991, Texas Tech University, Lubbock, TX. To appear in: Acquisition,
Representation, and Processing of Categories and Concepts: The Contribution of
Exemplars and Theories, edited by G. Nakamura & R. Taraban, Academic Press.
Recent results in both machine learning and cognitive psychology demonstrate
that effective category learning involves an integration of theory and data.
First, theories can bias induction, affecting what category definitions are
extracted from a set of examples. Second, conflicting data can cause theories
to be revised. Third, theories can alter the representation of data through
feature formation. This chapter reviews two machine learning systems
that attempt to integrate theory and data in one or more of these ways. IOU
uses a domain theory to acquire part of a concept definition and to focus
induction on the unexplained aspects of the data. {\sc Either} uses data to
revise an imperfect theory and uses theory to add abstract features to the
data. Recent psychological experiments reveal that these machine learning
systems exhibit several important aspects of human category learning.
Specifically, IOU has been used to successfully model some recent experimental
results on the effect of functional knowledge on category learning.
===============================================================================
either-aij
Theory Refinement Combining Analytical and Empirical Methods
Dirk Ourston and Raymond J. Mooney
November, 1991.
To appear in Artificial Intelligence.
This article describes a comprehensive approach to automatic theory revision.
Given an imperfect theory, the approach combines explanation attempts for
incorrectly classified examples in order to identify the failing portions of
the theory. For each theory fault, correlated subsets of the examples are used
to inductively generate a correction. Because the corrections are {\em
focused}, they tend to preserve the structure of the original theory. Because
the system starts with an approximate domain theory, in general fewer training
examples are required to attain a given level of performance (classification
accuracy) compared to a purely empirical system. The approach applies to
classification systems employing a propositional Horn-clause theory. The system
has been tested in a variety of application domains, and results are presented
for problems in the domains of molecular biology and plant disease diagnosis.
===============================================================================
iou-mlj
Induction Over the Unexplained: Using Overly-General Domain Theories to Aid
Concept Learning
Raymond J. Mooney
November, 1991
To appear in the Machine Learning Journal.
This paper describes and evaluates an approach to combining empirical and
explanation-based learning called Induction Over the Unexplained (IOU).
IOU is intended for learning concepts that can be partially explained by an
overly-general domain theory. An eclectic evaluation of the method is presented
which includes results from all three major approaches: empirical, theoretical,
and psychological. Empirical results shows that IOU is effective at refining
overly-general domain theories and that it learns more accurate concepts from
fewer examples than a purely empirical approach. The application of
theoretical results from PAC learnability theory explains why IOU requires
fewer examples. IOU is also shown to be able to model psychological data
demonstrating the effect of background knowledge on human learning.
===============================================================================
aaai91
An Efficient First-Order Horn-Clause Abduction System Based on the ATMS
Hwee Tou Ng and Raymond J. Mooney
Appears in the Proceedings of the Ninth National Conference on
Artificial Intelligence, pages 494-499, Anaheim, CA, July 1991.
This paper presents an algorithm for first-order Horn-clause abduction
that uses an ATMS to avoid redundant computation. This algorithm is
either more efficient or more general than any other previous
abduction algorithm. Since computing all minimal abductive
explanations is intractable, we also present a heuristic version of
the algorithm that uses beam search to compute a subset of the
simplest explanations. We present empirical results on a broad range
of abduction problems from text understanding, plan recognition, and
device diagnosis which demonstrate that our algorithm is at least an
order of magnitude faster than an alternative abduction algorithm that
does not use an ATMS.
===============================================================================
ml91-td
Improving Shared Rules in Multiple Category Domain Theories
Dirk Ourston and Raymond J. Mooney
Appears in the Proceedings of the Eighth International Machine Learning
Workshop, pp. 534-538, Evanston, IL, June 1991
This paper presents an approach to improving the classification performance of
a multiple category theory by correcting intermediate rules which are shared
among the categories. Using this technique, the performance of a theory in one
category can be improved through training in an entirely different category.
Examples of the technique are presented and experimental results are given.
===============================================================================
ml91-ci
Constructive Induction in Theory Refinement
Raymond J. Mooney and Dirk Ourston
Appears in the Proceedings of the Eighth International Machine Learning
Workshop, pp. 178-182, Evanston, IL. June 1991.
This paper presents constructive induction techniques recently added to the
EITHER theory refinement system. These additions allow EITHER to handle
arbitrary gaps at the ``top,'' ``middle,'' and/or ``bottom'' of an incomplete
domain theory. {\it Intermediate concept utilization} employs existing rules
in the theory to derive higher-level features for use in induction. {\it
Intermediate concept creation} employs inverse resolution to introduce new
intermediate concepts in order to fill gaps in a theory that span multiple
levels. These revisions allow EITHER to make use of imperfect domain theories
in the ways typical of previous work in both constructive induction and theory
refinement. As a result, EITHER is able to handle a wider range of theory
imperfections than does any other existing theory refinement system.
===============================================================================
either-noise
Theory Refinement with Noisy Data
Raymond J. Mooney and Dirk Ourston
Appears as Technical Report AI 91-153, Artificial Intelligence Lab,
University of Texas at Austin, March 1991
This paper presents a method for revising an approximate domain theory based on
noisy data. The basic idea is to avoid making changes to the theory that
account for only a small amount of data. This method is implemented in the
{\small EITHER} propositional Horn-clause theory revision system. The paper
presents empirical results on artificially corrupted data to show that this
method successfully prevents over-fitting. In other words, when the data is
noisy, performance on novel test data is considerably better than revising the
theory to completely fit the data. When the data is not noisy, noise processing
causes no significant degradation in performance. Finally, noise processing
increases efficiency and decreases the complexity of the resulting theory.
===============================================================================
aaai90
On the Role of Coherence in Abductive Explanation
Hwee Tou Ng and Raymond J. Mooney
Appears in the Proceedings of the Eighth National Conference on
Artificial Intelligence, pages 337-342, Boston, MA, 1990.
Abduction is an important inference process underlying much of human
intelligent activities, including text understanding, plan
recognition, disease diagnosis, and physical device diagnosis. In
this paper, we describe some problems encountered using abduction to
understand text, and present some solutions to overcome these
problems. The solutions we propose center around the use of a
different criterion, called {\em explanatory coherence}, as the
primary measure to evaluate the quality of an explanation. In
addition, explanatory coherence plays an important role in the
construction of explanations, both in determining the appropriate
level of specificity of a preferred explanation, and in guiding the
heuristic search to efficiently compute explanations of sufficiently
high quality.
===============================================================================