Master of Science in Logic & Reasoning

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Master of Science in Logic & Reasoning

The Master of Science in Logic & Reasoning offers an advanced exploration of formal logic, argumentation theory, and computational reasoning. This program is crafted for those seeking to delve into classical and non-classical logic, modal reasoning, AI-based inference systems, and cognitive models of rationality. It prepares graduates to develop structured reasoning systems, intelligent algorithms, and decision-support models essential in various scientific and technological fields.

About the Program

The M.Sc in Logic & Reasoning provides a rigorous education in the theoretical underpinnings and practical applications of logical reasoning. Students will engage with a wide range of logic forms, including classical, modal, and non-classical, applying these frameworks to enhance computational reasoning and argumentation models. The curriculum bridges philosophical logic and AI technology, empowering students to design sophisticated inference systems and algorithms that support complex decision-making processes.

Students will learn to apply logical principles to real-world problems, designing algorithms that can reason under uncertainty and construct persuasive arguments in legal, technological, and academic settings. The program also covers advanced topics in cognitive models of rationality, exploring how these can be integrated with AI systems to enhance their decision-making capabilities.

Key Areas of Study

  • Foundations of Classical and Non-Classical Logic
  • Advanced Argumentation Theory and Practice
  • Modal and Computational Reasoning
  • AI-Based Inference Systems and Algorithm Design
  • Cognitive Models of Rationality and Decision-Making

Who Should Enroll?

This program is ideal for individuals targeting careers in AI logic programming, formal argumentation, computational reasoning, and philosophical logic research. It is particularly suited for professionals and scholars who aspire to advance the field of logical reasoning and its application in AI, enhancing both theoretical understanding and practical implementation.

Core Curriculum & Program Structure

Program Courses: 57 credits

Degree Requirements

Total Credits Required: 57 credits

Core Major Courses: 33 credits

Research & Thesis: 18 credits

Electives: 6 credits

Falll Semester 1

LOR 601 – Advanced Propositional & Predicate Logic (3 credits)

LOR 602 – Non-Classical Logic & Modal Reasoning (3 credits)

LOR 603 – Computational Logic & Automated Theorem Proving (3 credits)

LOR 604 – Rational Decision-Making & Argumentation Theory (3 credits)

Research Methods in Formal & Computational Logic (3 credits)

Spring Semester 2

LOR 605 – Logic for AI & Knowledge Representation (3 credits)

LOR 606 – Paraconsistent, Fuzzy, and Quantum Logic (3 credits)

LOR 607 – Epistemic & Deontic Logic in AI (3 credits)

LOR 608 – Formal Argumentation & Persuasion Models (3 credits)

Research Project in Applied Logic & Reasoning (3 credits)

Falll Semester 3

LOR 701 – Cognitive Reasoning & Theories of Rationality (3 credits)

LOR 702 – Logical Foundations of Decision Theory (3 credits)

LOR 703 – Ontological Foundations of Logical Structures (3 credits)

Elective in AI Logic Systems or Computational Reasoning (3 credits)

Independent Research in Logic & AI Reasoning (3 credits)

Spring Semester 4

LOR 704 – Capstone Thesis in Logical Systems & Argumentation (6 credits)

LOR 705 – AI-Based Logical Reasoning & Formal Decision Systems (3 credits)

Final Research Elective or Internship in Applied Logic (3 credits)