Philipps, Lothar

English

Open Access LMU

* * * * * • * COUNCIL * * CONSEIL OFEUROPE * * * DE L'EUROPE Proceedings of the 9th Symposium on Legal Data Processing in Europe Bonn, 10-12 October 1989 Systems based on artificial intelligence in the legal field Strasbourg 1991 French edit ion: Les systemes bases sur l'intelligence artificielle dans le domaine juridique ISBN 92-871-1951-1 Strasbourg, Council of Europe, Publishing and Documentation Service ISBN 92-871-1952-X © Copyright, Council of Europe, Strasbourg, 1991 Printed at the Council of Europe - 3 -TABLE OF CONTENTS Page Introduction 5 Programme 7 Reports From Information r e t r i e v a l to active decision support -Introductory Report by the General Rapporteur Professor J . Bing 11 The support of l e g a l decisions by the integration of le g a l Information Systems and expert Systems technology -Austrian delegation, Messrs. Enser, Quirchmayr, Traunmüller and W i l f e r t 33 JUTEXT : a s p e c i a l expert System within the Austrian administration of j u s t i c e - Austrian delegation, Mr. M. Schneider 45 Report on l e g a l expert Systems i n Belgium - Belgian delegation, Mrs. D. Brouwers and Mr. F. Robben 55 Report submitted on behalf of the Danish delegation 65 Report submitted on behalf of the Finnish d e l e g a t i o n , . 71 Report submitted on behalf of the French delegation 75 Computers and l e g a l reasoning - outline of developments in the Federal Republic of Germany - German delegation, Professor H. Fi e d l e r 81 Legal expert Systems i n the Federal Republic of Germany - German delegation, Professor F. Haft 93 A neural network to i d e n t i f y l e g a l precedents - German delegation, Professor L. Philipps 99 The l e g a l expert System s h e l l JUREX - German delegation, Mr. U. Hartleb 107 " I n t e l l i g e n t " access to legal data banks - I t a l i a n delegation, Mrs. F. R o l l e r i 113 - 4 -Page Report submitted on behalf ©>f the Maltese delegation -Dr. J. Cannataci 117 Knovledge-based Systems i n Mörwegian public administration - report submitted on behalf of the Norwegian delegation .... 119 Report submitted on behalf af the Swedish delegation... 127 DOCUMENTRIX - Programme package for computerised associative multi-lingual document storage and r e t r i e v a l , Swiss delegation, Mr. B. Vischer 137 Report submitted on behalf of the Turkish delegation... 147 Report submitted on behalf of the United Kingdom delegat ion 159 General survey of the Situation i n Europe concerning a r t i f i c i a l i n t e lligence Systems i n the legal f i e l d -report presented at the closure of the Symposium by the General Rapporteur Dr. R.E. Susskind 173 Conclusions 191 L i s t of participants . 193 - 99 -9TH SYMPOSIUM ON LEGAL DATA PROCESSING IN EUROPE (CJ-IJ Symp) Bonn, 10 - 12 October 1989 A NEUTRAL NETWORK TO IDENTIFY LEGAL PRECEDENTS Paper submitted by Prof. Dr. Lothar Philipps I n s t i t u t für Rechtsphilosophie und Rechtsinformatik Ludwig-Maximillians-Universität München - 100 -A NEURAL NETWORK TO IDENTIFY LEGAL PRECEDENTS Abstract The authors explore how the special a b i l i t i e s of neural networks (learning of arbitrary input-output pattern combinations t to a li m i t e d extent generalisation of ascertained r e g u l a r i t i e s when non-learned patterns are presented) can be used to b u i l d up an a r t i f i c i a l System which can record and i d e n t i f y l e g a l preGßdents. A method w i l l be introduced i n which precedents i n the area of the law of immaterial damages can he i d e n t i f i e d by use of a neural network : The System s h a l l , with the aid: of a previously supplied l i s t of court decisions, "learn" to predict the magnitude of the expected immaterial damages based on the factual circumstances (type, seriousness and duration of the inju r y , seriousness and duration of consequences, etc ) . The network i s feasible not only i n a combinatorial background (with binary pattern combinations as input and Output), but i n an analog background (with analog input and Output values) as well. In the f i r s t instance, the network operates as a kind of "pattern. c l a s s i f i c a t o r " , i n the second instance a function i s interpolated from the trained input and Output values which synthesises the correct Output values for the respective input values. I. The Neural Networks Approach "Neural networks" / "neural Computing" / " p a r a l l e l d i s t r i b u t e d processing" i n the past three? years has become a key word i n the area of a r t i f i c i a l i n t e l l i g e n c e research. The amazing successes which have been achieved i n some f i e l d s through the use of simulated neural networks have awakened the hope that with t h i s approach the well-known narrow l i m i t s of the current object - or rule-based expert Systems can be overcome. The application of neural networks opens new perspectives, i n pa r t i c u l a r , i n areas i n which ihcomplete or distorted data i s to be processed or where no clear and complete rule System can be found. For example, neural networks have been used, among other things, to transform written texts to phonemes (1),, to clässify sonar targets with the aid of the reflected sonar Signals (2))». to predict the weather based on the development of the atmospheric: pressure during the course of the day (3), and even to predict the futurej development of the stock index based on i t s previous development (4). The?approach i s the same i n a l l cases: The respective input and Output arte* converted into binary or analog values, which s h a l l then form the respective input and Output of the network. Based on a s u f f i c i e n t number ofT sample pattern combinations, the network w i l l be "trained" u n t i l i t prodüces the desired Output pattern when provided with the respective input pattern. Since through the architecture of the network s i m i l a r input patterns produce s i m i l a r Output patterns, after completion of the learning process the System can, within certain l i m i t s , f i n d correct or nearly correct Output patterns even for distorted or non-learned input patterns. In; t h i s fashion, the text-transforming network described above can, after the learning phase, also transform non-learned words with a high rate of accuracy: that i s "generalise" or based on the examples "abstract" the rule from the sample patterns. - 101 -I I . Transfer* to> thei Area of Law Basedi ort the thesis. put forward some years ago by Philipps (5) that l e g a l thinking can be deseribedl as a special kind of pattern recognition, a possible application of surf* neusral network simulations i s apparent i n the area of law : Precedents* scholarly opinions, etc. can be presented to a neural network as combinations of tr a i n i n g patterns, i n which the respective factual circumstances and the l e g a l consequences r e s u l t i n g according the court decision or the academic opinion are converted into a binary or analog pattern. In the same manner the connection between the factual circumstances and the elements of the offence or between the elements of the offence and the lega l consequences could be trained by presenting the respecting input and Output as a binary or analog pattern. This thesis w i l l now be made clear by a concrete application : I I I . An application : Learning precedents i n the Law of Immaterial Damages The applications of a neural network for the determination of legal precedents i s only interesting i f the above-described a b i l i t y of neural networks to generalise can be used. This i s only possible i f a r e l a t i v e l y large number of tr a i n i n g patterns i s presented because only then consistencies i n the presented input patterns can be recognised and transferred to unknown patterns. The German J u r i s d i c t i o n of immaterial damages i s therefore well-suited as a data base for such a project: An enormous number of precedential cases i n table form i s available; the respective factual circumstances are r e l a t i v e l y closely described (through c r i t e r i a such as type, severity and duration of injury and t h e i r e f f e c t s , etc.) and the legal consequences can be summarised i n a Single figger, namely, the amount of the immaterial damages awarded. (1) Sejnowski & Rosenberg, P a r a l l e l Networks that Learn to Pronounce English Text (2) Gorman & Sejnowski, Analysis of Hidden Units i n a Layered Network Trained to c l a s s i f y Sonar Targets (3) Described i n NeuralWarelnc, NeuralWorks Explorer Networks I pp 382-384 (4) NeuralWarelnc, NeuralWorks Explorer Networks I, pp 455-457 (5) P h i l i p p s , Täter und Teilnehmer - Versuch und Irrtum; ein Modell für die rechtswissenschaftliche Analyse, Rechtstheorie Band 5 (1974), pp 125-146 - 102 -1. Preparation of the Factual Circumstances and Legal Consequences The precedents can be prepared as data basis as follows : Type, severity and duration of the injury : A c e r t a i n number of di f f e r e n t i n j u r i e s can be distinguished according to the respective body part affected (e.g. head inj u r y , injury of the hand, shock, etc.) so that a l l the elements of an offence can be categorised. For each injured part of the body the severity and duration of the in j u r y i s then determined. Input : Severity and duration of the effects : The severity of the effects of the entire injury i s divided into levels according to the degree of diminishment of earning capacity (DEC), For each l e v e l (10#, 20% DEC, etc.) a period of time i s determined during which t h i s diminished capacity existed. Further c r i t e r i a which could be important i n some way for the determination of the amount of the immaterial damages : The sex of the injured person (male/female), whether the occupation of the injured person had played an especially important role (yes/no) and whether the injury occurred due to medical malpractice (yes/no). Output : The amount of the immaterial damages (adjusted for the comparative contributory negligence of the injured person). Input : Output Type of injury Body part 1 Body part 2 (...) Body part X Effects DEC 10% 20% (...) 100% Duration Severity Duration Severity Duration Severity Duration Duration Duration Amount of immaterial damages i n German Marks Sex Occupation c r u c i a l P a r t i c u l a r severity Medical malpractice Male/Female yes/no yes/no yes/no As data basis for our experiments we took 200 court decisions from the immaterial damages table of the ADAC (German Automobile Club). From these, 32 d i f f e r e n t types of injuries/body parts and degrees of diminishes earning capacity were deterimined. The length and degree of injury capacity were each divided into four l e v e l s . Through t h i s process, the most important factual characteristics which are or could be essential for the amount of immaterial damages can be determined. This exact C l a s s i f i c a t i o n has the advantage that inconsistencies within the trained input/output patterns (same input, diff e r e n t Output) can be held to a minimum. The remaining inconsistencies must be accepted, as the law i n the area of immaterial damages i s anything other than uniform. - 103 -2. Transfer to the Input-Output Pattern This data basis can now be transferred to the concrete input/output pattern for the network, For a c l a s s i c a l network of neurons based on McCulloch's model, the input/output pattern i s conceivable as a binary as well as an analog background; the network can then be trained according to the back propagation algorithm. Both p o s s i b i l i t i e s are currently being investigated by us i n a Software Simulation. The f i r s t results are very promising; however, due to the very limited Computing capacity a v a i l a b l e to us (PC 80386 with 80387 co-processor) no conclusive results can yet be presented because the Simulation of networks of thi s magnitude requires an enormous amount of c a l c u l a t i n g . For t h i s reason, both p o s s i b i l i t i e s s h a l l only be b r i e f l y introduced : a) Binary Background For each l e v e l of the severity and duration of an inju r y and the duration of the respective diminishment i n earning capacity, an input neuron i s reserved as the binary input. For four l e v e l s each these are therefore : 32 body parts * each (4 levels of severity of inju r y +4 levels of duration of injury) + 5 gradations of diminishment of earning capacity * 4 l e v e l s of duration • 1 neuron to distinguish male/female • 1 neuron occupation yes/no + 1 neuron p a r t i c u l a r severity yes/no + 1 neuron medical malpractice yes/no 3 2 * 8 + 5 * 4 + 4 = 280 input neurons assigned (!). for the creation of the output pattern, the awarded amount of immaterial damages are divides into 13 levels for which one output neuron i s reserved for each, result i n g i n a t o t a l of 13 output neurons. The Network has one or more hidden layers. As activ a t i o n function, a sigmoid function i s employed for a l l neurons of the hidden and output c e l l s . b) Analog Background : One analog c e l l i s s u f f i c i e n t for the severity and duration of the injury and the duration of the respective diminishment of earning capacity. This results i n a number of - 104 -32 body parts * each ( 1 levels of severity of i n j u r y +1 levels of duration of injury) + 5 gradations of diminishment of earning capacity * 1 neuron for duration • 1 neuron to distinguistt male/female + 1 neuron occupation xes/no + 1 neuron p a r t i c u l a r severity yes/no + 1 neuron medical malpractice yes/no 32 body parts * 2 + 5 * 1 • 4 = 73 input neurons. For the output values to be trained, only one analog neuron i s required. The activation function of the output c e l l s must have a value ränge of at least 1-300,000 (hightest German Mark value of out tr a i n i n g court awards). For exampie, a, simple l i n e a r a c t i v a t i o n function i s usable. Based on the experience gathered thus f a r , a combination of hidden c e l l s with sigmoid and those with sinus activation function i s usable. Based on the experience gathered thus f a r , a combination of hidden c e l l s with sigmoid and those with sinus activation functions are preferred for synthesising an analog output value from input values. In r e a l i t y , the System interpolates from the trained exampie values a function (in our case with 74 dimensions!) which for the learned input values results i n the correct output values and for the non-learned input values i n at least acceptable output values. - 105 -A P P E N D I X I. A short introjdiuctiani i n t o the neural networks idea The basic icfea) ©>f & neural network i s the idea that an a r t i f i c i a l System of the human brai n with s i m i l a r a b i l i t i e s such as associative and abstract thinking can only be achieved i f i t i s based on an architecture and working method which i s s i m i l a r to the human brain : Similar to the b r a i n , neural networks consist of a multitude öf Single c e l l s (neurons) which are closely interconnected. Each neuron processes the Signals sent to i t from an adjoining c e l l i n accordance with a simple mathematical or l o g i c a l function and further sends the res u l t to the neurons connected at i t s output side. I f a signal from the outside i s given to a number of neurons (input neurons), t h i s signal i s spread over the network and i s transformed into an output signal which i s emitted by the Output neurons. A neural network i s not to be programmed, but i t i s trained : i f the input-output combination which i s to be learned i s aplied to the input and output side, the connections between the neurons are so marked or weakened, that l a t e r , each time the input signal i s applied, the corresponding output signal w i l l be produced independently. The learning process therefore does not take place i n the neurons, but rather i n the connections (with respect to the brain, the synapses). With respect to our simulations, we followed the c l a s s i c a l model of the neuron as set forth by McCulloch and, i n p a r t i c u l a r , Rosenblatt. We used a feed-forward network, which i s to be trained by the backpropagation algorithmus. An exact mathematical description of th i s network and the employed model i s not to be given here, since there i s already a l o t of l i t e r a t u r e about t h i s subject. (1) (1) An introduction i s given i n McClelland : Rumelhart, Volume 1 : Poundations, Cambridge Massachusetts USA 19Ö5. An overview i s given by Weick. M., Hopfield Model. Boltzmann Machine, Multilayer Perceptron and Selected Applications i n Haken (ed.) Neural and Synergetic Computers, pp. 111-119. 

A Neural Network to Identify Legal Precedents

Universität München: Elektronischen Publikationen

* * * * * • * 
COUNCIL * * CONSEIL 
OFEUROPE * * * DE L'EUROPE 
Proceedings of the 9th Symposium 
on Legal Data Processing in Europe 
Bonn, 10-12 October 1989 
Systems based 
on artificial intelligence in the legal field 
Strasbourg 1991 
French edit ion: 
Les systemes bases sur l'intelligence artificielle dans le domaine juridique 
ISBN 92-871-1951-1 
Strasbourg, Council of Europe, Publishing and Documentation Service 
ISBN 92-871-1952-X 
© Copyright, Council of Europe, Strasbourg, 1991 
Printed at the Council of Europe 
- 3 -
TABLE OF CONTENTS 
Page 
Introduction 5 
Programme 7 
Reports 
From Information r e t r i e v a l to active decision support -
Introductory Report by the General Rapporteur 
Professor J . Bing 11 
The support of l e g a l decisions by the integration of 
le g a l Information Systems and expert Systems technology -
Austrian delegation, Messrs. Enser, Quirchmayr, 
Traunmüller and W i l f e r t 33 
JUTEXT : a s p e c i a l expert System within the Austrian 
administration of j u s t i c e - Austrian delegation, 
Mr. M. Schneider 45 
Report on l e g a l expert Systems i n Belgium - Belgian 
delegation, Mrs. D. Brouwers and Mr. F. Robben 55 
Report submitted on behalf of the Danish delegation 65 
Report submitted on behalf of the Finnish d e l e g a t i o n , . 71 
Report submitted on behalf of the French delegation 75 
Computers and l e g a l reasoning - outline of developments 
in the Federal Republic of Germany - German delegation, 
Professor H. Fi e d l e r 81 
Legal expert Systems i n the Federal Republic of Germany 
- German delegation, Professor F. Haft 93 
A neural network to i d e n t i f y l e g a l precedents - German 
delegation, Professor L. Philipps 99 
The l e g a l expert System s h e l l JUREX - German delegation, 
Mr. U. Hartleb 107 
" I n t e l l i g e n t " access to legal data banks - I t a l i a n 
delegation, Mrs. F. R o l l e r i 113 
- 4 -
Page 
Report submitted on behalf ©>f the Maltese delegation -
Dr. J. Cannataci 117 
Knovledge-based Systems i n Mörwegian public 
administration - report submitted on behalf of the 
Norwegian delegation .... 119 
Report submitted on behalf af the Swedish delegation... 127 
DOCUMENTRIX - Programme package for computerised 
associative multi-lingual document storage and r e t r i e v a l , 
Swiss delegation, Mr. B. Vischer 137 
Report submitted on behalf of the Turkish delegation... 147 
Report submitted on behalf of the United Kingdom 
delegat ion 159 
General survey of the Situation i n Europe concerning 
a r t i f i c i a l i n t e lligence Systems i n the legal f i e l d -
report presented at the closure of the Symposium by 
the General Rapporteur Dr. R.E. Susskind 173 
Conclusions 191 
L i s t of participants . 193 
- 99 -
9TH SYMPOSIUM ON LEGAL DATA PROCESSING IN EUROPE (CJ-IJ Symp) 
Bonn, 10 - 12 October 1989 
A NEUTRAL NETWORK TO IDENTIFY LEGAL PRECEDENTS 
Paper submitted by 
Prof. Dr. Lothar Philipps 
I n s t i t u t für Rechtsphilosophie und Rechtsinformatik 
Ludwig-Maximillians-Universität München 
- 100 -
A NEURAL NETWORK TO IDENTIFY LEGAL PRECEDENTS 
Abstract 
The authors explore how the special a b i l i t i e s of neural networks 
(learning of arbitrary input-output pattern combinations t to a li m i t e d 
extent generalisation of ascertained r e g u l a r i t i e s when non-learned patterns 
are presented) can be used to b u i l d up an a r t i f i c i a l System which can 
record and i d e n t i f y l e g a l preGßdents. 
A method w i l l be introduced i n which precedents i n the area of the 
law of immaterial damages can he i d e n t i f i e d by use of a neural network : 
The System s h a l l , with the aid: of a previously supplied l i s t of court 
decisions, "learn" to predict the magnitude of the expected immaterial 
damages based on the factual circumstances (type, seriousness and duration 
of the inju r y , seriousness and duration of consequences, etc ) . The network 
i s feasible not only i n a combinatorial background (with binary pattern 
combinations as input and Output), but i n an analog background (with analog 
input and Output values) as well. In the f i r s t instance, the network 
operates as a kind of "pattern. c l a s s i f i c a t o r " , i n the second instance a 
function i s interpolated from the trained input and Output values which 
synthesises the correct Output values for the respective input values. 
I. The Neural Networks Approach 
"Neural networks" / "neural Computing" / " p a r a l l e l d i s t r i b u t e d 
processing" i n the past three? years has become a key word i n the area of 
a r t i f i c i a l i n t e l l i g e n c e research. The amazing successes which have been 
achieved i n some f i e l d s through the use of simulated neural networks have 
awakened the hope that with t h i s approach the well-known narrow l i m i t s of 
the current object - or rule-based expert Systems can be overcome. 
The application of neural networks opens new perspectives, i n 
pa r t i c u l a r , i n areas i n which ihcomplete or distorted data i s to be 
processed or where no clear and complete rule System can be found. For 
example, neural networks have been used, among other things, to transform 
written texts to phonemes (1),, to clässify sonar targets with the aid of 
the reflected sonar Signals (2))». to predict the weather based on the 
development of the atmospheric: pressure during the course of the day (3), 
and even to predict the futurej development of the stock index based on i t s 
previous development (4). The?approach i s the same i n a l l cases: The 
respective input and Output arte* converted into binary or analog values, 
which s h a l l then form the respective input and Output of the network. 
Based on a s u f f i c i e n t number ofT sample pattern combinations, the network 
w i l l be "trained" u n t i l i t prodüces the desired Output pattern when 
provided with the respective input pattern. Since through the architecture 
of the network s i m i l a r input patterns produce s i m i l a r Output patterns, 
after completion of the learning process the System can, within certain 
l i m i t s , f i n d correct or nearly correct Output patterns even for distorted or 
non-learned input patterns. In; t h i s fashion, the text-transforming network 
described above can, after the learning phase, also transform non-learned 
words with a high rate of accuracy: that i s "generalise" or based on the 
examples "abstract" the rule from the sample patterns. 
- 101 -
I I . Transfer* to> thei Area of Law 
Basedi ort the thesis. put forward some years ago by Philipps (5) that 
l e g a l thinking can be deseribedl as a special kind of pattern recognition, a 
possible application of surf* neusral network simulations i s apparent i n the 
area of law : Precedents* scholarly opinions, etc. can be presented to a 
neural network as combinations of tr a i n i n g patterns, i n which the 
respective factual circumstances and the l e g a l consequences r e s u l t i n g 
according the court decision or the academic opinion are converted into a 
binary or analog pattern. In the same manner the connection between the 
factual circumstances and the elements of the offence or between the 
elements of the offence and the lega l consequences could be trained by 
presenting the respecting input and Output as a binary or analog pattern. 
This thesis w i l l now be made clear by a concrete application : 
I I I . An application : Learning precedents i n the Law of 
Immaterial Damages 
The applications of a neural network for the determination of legal 
precedents i s only interesting i f the above-described a b i l i t y of neural 
networks to generalise can be used. This i s only possible i f a r e l a t i v e l y 
large number of tr a i n i n g patterns i s presented because only then 
consistencies i n the presented input patterns can be recognised and 
transferred to unknown patterns. 
The German J u r i s d i c t i o n of immaterial damages i s therefore 
well-suited as a data base for such a project: An enormous number of 
precedential cases i n table form i s available; the respective factual 
circumstances are r e l a t i v e l y closely described (through c r i t e r i a such as 
type, severity and duration of injury and t h e i r e f f e c t s , etc.) and the 
legal consequences can be summarised i n a Single figger, namely, the amount 
of the immaterial damages awarded. 
(1) Sejnowski & Rosenberg, P a r a l l e l Networks that Learn to Pronounce 
English Text 
(2) Gorman & Sejnowski, Analysis of Hidden Units i n a Layered Network 
Trained to c l a s s i f y Sonar Targets 
(3) Described i n NeuralWarelnc, NeuralWorks Explorer Networks I 
pp 382-384 
(4) NeuralWarelnc, NeuralWorks Explorer Networks I, pp 455-457 
(5) P h i l i p p s , Täter und Teilnehmer - Versuch und Irrtum; ein Modell 
für die rechtswissenschaftliche Analyse, Rechtstheorie Band 5 
(1974), pp 125-146 
- 102 -
1. Preparation of the Factual Circumstances and Legal Consequences 
The precedents can be prepared as data basis as follows : 
Type, severity and duration of the injury : A c e r t a i n number of 
di f f e r e n t i n j u r i e s can be distinguished according to the respective body 
part affected (e.g. head inj u r y , injury of the hand, shock, etc.) so that 
a l l the elements of an offence can be categorised. For each injured part 
of the body the severity and duration of the in j u r y i s then determined. 
Input : Severity and duration of the effects : The severity of 
the effects of the entire injury i s divided into levels according to the 
degree of diminishment of earning capacity (DEC), For each l e v e l (10#, 
20% DEC, etc.) a period of time i s determined during which t h i s diminished 
capacity existed. Further c r i t e r i a which could be important i n some way 
for the determination of the amount of the immaterial damages : The sex of 
the injured person (male/female), whether the occupation of the injured 
person had played an especially important role (yes/no) and whether the 
injury occurred due to medical malpractice (yes/no). 
Output : The amount of the immaterial damages (adjusted for the 
comparative contributory negligence of the injured person). 
Input : Output 
Type of injury Body part 1 
Body part 2 
(...) 
Body part X 
Effects DEC 10% 
20% 
(...) 
100% 
Duration 
Severity 
Duration 
Severity 
Duration 
Severity 
Duration 
Duration 
Duration 
Amount of immaterial damages 
i n German Marks 
Sex 
Occupation c r u c i a l 
P a r t i c u l a r severity 
Medical malpractice 
Male/Female 
yes/no 
yes/no 
yes/no 
As data basis for our experiments we took 200 court decisions from 
the immaterial damages table of the ADAC (German Automobile Club). From 
these, 32 d i f f e r e n t types of injuries/body parts and degrees of diminishes 
earning capacity were deterimined. The length and degree of injury 
capacity were each divided into four l e v e l s . Through t h i s process, the 
most important factual characteristics which are or could be essential for 
the amount of immaterial damages can be determined. This exact 
C l a s s i f i c a t i o n has the advantage that inconsistencies within the trained 
input/output patterns (same input, diff e r e n t Output) can be held to a 
minimum. The remaining inconsistencies must be accepted, as the law i n the 
area of immaterial damages i s anything other than uniform. 
- 103 -
2. Transfer to the Input-Output Pattern 
This data basis can now be transferred to the concrete input/output 
pattern for the network, For a c l a s s i c a l network of neurons based on 
McCulloch's model, the input/output pattern i s conceivable as a binary as 
well as an analog background; the network can then be trained according to 
the back propagation algorithm. 
Both p o s s i b i l i t i e s are currently being investigated by us i n a 
Software Simulation. The f i r s t results are very promising; however, due to 
the very limited Computing capacity a v a i l a b l e to us (PC 80386 with 80387 
co-processor) no conclusive results can yet be presented because the 
Simulation of networks of thi s magnitude requires an enormous amount of 
c a l c u l a t i n g . For t h i s reason, both p o s s i b i l i t i e s s h a l l only be b r i e f l y 
introduced : 
a) Binary Background 
For each l e v e l of the severity and duration of an inju r y and the 
duration of the respective diminishment i n earning capacity, an input 
neuron i s reserved as the binary input. For four l e v e l s each these are 
therefore : 
32 body parts * each (4 levels of severity of inju r y 
+4 levels of duration of injury) 
+ 5 gradations of diminishment of earning capacity * 4 l e v e l s of 
duration 
• 1 neuron to distinguish male/female 
• 1 neuron occupation yes/no 
+ 1 neuron p a r t i c u l a r severity yes/no 
+ 1 neuron medical malpractice yes/no 
3 2 * 8 + 5 * 4 + 4 = 280 input neurons assigned (!). 
for the creation of the output pattern, the awarded amount of immaterial 
damages are divides into 13 levels for which one output neuron i s reserved 
for each, result i n g i n a t o t a l of 13 output neurons. 
The Network has one or more hidden layers. As activ a t i o n 
function, a sigmoid function i s employed for a l l neurons of the hidden and 
output c e l l s . 
b) Analog Background : 
One analog c e l l i s s u f f i c i e n t for the severity and duration of the 
injury and the duration of the respective diminishment of earning 
capacity. This results i n a number of 
- 104 -
32 body parts * each ( 1 levels of severity of i n j u r y 
+1 levels of duration of injury) 
+ 5 gradations of diminishment of earning capacity * 1 neuron for 
duration 
• 1 neuron to distinguistt male/female 
+ 1 neuron occupation xes/no 
+ 1 neuron p a r t i c u l a r severity yes/no 
+ 1 neuron medical malpractice yes/no 
32 body parts * 2 + 5 * 1 • 4 = 73 input neurons. 
For the output values to be trained, only one analog neuron i s 
required. The activation function of the output c e l l s must have a value 
ränge of at least 1-300,000 (hightest German Mark value of out tr a i n i n g 
court awards). For exampie, a, simple l i n e a r a c t i v a t i o n function i s usable. 
Based on the experience gathered thus f a r , a combination of hidden 
c e l l s with sigmoid and those with sinus activation function i s usable. 
Based on the experience gathered thus f a r , a combination of hidden c e l l s 
with sigmoid and those with sinus activation functions are preferred for 
synthesising an analog output value from input values. In r e a l i t y , the 
System interpolates from the trained exampie values a function (in our case 
with 74 dimensions!) which for the learned input values results i n the 
correct output values and for the non-learned input values i n at least 
acceptable output values. 
- 105 -
A P P E N D I X 
I. A short introjdiuctiani i n t o the neural networks idea 
The basic icfea) ©>f & neural network i s the idea that an a r t i f i c i a l 
System of the human brai n with s i m i l a r a b i l i t i e s such as associative and 
abstract thinking can only be achieved i f i t i s based on an architecture 
and working method which i s s i m i l a r to the human brain : Similar to the 
b r a i n , neural networks consist of a multitude öf Single c e l l s (neurons) 
which are closely interconnected. Each neuron processes the Signals sent 
to i t from an adjoining c e l l i n accordance with a simple mathematical or 
l o g i c a l function and further sends the res u l t to the neurons connected at 
i t s output side. I f a signal from the outside i s given to a number of 
neurons (input neurons), t h i s signal i s spread over the network and i s 
transformed into an output signal which i s emitted by the Output neurons. 
A neural network i s not to be programmed, but i t i s trained : i f 
the input-output combination which i s to be learned i s aplied to the input 
and output side, the connections between the neurons are so marked or 
weakened, that l a t e r , each time the input signal i s applied, the 
corresponding output signal w i l l be produced independently. The learning 
process therefore does not take place i n the neurons, but rather i n the 
connections (with respect to the brain, the synapses). 
With respect to our simulations, we followed the c l a s s i c a l model of 
the neuron as set forth by McCulloch and, i n p a r t i c u l a r , Rosenblatt. We 
used a feed-forward network, which i s to be trained by the backpropagation 
algorithmus. An exact mathematical description of th i s network and the 
employed model i s not to be given here, since there i s already a l o t of 
l i t e r a t u r e about t h i s subject. (1) 
(1) An introduction i s given i n McClelland : Rumelhart, Volume 1 : 
Poundations, Cambridge Massachusetts USA 19Ö5. 
An overview i s given by Weick. M., Hopfield Model. Boltzmann 
Machine, Multilayer Perceptron and Selected Applications i n Haken 
(ed.) Neural and Synergetic Computers, pp. 111-119. 


https://epub.ub.uni-muenchen.de/4919/1/4919.pdf

A Neural Network to Identify Legal Precedents

Abstract

Similar works

Full text

Available Versions

Open Access LMU

Universität München: Elektronischen Publikationen