Learning Agents Laboratory

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CS 782 Machine Learning
10 Case-Based Reasoning and Learning
Prof. Gheorghe Tecuci
Learning Agents Laboratory Computer Science
Department George Mason University
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Overview
Introduction
Protos A case-based reasoning and learning
system
Knowledge representation and organization
Learning
Recommended reading
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Case-based reasoning

• Case-Based Reasoning (CBR) is a name given to a
  reasoning method that uses specific past
  experiences rather than a corpus of general
  knowledge.
• It is a form of problem solving by analogy in
  which a new problem is solved by recognizing its
  similarity to a specific known problem, then
  transferring the solution of the known problem to
  the new one.
• CBR systems consult their memory of previous
  episodes to help address their current task,
  which could be
• planning of a meal,
• classifying the disease of a patient,
• designing a circuit, etc.

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Classification tasks
What is a classification?
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Case-based reasoning for classification tasks
Case-based reasoning for classification is a kind
of instance-based learning, where the instances
have a more complex (structural) description. In
CBR systems, a concept ci is represented
extensionally as a collection of examples (called
exemplars or cases) ci ei1, ei2, ....
Then a belongs to the concept ci if a is
similar to an element eij of ci, and this
similarity is greater than the similarity between
a and any other concept exemplar.
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Overview
Introduction
Protos A case-based reasoning and learning
system
Knowledge representation and organization
Learning
Recommended reading
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The PROTOS system
Protos is a case-based problem solving and
learning system for heuristic classification
tasks. The main features of the system will be
presented in the context of a task for the
classification of hearing disorders. In Protos,
a concept ci is represented extensionally as a
collection of examples (called exemplars or
cases) ci ei1, ei2, .... Classifying an
input NewCase involves searching for a concept
exemplar ejk that strongly matches NewCase. If
such an exemplar is found then Protos asserts
that NewCase belongs to the concept cj (the
concept whose exemplar is ejk).
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The classification and learning algorithm

Input a set of exemplar-based categories C
c1, c2, ... , cn and a case (NewCase) to
classify. REPEAT Classify Find an exemplar
of ci ? C that strongly matches NewCase and
classify NewCase as ci. Explain the
classification. Learn If the expert disagrees
with the classification or explanation
then acquire classification and explanation
knowledge and adjust C so that NewCase is
correctly classified and explained. UNTIL the
expert approves the classification and
explanation.
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The PROTOS system explaining a classification
C is the set of all concepts recognized by the
system, each concept being represented
extensionally as a set of representative
exemplars. How could one explain the
classification of a case to a concept?
Explaining the classification involves showing
the line of reasoning used during matching.
Which would be a simple type of explanation?
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Explaining a classification (cont.)
Which would be a simple type of explanation?
The simplest explanation is a list of the matched
features of the case and the exemplar.
Which would be a more detailed explanation?
A more detailed explanation may include
justifications of the flexible matches performed
as, for instance, in the case of classifying
chairs 'pedestal' was matched with 'legs(4)'
because both are specializations of seat
support" or 'seat' was matched with 'backrest'
because seat enables 'hold(person)' and
backrest enables 'hold(person)'
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The PROTOS system learning
When would a CBR system like PROTOS need to learn?
When it makes mistakes.
What kind of mistakes could PROTOS make?
Errors of classification and errors of
explanation.
How could it learn?
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The PROTOS system learning
How could it learn?
Adjust the categories so that the case will be
properly classified and explained.
Which is a simple way to assure that the case
will be correctly classified in the future?
Add the case to the correct category as a new
exemplar.
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Overview
Introduction
Protos A case-based reasoning and learning
system
Knowledge representation and organization
Learning
Recommended reading
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Knowledge representation and organization
In Protos, the exemplars and the cases to be
classified are represented as collections of
features. The description of a case (or
exemplar) may be incomplete, in the sense that it
does not include some of the features present in
other case descriptions. Also, the features with
which cases are described may not directly
indicate category membership. Therefore, one has
to make inferences.
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Sample case description
Case to be classified as belonging to one of the
categories normal_ear, cochlear_noise,
cochlear_age, otitis_media, ... NewCase senso
rineural mild notch_at_4K history
noise speech normal oc_acoustic_reflex
normal oi_acoustic_reflex elevated i_acoustic_r
eflex normal c_acoustic_reflex normal static
normal tympanogram a air normal
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Organization of the exemplars and concepts
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Remindings and Censors
Remindings associate features with categories or
particular exemplars. Such associations provide
Protos with hypotheses during classification,
which restrict its search for a matching
exemplar. For example, "air normal" would be a
reminder of the category "normal_ear". Remindings
are compiled from explanations of the relevance
of features to categories or exemplars. A
reminding has a strength that estimates the
conditional probability p(categoryfeature) or
p(exemplarfeature).
Censors are negative remindings. A censor is a
feature that tends to rule out a classification.
For example, "temperature fever" would be a
censor for the category "healthy_patient".
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Prototypicality and difference links
Prototypicality ratings provide a partial
ordering on exemplars within a category.
Exemplars of a category which have the highest
family resemblance (i.e. are most similar to
other members of the category) are most
prototypical. A difference link connects two
exemplars (in the same or different categories)
and records important featural differences
between them which may suggest alternate
classifications and better exemplars for use
during classification.
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Case classification
Hypothesize classifications based on the case's
features by using remindings and censors. The
remindings and censors associated with the
features of a new case are combined to produce an
ordered list of possible classifications. Attempt
to confirm a hypothesis by matching the new case
with prototypical exemplars. A process of
knowledge-based pattern matching determines the
similarity of the case and each exemplar. It uses
previously acquired domain knowledge to explain
how features of the case provide the same
evidence as features of the exemplar. Overall
similarity of the two cases is asserted by
evaluating the quality of the resulting
explanation and the importance of unmatched
features. If a match is imperfect, Protos
searches for a more similar exemplar by
traversing difference links associated with the
current exemplar. If the match is strong (i.e.,
adequately explained), it is presented to the
user for approval and discussion. If it is weak,
Protos considers other hypotheses and
exemplars. It reports failure if its hypotheses
are exhausted without finding an adequate match.
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Use of reminders to suggest classifications of
input
Some of the features of NewCase are reminders for
two possible diagnosis, "normal_ear" and
"cochlear_age
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Matching of the input with an exemplar
When the individual remindings are combined,
"normal_ear" is the strongest hypothesis. Protos
retrieves the most prototypical exemplar of
"normal_ear" and attempts to match it to the
NewCase to confirm the hypothesis
NewCase Case p8447L of
Normal_Ear sensorineural mild notch_at_4K history
noise speech normal ---------------------------
------ speech normal oc_acoustic_reflex normal
------------------ oc_acoustic_reflex normal
oi_acoustic_reflex elevated -----------------
oi_acoustic_reflex elevated i_acoustic_reflex
normal --------------------- i_acoustic_reflex
normal c_acoustic_reflex normal
-------------------- c_acoustic_reflex normal
static normal ----------------------------------
-- static normal tympanogram a
-------------------------------- tympanogram a
- air normal ------------------------------------
--- air normal
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Overview
Introduction
Protos A case-based reasoning and learning
system
Knowledge representation and organization
Learning
Recommended reading
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Protos-teacher dialog
Protos believes the match to be strong since all
the exemplar's features are matched. However the
teacher rejects the classification of the NewCase
as a "normal_ear". Dialog with the user helps
Protos to improve its knowledge. Protos asks
about the features of the NewCase that were not
matched by the exemplar and is told that all are
incompatible with "normal_ear".
How could this information be used?
This information will be used to define a
difference link associated with the normal_ear
exemplar, which will point toward the current
(but not yet known) classification of the
NewCase. Protos also asks whether the exemplar
has additional features that discriminate it from
NewCase, but the teacher does not identify any.
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Protos-teacher dialog (cont.)
Protos then tries to confirm its second
hypothesis, "cochlear_age", but it fails to find
a good match with any exemplar. This means that
Protos exhausted all the hypotheses.
What can be done in this situation?
Protos asks the user to classify the NewCase and
it is told that the classification is
"cochlear_noise". Since the system has no
exemplar of this category, NewCase is retained as
an exemplar.
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Protos-teacher dialog (cont.)
Dialog with the user helps Protos to acquire
general knowledge of "cochlear_noise". Protos
asks the teacher to explain the relevance of each
case feature to the classification and receives
explanations such as "historynoise is
required by cochlear_noise"
How could this information be used?
The feature is required indicates Protos to
define "historynoise" as a strong remainding to
"cochlear_noise".
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Protos-teacher dialog (cont.)
Another explanation is "notch_at_4k is usually
caused by cochlear_noise"
How could this information be used?
The relationship is usually caused indicates to
define "notch_at_4k" as a less strong remainding
to "cochlear_noise". Protos provides a set of
relationships that the user may use in
explanations. Protos also installs a difference
link between the exemplar p8447L of the
"normal_ear" and the NewCase and annotates it
with the features of NewCase that the teacher has
previously stated that are incompatible with
normal_ear.
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The role of explanations in Protos
What role do explanations play in learning?
Explanations describe the relevance of exemplar
features to categories. From such explanations
Protos extracts remindings and assesses their
strength. Such an explanation is "historynoise
is required by cochlear_noise"
Explanations describe how different features
provide equivalent evidence for classification.
Such explanations provide knowledge to match
features that are not identical. Examples of
such explanations are notch_at_4k is
definitionally equivalent to notch_4k or if the
category is cochlear_noise then
c_acoustic_reflex normal is sometimes
interchangeable with c_acoustic_reflex elevated
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The learning algorithm

GIVEN a new case FIND a classification of the
case and an explanation of the classification Sea
rch for an exemplar that matches the new case IF
not found THEN classification failure Ask
teacher for classification Acquire explanations
relating features to classification Compile
remindings Retain case as an exemplar ELSE IF
the teacher disapproves THEN discrimination
failure Reassess remindings Discuss
featural matches with the teacher Ask for
discriminating features Remember unmatched
features to add difference link ELSE
classification is correct Increase
exemplar's prototypicality rating IF match is
incompletely explained THEN explanation
failure Ask the teacher for explanation
of featural equivalence IF not
given THEN Retain case as exemplar ELSE
processing was successful
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Exercise
At a high level of generality, CBR systems can be
described as performing the following 4-step
process 1. Retrieve the most similar case 2.
Use the case to produce a tentative solution of
the input problem 3. Revise the proposed
solution 4. Learn from this experience to improve
performance in the future. Explain how each of
this step is performed in Protos.
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Exercise
What is the difference between instance-based
learning, on one hand, and case-based reasoning
and learning, on the other hand?
What are the relative characteristic features of
instance-based learning?

• Instance-based learning
• is a special (simplified) type of case-based
  reasoning for classification tasks
• uses a simple (feature-vector) representation of
  the exemplars
• compensates the lack of guidance from general
  domain knowledge by using a large number of
  examples.

What are the relative characteristic features of
CBR?

• Case-based reasoning and learning
• is used for other tasks besides classification
• in general, a case has a complex structure, not
  just a feature vector
• a retrieved case is generally modified, when
  applied to a new problem
• utilizes general domain knowledge.

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Exercise
What is the difference between case-based
reasoning, on one hand, and analogical reasoning,
on the other hand?
What are the relative characteristic features of
case-based reasoning?
All the cases are from the same domain.
Therefore it does not need to deal with the
ACCESS problem. It is generally regarded as a
special type of analogical reasoning.
What are the relative characteristic features of
analogical reasoning?
The sources and the target are generally from
different domains.
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Recommended reading
Porter B.W, Bareiss R., Holte R.C., Concept
Learning and Heuristic Classification in
Weak-Theory Domains, in Readings in Knowledge
Acquisition and Learning, Morgan Kaufmann,
1992. Bareiss R., Porter B.W, Murray K.S.,
Supporting Start-to-Finish Development of
Knowledge Bases, Machine Learning, 4, 259-283,
1989. Bareiss R., Exemplar-Based Knowledge
Acquisition, Academic Press, 1989. Aamodt A.,
Knowledge Acquisition and learning by experience
the role of case-specific knowledge, in Tecuci
G. and Kodratoff Y. (eds.), Machine Learning and
Knowledge Acquisition Integrated Approaches,
pp.197-245, Academic Press, 1995. Sycara K.
Miyashita K., Learning control knowledge through
case-based acquisition of user optimization
preferences in ill-structured domains, in Tecuci
G. and Kodratoff Y. (eds.), Machine Learning and
Knowledge Acquisition Integrated Approaches, pp.
247-275, Academic Press, 1995.