case-based reasoning and stochastic processes michael m. richter tu kaiserslautern, university of...

Case-Based Reasoningand

Stochastic Processes

Michael M. Richter

TU Kaiserslautern, University of Calgary

Our Questions Today

• What are stochastic processes?• Can one use CBR for stochastic processes?• Problems and answers:

Problems, Questions:

- When does a property hold for a process?

- How to find a process with a specific property?

For the answers one needs sophisticated methods and a CBR system has to provide them.

Michael M. Richter ICCBR 2015

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Stochastic Processes

• Many processes are human made in order to achieve desired results, e.g.– Business processes– Workflows etc.

• Others : They are presented in a form the user does not know. The task is to detect the information contained in the processes. Examples:– Medical processes– Seismic processes– Waves coming from speech, etc

Michael M. Richter ICCBR 2015 3

Hidden Stochastic Models HSM

Stochastic processes are

not deterministic

Instead of rules only probabilities are provided

What is if the probabilities are unknown??

This is an extension of stochastic processes:

Hidden Stochastic Models


Problems of Stochastic Processes


Stochastic Processes present Additional Difficulties:

Unkown Probabilities!!

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Logic versus Approximation

• Logic Orientation: We look at a 0-1 problem: True or false.

• In this case theorem provers can be used.

• Approximation Orientation: • The answer is less unique and clear. • The aspects are not clear and an ideal object may not

exist. Answers may be more or less useful.

• For HSM only some approximation can be expected


Stochastic Processes in Speech (1)

• If persons are talking to each other they are sending digits from the sender to the receiver.

• If the same spoken word is spoken two times the digits differ!

• One sends a stochastic process!• The probabilities are unknown because they depend on

the actual state of the body.• In addition it is problematic if this takes place in a noisy

environment.




The machine accepts the digit signals and performs the transformations that originally have been done by the human ear. This recognition is the part which we want to automate.

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• When a user talks to a machine one mostly wants to execute an action and gives commands.

• There are two machines involved:– The machine that receives the command– The machine that executes the action.

• Between the two machines there is a communication:

- The first machine understands the command and reformulates it in a way understood by the second machine and sends it

to it.

- The second machine executes the command and gives possibly a feedback to the user.


Speech and Music• Speech and Music both happens in sounds and both have

a written form. However, there are serious differences in the written form:

– In speech it is addressed to readers

– In music it is addressed to producers.

• Some of the applications of music signal processing methods include the following:

• Music coding for efficient storage and transmission of music signals. MP3, Sony’s adaptive transform acoustic coder.

• Music synthesis, pitch modification, audio mixing, audio morphing,

.


Stochastic Processes in Medicine (1)

• The heart sends out signals in the form of electrical impulses.

• The sender has not a certain intention about this.• The signals contain hidden information about the health of

the persons. • The process is stochastic because the human body is not

a deterministic machine.• The techniques for this are in principle close to those

employed for psychoacoustic phenomena in speech.


Stochastic Processes in Medicine (2)

• Examples:

Identify epileptic seizures • Identify organic encephalopathy or delirium

• Serve as an adjunct test of brain death Prognosticate in patients with coma

• Determine whether to use anti-epileptic medications


Stochastic Processes in Seismic (1)

• The seismic signals are generated from a seismic source. A seismic source is a device that controls the performance of the reflection and refraction of the seismic energy.

• The main principle of seismics is to send a vibration in the form of an elastic wave. Similar to the signal processes in medicine the wave do not have a precise semantics understandable to humans.

• In general, seismic processes are Hidden Stochastic Models because one does not know how precisely the sources operate.


Stochastic Processes in Seismic (2)



Stochastic Processes and Multimedia

• The forms of representations of questions and answers can be different.

• In applications they are sometimes mixed• Multimedia have no precise semantics.• In single-rate systems one sampling rate is used. Multirate

systems include more than one sample rate. • The main advantage of a multirate system is the

substantial decrease of computational complexity.• Decimation, or down-sampling, reduces the sampling rate.• A basic operation in multirate signal processing is to

decompose a signal into a number of subband components, which can be processed at a lower rate.

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Stochastic Processes and Images

• The digits in an image are the pixels. An image is a bunch (process) of pixels

• Humans pay no attention to individual pixels. They are able to accept the image as a whole. However, they have to be familar with the objects on the image and their meaning.

• This proceeds in levels.• For automatic recognition one has to implement the levels

and their connections.


Image Levelshigh Overall Description

Michael M. Richter,ICCBR 2015

Pixel Level

Level ofImage processing

Geometric - visualLevel

SingleObjects

low


From Bits to Meaning (1)

• Humans as well as machines obtain first bits.• Humans have methods to jump to a higher level of

abstractions.• The meaning of stochastic processes is more complex to get.• It requires an understanding of the various details of the

objects represented by the process.• In HSM the probabilities can be learned from observations.

.


From Bits to Meaning (2)

• A special meaning of a process can in the first place not automatically be deduced.

• It has to be determined by additional observations.• But after that the observations can pe predicted.• This leads to a combination that allows to see the

information contained in the process.


Dynamic Time Warping

Dynamic Time Warping (DTW).

A mapping between both

sequences such that the cumulative

Distance becomes minimal.

This reflects the closeness of

the whole sequences.


j

d(i − 1, j)

d(i-1, j − 1)

d(i, j)

d(i, j − 1)

i

j

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Additional Similarity Aspects

• Besides the DTW aspects there are some more to be considered as e.g. the maximal loudness.

• These are new attributes that give rise to new local similarity measures.


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Utility as the semantics of similarity

Functional form: Utility functions

Relational forms: Preference relations

In stochastic situations: Expected utility

More similar means more useful

Utility is subjective

Utility is more important than truth!


Noises

• There are quite many types of noise.• Each noise disturbs the original signal process and

therefore the information contained in it.• In case of disturbance, what should one do?• For specific noises there are methods to handle them.• Noise handling can disturb the signals.


Mild Noise


Steady-unsteady and time varying noise.


Strong Noise (1)

Strong noise overwhelms all the other signalsStrong noise for a long time cannot be handled. We model them as shots.

Shots are:

-- last for a very short time

-- are statistically distributed by

Poisson DistributionsPoisson Distributions


Strong Noise (2)

• The Idea:• Use match filters for.detecting the shots.• The shots are totally removed.

• The danger is that beside the short duration some speech may be removed too.

• For this reason the receiver is equipped with» An equipment recognizing this» A feedback possibiltiy with a speech syntheziser

saying

»REPEAT THIS“


Silence

• If there is an interval where no signals are it is a silence interval.

Can one simply ignore it?• However: Silence can be an important information!• Examples:

– Silence of heartbeat is dangerous– Silence in speech (a psychoacoustic

phenomenon) can change the meaning.


Simultaneously

• The original processes plus the different kinds of noise may occur simultaneously.

• The problem is to separate them.• This is a serious problem for noise:

– One wants to remove all noise– Each noise can be handled individually– Problem: Individual handling of one noise may disturb handling of

other noises.


Applying CBR (1)

• We formulated problems and solutions.• Examples of the problems are

– „What is the meaning of this process?– Is it dangerous?– What has to be done?

• Possible solutions are:– A description– Yes, no, or a little, depending on etc– Some recommended action.


Applying CBR (2)

• In principle, there is nothing new, only in the details. For this one has to be familiar with the domain.

• One should fill the CBR containers:– Collect the cases: These are pairs (problems, answers),– Formulate the similarity measure. They differ somewhat from

application type and applications too. This is most difficult.– They should contain the DTW and depending on the application

additional similarities.– Adaptation: Application depending.


Intermediate Results

• Often the intendet results depend on previous results that can be measured independently (possibly with high costs).

• One can directly compute these quantities from the original processes:

processes intermediate result final result

Example: Energy in seismic processes

ult


Realizations

• An important measure for the success of an approach is the number and quality of working applications.

• For stochastic processes there is no general experience with CBR.

• In some CBR systems there are elements of stochastic processes.


Thank you!

Acknowledgement:

Sheuli Paul



Michael M Richter: Sheuli Paul: Stochastic Processes, Machine Learning and Applications. In preparation.Michael M. Richter, Rosina O. Weber: CBR Textbook. Springer 2013.Michael M. Richter, Sebastian v. Mammen: Remarks on the future of AI: machines and communication 61 (1): 41-46 (2011).Michael M. Richter: On the Foundations of Equality and Similarity. In: Logik als Grundlage von Wissen(schaft). (ed. W.Neuser, W. Lenski) p.155-178. Winter, Heidelberg 2010.

Literatur

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case-based reasoning and stochastic processes michael m. richter tu kaiserslautern, university of...

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