jehumtine / lawlia

LAWLIA is an open-source computational legal framework designed to revolutionize legal reasoning and analysis. It combines the power of large language models with a structured syntactical grammar to facilitate precise legal assessments, truth values, and verdicts. LAWLIA is the future of computational jurisprudence

LAWLIA - Bridging Law and Computation

Table of Contents:

• Introduction

• Key Concepts

• Syntactical Computation

• How LAWLIA Works

• The Functioning of the LAWLIA Legal Code

• Syntactical Computation and Legal Precision

• Using LAWLIA for Legal Queries

• Getting Started

• Contributing

• License

Introduction

Welcome to LAWLIA, the innovative framework that bridges the worlds of law and computation. LAWLIA is not just a language for legal discourse; it's a computational framework that intrinsically embodies the very nature of law – a system of regulated and reproducible governance over complex human interactions.

At its core, LAWLIA is founded on the profound realization that legal problems, by their inherent nature, are fundamentally computational challenges. These challenges entail the meticulous evaluation of factual inputs against an ever-expanding body of legal rules and principles, a sort of legal computational machine to output precise verdicts or resolutions. LAWLIA accentuates this computational essence by embracing the concept that the law itself is, in essence, a language – one that defines and regulates human behavior through a structured framework of rules, procedures, and precedents.

This GitHub repository hosts the LAWLIA code, which is designed to be a pioneering computational legal grammar that transcends traditional legal discourse, embracing mathematical precision and objectivity. LAWLIA's computational approach revolutionizes the legal domain, underpinning the development of intelligent autonomous agents capable of delivering accurate legal outcomes.

Key Concepts

Syntactical Computation

Lawlia's syntactical computation is the process by which legal statements, arguments, and constructs are distilled into a structured, mathematical format. It involves the transformation of natural language expressions into a formal computational representation. This transformation eradicates linguistic ambiguity and facilitates rigorous analysis based on defined rules and operations.

1. (party) Mtine enters into a contract with Chansa(party) under the English Contract Law (governing_law).

• This sentence defines the fundamental structure of a contractual relationship, specifying the parties involved and the governing law.

2. In case of Fire(event), Mwansa (party) shall be liable for Damages (penalty).

• This sentence outlines a contingency within a contract, defining the consequences in the event of a specified occurrence.

3. The (court_name) local court shall have jurisdiction over disputes arising from this contract.

• This sentence establishes the jurisdiction of a particular court concerning contractual disputes.

4. This (document_type) is hereby executed by (party) on (date).

• It signifies the formal execution of a legal document by a party on a specific date.

5. Notwithstanding any provision herein, (exception).

In each of these examples, Lawlia's computational grammar transforms complex legal concepts into a structured and formal representation. The use of parentheses denotes the roles and relationships of entities, actions, and attributes within the legal statement. This syntactical computation facilitates precise legal analysis, enabling Lawlia to perform tasks like truth assessment, precedent benchmarking, and decision-making based on mathematical rigor.

Leveraging Large Language Models (LLMs)

The development of LAWLIA involves two distinct yet complementary approaches. The first entails the collaborative efforts of experts in computer science and legal professionals. This collaborative endeavor dissects legal cases, extracting computational information embedded within them. From this information, LAWLIA constructs a systematic framework of classes, functions, and rules, each representing different branches of law and various legal scenarios.

The second approach leverages the capabilities of Large Language Models (LLMs). These models, such as GPT, are well-versed in natural language processing and understanding. They ingest vast troves of legal texts, constitutions, and jurisprudential works, synthesizing this knowledge into computational code. Through this method, LAWLIA's computational language expands continually, enriched by the collective wisdom of legal literature.

Classes, Functions, and Legal Scenarios

LAWLIA's modular design revolves around the concept of classes, functions, and rules. These elements encapsulate different branches of law and various legal scenarios. Each class represents a specific legal domain, while functions within these classes handle different legal scenarios or permutations. Class variables hold the required information for a case within a specific legal branch to be solved.

• Classes: Lawlia defines distinct classes for various branches of law, such as contract law, tort law, criminal law, and more. Each class encompasses the rules and legal constructs specific to that domain.

• Functions: Functions within Lawlia represent thought processes of which are required to evaluate different permutations of legal scenarios that can occur within a particular branch of law. These functions encapsulate the logic and decision-making processes necessary to address legal issues.

• Class Variables: Class variables within Lawlia hold the required information for a case within a specific legal branch to be solved. They serve as input data that Lawlia utilizes to provide legal verdicts or decisions.

Using LAWLIA for Legal Queries

LAWLIA's predefined classes and syntactical grammar for various legal domains enable autonomous agents to address general legal queries systematically. By inputting relevant case details and legal information, the agent harnesses LAWLIA's computational power to analyze legal scenarios, assess compliance with legal standards, and provide informed legal responses.

In conclusion, Lawlia stands as a computational legal grammar that bridges the realms of law and computation.

illustration of how an autonomous agent could use Lawlia for general legal queries by providing sample prompts and showcasing how Lawlia's computational legal framework processes these queries. These prompts cover various aspects of law, demonstrating Lawlia's versatility in handling diverse legal questions:

1. Contract Law Query:

Prompt to Agent: "Analyze a contract between Party A and Party B. Determine the validity of the contract and identify any breach of contract clauses."

Agent's Use of Lawlia:

• The agent creates an instance of the ContractLaw class in Lawlia.

• It defines the contract terms, parties involved, and contract clauses.

• Lawlia processes this information to assess the contract's validity and identify any breach of contract clauses based on predefined rules within the Lawlia computational legal framework.

2. Family Law Query:

Prompt to Agent: "Evaluate a child custody case between Parent X and Parent Y. Consider factors such as the child's best interests and parental responsibilities."

Agent's Use of Lawlia:

• The agent utilizes Lawlia's FamilyLaw class to represent the child custody case.

• It provides relevant details about the parents, the child, and the circumstances.

• Lawlia's syntactical grammar for family law assists in assessing the child's best interests and parental responsibilities, offering an informed analysis.

3. Criminal Law Query:

Prompt to Agent: "Analyze a criminal case involving Defendant Z. Determine the charges, evidence, and potential legal defenses."

Agent's Use of Lawlia:

• The agent employs Lawlia's CriminalLaw class to model the criminal case.

• It specifies details about the defendant, the charges, and the available evidence.

• Lawlia's computational framework processes the information, identifies the charges, reviews the evidence, and suggests potential legal defenses based on established legal principles.

4. Intellectual Property Query:

Prompt to Agent: "Assess a trademark registration application. Verify the distinctiveness and potential for confusion with existing trademarks."

Agent's Use of Lawlia:

• The agent leverages Lawlia's IntellectualPropertyLaw class to handle the trademark registration application.

• It provides information about the proposed trademark and existing trademarks.

• Lawlia's syntactical grammar for intellectual property law assesses the distinctiveness of the proposed trademark and examines the potential for confusion with existing trademarks.

5. Real Estate Query:

Prompt to Agent: "Examine a real estate transaction between Buyer C and Seller D. Verify compliance with local zoning laws and contractual obligations."

Agent's Use of Lawlia:

• The agent utilizes Lawlia's RealEstateLaw class to represent the real estate transaction.

• It includes details about the buyer, seller, property, and local zoning laws.

• Lawlia's computational legal framework reviews the transaction for compliance with zoning laws and contractual obligations, offering insights based on established legal standards.

In these examples, the autonomous agent seamlessly integrates Lawlia into its decision-making process. Lawlia's predefined classes and syntactical grammar for various legal domains enable the agent to address general legal queries systematically. By inputting relevant case details and legal information, the agent harnesses Lawlia's computational power to analyze legal scenarios, assess compliance with legal standards, and provide informed legal responses.

Below is not a hard coded aspect of the computational language rather a way in which an autonomous agent can use the predifined structure of LAWLIA to reason, not code to be run but a reasoning process withing the framework of lawlia:

# Import the Lawlia classes and functions (simplified representation)

from lawlia.contract_law import ContractLaw

from lawlia.family_law import FamilyLaw

from lawlia.criminal_law import CriminalLaw

from lawlia.intellectual_property_law import IntellectualPropertyLaw

from lawlia.real_estate_law import RealEstateLaw

  

# 1. Contract Law Query

contract_property_sale = ContractLaw(

contract_name="Property Sale Contract",

parties=["Party A (Seller)", "Party B (Buyer)"],

effective_date="2023-01-01",

expiration_date="2023-12-31",

terms="Sale of property with security system",

governing_law="State of [Governing State]"

)

  

# Agent provides relevant contract details

party_a_statement = "The property has a fully functional security system installed."

contract_property_sale.assert_misrepresentation(party_a_statement)

misrepresentation_result = contract_property_sale.evaluate_misrepresentation()

  

if "Misrepresentation" in misrepresentation_result:

party_b_demands = "Compensation"

resolution_result = contract_property_sale.resolve_contract_dispute(party_b_demands, None)

else:

resolution_result = "No misrepresentation asserted."

  

# 2. Family Law Query

child_custody_case = FamilyLaw(

case_name="Child Custody Case",

parents=["Parent X", "Parent Y"],

child_name="Child Z",

case_details="Child custody dispute between parents."

)

  

# Agent provides relevant details

best_interests_assessment = child_custody_case.assess_childs_best_interests()

parental_responsibilities = child_custody_case.evaluate_parental_responsibilities()

  

# 3. Criminal Law Query

criminal_case = CriminalLaw(

case_name="Criminal Case",

defendant="Defendant Z",

charges=["Charge A", "Charge B"],

evidence="Evidence description."

)

  

# Agent provides relevant details

charge_identification = criminal_case.identify_charges()

evidence_review = criminal_case.review_evidence()

legal_defenses = criminal_case.suggest_legal_defenses()

  

# 4. Intellectual Property Query

trademark_application = IntellectualPropertyLaw(

application_name="Trademark Application",

proposed_trademark="Proposed Trademark",

existing_trademarks=["Existing Trademark 1", "Existing Trademark 2"]

)

  

# Agent provides relevant details

distinctiveness_verification = trademark_application.verify_distinctiveness()

confusion_assessment = trademark_application.assess_confusion()

  

# 5. Real Estate Query

real_estate_transaction = RealEstateLaw(

transaction_name="Real Estate Transaction",

buyer="Buyer C",

seller="Seller D",

property_details="Property description",

zoning_laws="Local zoning laws"

)

  

# Agent provides relevant details

zoning_compliance = real_estate_transaction.check_zoning_compliance()

contractual_obligations = real_estate_transaction.verify_contractual_obligations()

  

# Displaying Results (simplified representation)

print("Contract Law Query Results:")

print(f"Misrepresentation Result: {misrepresentation_result}")

print(f"Resolution Result: {resolution_result}")

  

print("\nFamily Law Query Results:")

print(f"Child's Best Interests: {best_interests_assessment}")

print(f"Parental Responsibilities: {parental_responsibilities}")

  

print("\nCriminal Law Query Results:")

print(f"Charges Identified: {charge_identification}")

print(f"Evidence Review: {evidence_review}")

print(f"Legal Defenses: {legal_defenses}")

  

print("\nIntellectual Property Query Results:")

print(f"Distinctiveness Verification: {distinctiveness_verification}")

print(f"Confusion Assessment: {confusion_assessment}")

  

print("\nReal Estate Query Results:")

print(f"Zoning Compliance: {zoning_compliance}")

print(f"Contractual Obligations: {contractual_obligations}")

  

In these examples, the autonomous agent employs Lawlia's computational legal classes and functions to handle different legal queries effectively. Each class is tailored to a specific legal domain, allowing the agent to input relevant case details and retrieve computed legal responses. This approach ensures consistency, accuracy, and efficiency in addressing various legal scenarios.

The aforementioned examples were produced by ChatGPT in response to a streamlined rendition of the LAWLIA code structure. Lawlia operates within the guiding linguistic axiom that specific grammars and structures will produce specific forms of reasoning. This axiom for now which we will refer to Thought-Grammar dependence. This is at the heart of the effectiveness of the lawlia code. Natural Language Generation models will be forced to follow this rigourously and mathematically defined computational grammar allowing it to produce consistent and fair results.

---

Getting Started with LAWLIA

LAWLIA is an innovative computational framework that bridges the realms of law and computation. It's designed to assist in legal reasoning, decision-making, and analysis by applying mathematical precision and computational acumen to legal problems. This guide will help you get started with LAWLIA and harness its power for various legal tasks.

How to Run

For the first iterative session of LAWLIA it can be implemented by feeding the entirety of the computational code and have it simulate a computer running that code and output based on that code.

Example:

Human:Simulate the operation of a computer program designed to emulate LAWLIA, a legal
reasoning system. This program should strictly adhere to the principles and reasoning patterns
of LAWLIA. Your task is to provide a comprehensive step-by-step demonstration of how this
program would analyse and respond to a legal question while processing a hypothetical legal
case. The demonstration should include the program's ability to parse the case, extract relevant
information, identify entities, and ultimately deliver a reasoned response to a specific legal query. Emphasise that the program must remain fully compliant with the established LAWLIA
structure and reasoning, without deviating from it. Here is the Contract Law class from the
Lawlia Package:
{the class to be run comes here}

After giving it that input it assimilates the grammar and waits for another input for you to give it a case to analyse which could be done like this:

Human : Analyse the provided case using the principles and logic outlined in the "LAWLIA" class
and its methods, predict the outcome. Your response should include:
Step-by-step reasoning: For each part of the analysis, SHOW with code how the "LAWLIA" class
is implemented step by step and how the relevant methods from the class are used to arrive at a
result and provide the reasoning behind the results. Clear identification of the source: Indicate which part of the "lawlia" structure you are referencing for each step of the analysis.
Here's a case for analysis:
Case Name: Johnson v. Smith
Summary:
Case Type: Contract
Jurisdiction: Michigan
Date: 20th January 2023
Parties:
Plaintiff: Johnson
Defendant: Smith
Case Background:
A contractual dispute between a homeowner and a construction contractor. The plaintiff, Mr. Johnson, is a homeowner residing in Michigan. The defendant, Mr. Smith, operates a construction company, "Smith & Sons Builders LLC." On 15th January 2022, Mr. Smith provided a written estimate to Mr. Johnson for a kitchen remodeling project at Mr. Johnson's residence. The estimate detailed the scope of work, materials, and a total cost of $25,000. Mr. Johnson agreed to the estimate and signed a contract with Mr. Smith for the kitchen remodeling project. Per the contract, Mr. Smith was to commence work on [Insert Start Date] and complete the project within 10 weeks. Payment terms stipulated that Mr. Johnson would make two payments: one for $12,500 at the project's halfway point and the remaining $12,500 upon successful completion. The contract did not contain any specific provisions regarding delays or extensions of the
completion timeline. Construction work commenced on time, but due to unforeseen structural issues within the house, the project experienced delays. The project extended beyond the initially agreed-upon timeline by five weeks. As a result, Mr. Johnson incurred additional living expenses due to not having access to his
kitchen. Mr. Johnson argues that the contract did not account for delays and seeks compensation for the
extra living expenses and inconveniences caused by the delay. Mr. Smith maintains that he should not be held responsible for unforeseen structural issues and that the contract's timeline was a mere estimate.
Legal Question: Is Mr. Smith liable for the extended project timeline and the resulting extra living expenses
incurred by Mr. Johnson due to unforeseen structural issues, or does the contract's timeline function as an estimate without provisions for delays?
Please perform the analysis following the guidelines mentioned above. Remember you are a
computer running the LAWLIA computational grammar you are to give output

After that it should be able to give an output similar to this, this was the response on google PaLM.

import lawlia
# Create a ContractLaw instance
contract = lawlia.ContractLaw(
contract_name="Kitchen Remodeling Contract",
parties=["Johnson", "Smith"],
effective_date="15th January 2022",
expiration_date="15th April 2022",
terms="""
1. Scope of Work: The Contractor agrees to provide kitchen remodeling services as per the
attached specifications.
2. Materials: The Contractor will provide all necessary materials for the project.
3. Cost: The total cost of the project shall be $25,000.
4. Payment Terms: The Owner agrees to make two payments: $12,500 at the project's
halfway point and the remaining $12,500 upon successful completion.
5. Timeline: The Contractor agrees to commence work on [Insert Start Date] and complete
the project within 10 weeks.
6. Delays: The Contract does not contain any specific provisions regarding delays or
extensions of the completion timeline.
""",
governing_law="Michigan",
)
# Step 1: Check if the contract is valid
current_date = "20th January 2023"
is_valid = contract.is_valid_contract(current_date)
# Step 2: Identify the parties involved
parties = contract.parties_involved()
# Step 3: Get the contract terms
terms = contract.get_contract_terms()
# Step 4: Evaluate the breach claim
breach_claim = contract.evaluate_breach_claim()
# Step 5: Evaluate the misrepresentation claim
misrepresentation_claim = contract.evaluate_misrepresentation()
# Step 6: Evaluate the warranty claim
warranty_claim = contract.evaluate_warranty()
# Step 7: Resolve the contract dispute
party_a_demand = "compensation"
party_b_demand = "no liability"
resolution = contract.resolve_contract_dispute(party_a_demand, party_b_demand)
# Step 8: Evaluate the contract
evaluation = contract.evaluate_contract(current_date)
# Step 9: Get all contract details
contract_details = contract.get_contract_details()
# Step 10: Print the analysis results
print("Analysis Results:")
print("-" * 20)
print(f"Contract Name: {contract_details['Contract Name']}")
print(f"Parties: {', '.join(contract_details['Parties'])}")
print(f"Effective Date: {contract_details['Effective Date']}")
print(f"Expiration Date: {contract_details['Expiration Date']}")
print(f"Governing Law: {contract_details['Governing Law']}")
print(f"Is Valid: {is_valid}")
print(f"Breach Claim: {breach_claim}")
print(f"Misrepresentation Claim: {misrepresentation_claim}")
print(f"Warranty Claim: {warranty_claim}")
print(f"Resolution: {resolution}")
print(f"Evaluation: {evaluation}")
'''

The output of the program:

'''
Analysis Results:
--------------------
Contract Name: Kitchen Remodeling Contract
Parties: Johnson, Smith
Effective Date: 15th January 2022
Expiration Date: 15th April 2022
Governing Law: Michigan
Is Valid: True
Breach Claim: No breach claim asserted.
Misrepresentation Claim: No misrepresentation asserted.
Warranty Claim: No warranty asserted.
Resolution: No resolution required.
Evaluation: Contract has expired.
'''

Contributing

LAWLIA is an open-source project that welcomes contributions from legal experts, computer scientists, and enthusiasts passionate about advancing computational jurisprudence. You can contribute to LAWLIA's development by:

• Enhancing existing functions to improve accuracy and coverage.

• Adding new functions and classes to cover more legal scenarios.

• Expanding LAWLIA's knowledge base with legal precedents and principles.

• Reporting issues or bugs you encounter while using LAWLIA.

To contribute, fork the LAWLIA repository, make your changes, and submit a pull request.

License

LAWLIA is open-source and is released under the GNU Public License. You are free to use, modify, and distribute this code for your legal research and projects.