legal ARTIFICIAL INTELLIGENCE AND EXPERT SYSTEMS Legal Counseling by munguychaitu

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									                            ARTIFICIAL INTELLIGENCE AND
                                   EXPERT SYSTEMS
                                 Legal Counseling System




ABSTRACT:

Artificial intelligence, area of computer science focusing on creating machines that can engage on
behaviors that humans consider intelligent. This work introduces to various branches of AI, its
applications and its advantages. Expert systems, an application of AI describe how we can use it to
solve real practical problems. A Legal counseling system (legal expert system) intends to provide
intelligent support to legal professionals. It is an attempt to predict the most probable outcome of a
case according to statutory as well as real world knowledge of the legal domain. This paper
contains a detailed discussion on artificial intelligence-based case analysis of theft cases in a real
world perspective.

CONTENTS

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1. INTRODUCTION

2. VARIOUS BRANCHAES OF A.I.

3. A.I.PROBLEMS

4. APPLICATIONS OF A.I.

5. EXPERT SYSTEM

6. PROPOSED LEGAL SYSTEM

  6.1 knowledge structuring

  6.2 knowledge representation

  6.3 C-lattice operators

  6.4 discretion module

  6.5 credibility evaluator

7. ADVANTAGES OF EXPERT SYSTEM

8.CONCLUSION
1. INTRODUCTION
Artificial intelligence can be viewed from a variety of perspectives:
From the perspective of intelligence:
       ‘Artificial intelligence’ is making machines "intelligent" -- acting as we would expect people to
act.
From a research perspective:
       ‘Artificial intelligence’ is the study of how to make computers do things which, at the moment,
people do better"
From a business perspective:
       ‘Artificial intelligence’ is a set of very powerful tools, and methodologies for using those tools
to solve business problems. From a programming perspective:
       ‘Artificial intelligence’ includes the study of symbolic programming, problem solving, and
search.
       2. VARIOUS BRANCHES OF ARTIFICIAL INTELLIGENCE:
       1. Logical Artificial Intelligence: What a program knows about the world in general the facts of
       the specific situation in which it must act and its goals are all represented by sentences of some
       mathematical logical language. The program decides what to do by inferring that certain actions
       are appropriate for achieving its goals.



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2. Search: As programs examine large numbers of possibilities as moves in a chess game or
inferences by a theorem proving program.
3. Pattern recognition: When a program makes observations of some kind, it is often
programmed to compare what it sees with a pattern. For example, a vision program may try to
match a pattern of eyes and nose in a scene in order to find a face. Other patterns, example
natural language text, in chess position etc.
4. Representation: Facts about the world have to be represented in some way.
5. Inferences: From some facts others can be inferred.
6. Common Sense Knowledge and Reasoning: This is the area in which Artificial Intelligence is
farthest from human level. Example, the CYE system contains a large but spotty collection of
common sense facts.
7. Learning from Experience:
The approach of Artificial Intelligence based on connectionism and neural networks specialized in
that.


8. Planning: Planning programs start with general facts about the world, facts about the particular
situation and the statement of a goal. From these they generate a strategy for achieving the goal.
9. Epistemology: This is a study of the kinds of knowledge that are required for solving problems
in the world.
10. Heuristics: A heuristic is a technique that improves the efficiency of a search process, the
possibility by sacrificing the claims of completeness.
3. AI PROBLEMS:
      Formal Tasks (mathematics, games).
      Mundane tasks (perception, robotics, natural language, common sense reasoning).
      Expert tasks (financial analysis, medical diagnostics, engineering, scientific analysis, and
        other areas).
4. APPLICATIONS OF ARTIFICIAL INTELLIGENCE

From airport tarmacs to online job banks to medical labs, artificial intelligence is everywhere.
Applications of Artificial Intelligence have enhanced productivity in business, science,
Engineering and Military.
1. Game playing: You can buy machines that can play master level chess for a few $100/-.
2. Speech Recognition: It is possible to instruct some computers using speech. Thus United
Airlines has replaced its keyboard tree for flight information by a system using speech recognition
of flight numbers and city names.
3. Understanding Natural Language: Natural language processing includes understanding and
generation, as well as other tasks such as multilingual translation. By combining understanding and
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generation systems, it is possible to attack the problem of machine translation, by which we
understand text written in one language and then generate it in another language.
5. EXPERT SYSTEMS:
Expert systems are meant to solve real problems which normally would require a specialized
human expert. In other words, these are systems which provide expert quality advice, diagnosis
and recommendations given real world problems. One of the first expert system was Mycin in
1974 which diagnosed bacterial infections of blood and suggested treatments. Expert systems are
constructed by obtaining the knowledge from a human expert and coding it into a form that a
computer may apply to similar problems. This reliance on the knowledge of a human domain
expert for the system's problem solving strategies is a major feature of expert system.
6. PROPOSED LEGAL SYSTEMS:
The proposed system depicted in figure is a legal counseling system that accepts the current fact
situation of the case from a legal practitioner and interactively proceeds to analyze the case based
on statute and real world information. Processing of a case in a real world perspective demands
interactive case analysis. This system aims at predicting the most probable judgment. It has to
process the following three types of legal information regarding a case.
(a) Technical information consists of particulars of sections of the relevant act invoked in dealing
with the case, i.e. the ingredients and evidence level at which each of the ingredients has been
established. This information regarding a specific case can be represented as an instance of the
section's decision lattice (D-lattice).
(b) Non-technical information or the real world information of the case, such as the details of
how and why the crime was committed can be represented as instances of the corresponding
commonsense lattices (C-lattice).
(c) Formal general information regarding the sentential details of each section is represented as a
sentencing lattice (S-lattice) and it is of static nature.




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When the user interacts with the system, the shell collects the case details through a question-
answering session. The shell used the C-lattice instances to accommodate the details of the real
world information of the present case. Evidence estimator & D-lattice filler gets technical
information of the present case from the shell, and prepares the D-lattice instance representing
the case in view of the relevant section. Case strength evaluator evaluates the corresponding D-
lattice instance to measure the strength of a given case in accordance with the statute.
The discretion module accommodates the experience-based real world knowledge of legal
professionals as non-technical heuristics. Credibility evaluator applies these heuristics on the C-
lattice instances of the case to determine the credibility of the case. Decision maker suggests a
decision on whether the accused has to be convicted or not based on the combined effect of
strength and credibility of the case.
The judgment of a case includes the decision whether to convict or not as well as the sentence to
be undergone by the accused if necessary. If a decision to convict the accused is taken, the
decision-maker enables the sentencing module. Severity evaluator processes the C-lattice
instances of the present case to get a severity measure of the crime committed. Based on this
measure, punishment will be meted out to the accused in accordance with the sentential norms
contained in the relevant S-lattice. According to the norms provided by the S-lattice and the
severity of the present case, sentencing will be made by the sentencing module.
Since human reasoning is being simulated in a specific domain, the system becomes an expert
systems its decision-prediction performance tends to that of a human expert. In any case, this
system has been developed in an attempt to provide intelligent professional assistance to legal
professionals and offers intelligent support to busy legal professionals while applying the regular
domain specific techniques in case analysis so that they can concentrate better on critical aspects
of cases. In this paper the processing of non-technical knowledge to estimate the credibility of a
case is dealt with in detail.
6.1 Knowledge Structuring
LATTICE:

A class of objects/occurrences with a predefined set of attributes can be represented as a lattice.
The specific information regarding a particular object/occurrence can be represented as an
instance of the class lattice. The values of an attribute of the instance lattice can be filled, if and
only if the corresponding class lattice supports that attribute (i.e. if it is a relevant attribute).
Instead of uni-dimensional attributes, the lattice has two-dimensional attributes for the following
benefits.
(1) Two-dimensional attributes make the lattice more expressive and nearer to the natural way of
representing legal information.


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(2) Due to the modularity derived by the two-dimensional attribute lattice, it is preferred by
domain/legal experts. Hence, knowledge acquisition is convenient.
(3) a. Conversion of the domain expert's knowledge into internal knowledge structure is simpler
for the knowledge engineer.
     b. Checks for completeness and making modifications existing knowledge are more
convenient due to the modularity.
The value of an attribute of an instance lattice can either be an atomic value or an instance of
another lattice as dictated by the nature of the attribute.
6.2 Knowledge representation
Non-technical information of a case involves details of the case in layman's view. This
knowledge can be represented using various C-lattices. The set of C-lattices to represent theft
cases are as follows.
(a) Case-Ref: This lattice is at the topmost level in the lattice system. This has to be accessed by
the reference number of the case.
(b) Accused-name: This lattice gives the details of the accused in this case. All relevant known
information of the accused should be filled into various attributes of this lattice.
(c)Execution-Ref: This lattice accommodates the details of the commitment of the crime. These
details are in turn structured into the three lattices - event-no, abettors-name, and item-name.
(d)Event-no: This lattice represents the details of a particular event such as when and where the
event happened.
(e)Abettor-name: This represents the relevant capabilities of the abettors of the case.
(f)Item-name: It represents the characteristics of a particular item of interest.


6.3 C-lattice operators:
      C-lattices provide the structure for organizing the real world/non-technical knowledge of a
particular case. Each of these provides a general structure for a chunk of relevant non-technical
knowledge. Several functions were developed in Common-LISP to operate with these lattices.
The operations needed to store and retrieve the details of a case are as follows:
(1) (Intro-instance <ref-no> case-ref): This function generates an instance of case-ref lattice and
identifies it with <ref-no>.
(2) (Ct-put lattice-id > <attribute-path> < value>): This function is called while storing the
details of a case. The value of the detail is stored in the identified lattice at the location according
to the <attribute-path>. While storing, the function checks the relevancy of the attribute-path.
Automatic introduction of the value as an instance of its compatible lattice is done through this
function.


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(3) (Ct-remove <lattice-id> <attribute-path> <value>); this function can be used if a particular
value of an attribute is found to be wrong and has to be deleted.
The value will be deleted from the list of values of the attribute of the identified lattice.
(4) (Ct-update <lattice-id> <attribute-path> < value>): This function can be used to overwrite the
previous value of an attribute with a new value. When this function is called the <value> will be
stored as a single value of (attribute-path) of the <lattice-id>.
(5) (Ct-get <lattice-id> < attribute-path>): This function will be used to fetch/retrieve the list of
values of (attribute-path) of lattice identified.
(6) (Ct-remove latt (lattice-id)): This function can be used to delete lattices that were introduced
as sub-structures to the lattice-id in a cascaded way. This function will be of use in cases of
withdrawal of a case or cases that are finalized.
6.4 Discretion module
C-lattice instances associated with a case can be processed with the discretion module to evaluate
the credibility of the case. The discretion module consists of heuristic knowledge of judges. This
heuristic knowledge is represented procedurally over the C-lattice operators. Various chunks of
heuristic knowledge are represented as individual 'rules' and a rule cither supports or opposes the
guilt of the accused. Some of the heuristics useful for dealing with theft cases have been
implemented in our legal system. .




6.5 Credibility evaluator
Credibility is a positive real number associated with each case to represent the ‘believability’ of
the case. Then it selects the applicable discretion rules and executes them in an order dictated by
the Credibility is a positive real number associated with each case to represent the offence
involved. Credibility suggests the judgment in view of non-technical information of the case.
6.6 Case 1 Example:
Description of case 1
On 2nd August, 1992, Sunday, at 8-15 p.m. a theft occurred in the house of Reddy, situated at
Banjara Hills, Hyderabad, Reddy returned from his office with a briefcase containing one lakh
rupees in his blue Maruti-92 car. After he relaxed for 5 minutes he found that a man of 25 years
was driving away in his car and immediately noticed that the briefcase containing the cash was
missing. Through investigation it was found that Geetha, the maid servant in the house, had
dropped the briefcase and the car keys to help the accused. Three days later, one Rao was
arrested with a similar red Maruti car in Warangal. The accused produced alibi showing evidence
that he was consulting a doctor in Tata Hospital, Bombay, on the day of the theft at 5-30 p.m.
CASE1. Evaluation follows in context 3.
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(> evaluate C2S-380)
What is the distance in kilometers between HYDERABAD and BOMBAY?
750
Can the accused fly between HYDERABAD and BOMBAY?
Indicate y/n. y
Check whether a flight took off at BOMBAY on 2-8-92 after 6'0 clock and reached
HYDERABAD before 8.
Please indicate y/n. n
C2S-381 INVALID
The court believes the alibi is reasonable.
0 is the value of credibility for the present case C2S-380.
CASE 2: Evaluation follows in context 4.
> (evaluate C2S-380)
What is the distance in kilometers between HYDERABAD and BOMBAY?
750
Can the accused fly between HYDERABAD and BOMBAY?
Indicate y/n: y
Check whether a flight took off at BOMBAY on 2-8-92 after 6'0 clock and reached
HYDERABAD before 8.
Please indicate y/n: y
Is there a possibility to change the colour of CAR 21?
Indicate y/n. y
Did the accused prove his ownership/right of possession regarding each of the following items?
(CAR 21)
Please indicate y/n. n
1-25 is the value of creditability for the present case C2S-380.
7. ADVANTAGES OF EXPERT SYSTEMS:
1. Permanence - Expert systems do not forget, but human experts may
2. Reproducibility - Many copies of an expert system can be made, but training new human
experts is time-consuming and expensive
3. Efficiency - can increase throughput and decrease personnel costs
a. Although expert systems are expensive to build and maintain, they are         inexpensive to
operate
b. Development and maintenance costs can be spread over many users
c. The overall cost can be quite reasonable when compared to expensive and scarce human
experts
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4. Consistency - With expert systems similar transactions handled in the same way. The system
will         make        comparable        recommendations          for      like       situations.
Humans are influenced by
a. Recent effects (most recent information having a disproportionate impact on judgment)
b. Primacy effects (early information dominates the judgment).
5. Documentation - An expert system can provide permanent documentation of the decision
process
6. Completeness - An expert system can review all the transactions, a human expert can only
review a sample
7. Timeliness - Fraud and/or errors can be prevented. Information is available sooner for decision
making.


CONCLUSION:
Computer-based legal systems have to progress a long way to aid legal reasoning rather than
legal information retrieval. The existing legal consultation systems are aimed at certain specific
civil cases and a few of these systems attempt criminal cases. The distinctive features of criminal
cases as against civil cases are the increased effectiveness of non-technical matters in reaching
the judgment. In this paper a model of a judgment prediction system has been proposed. This
model aims at analyzing a specific criminal case through technical as well as non-technical
perspectives and accordingly suggests the judgment.


REFERENCES
        Developments in Applied Artificial Intelligence edited by Ali Moonis, Paul
          Chung, Chris Hinde.
        Artificial Intelligence and Expert Systems for Engineers
          by C S Krishnamoorthy
        www.google.com
        www.howstuffworks.com




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