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Dec 04, 2025
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2025-2026 Undergraduate Catalog
AI and Logic and Ontology BS
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 Return to: Majors and Combined Degrees
UB is the world center of research and education in Applied Ontology, and the demand for UB-trained ontologists has been expanding rapidly both nationally and internationally since the turn of the millennium. This program provides students with a solid foundation in the applied ontology, formal logic, and artificial intelligence techniques that are in such high market demand. The discipline of Ontology has its origins in philosophy but has over the past two decades been of increasing importance to computer science, linguistics, data science, and artificial intelligence. An ontology is a controlled vocabulary, comprising terms and definitions in both natural language and logical formats. Applied Ontologists work with subject-matter experts to create, test, evaluate, and maintain ontologies. The results are used to promote interoperability of data and information systems. Such work requires skills acquired in logic and ontology courses, such as categorical and taxonomic thinking, standardly taught in departments of philosophy. Of growing importance is obtaining expertise in artificial intelligence as well, in the interest of leveraging deep philosophical wisdom towards continuing technological innovation. An important aspect of this program is that students will take courses from specialists in applied ontology, formal logic, philosophy, and artificial intelligence.
Visit the AI and Logic and Ontology academic program page for more information about the academic experience, who you will learn from, opportunities outside of class and what you can do with this degree.
Visit the Philosophy department page for contact information, a brief overview of the department and the curricular options.
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Math Requirement (4 credits)
AI & Society Core (15 credits)
AI & Technology Core (19 credits)
Integrative Courses (6 credits)
AI & Society Capstone (3 credits)
AI & Logic & Ontology Core (28 credits)
Total Credits Required for Major: 75
Additional Degree Requirements Include:
- Additional coursework to fulfill UB Curriculum requirements
- Elective courses as needed to complete the 120 credit hour total
Total Credits Required for Graduation: 120
Total Credit Hours Required represents the minimum credits needed to complete this program, and may vary based on a number of circumstances. This should not be used for financial aid purposes. Academic Requirements
Minimum GPA of 2.000 overall. Curricular Plan
A Curricular Plan provides a roadmap for completing this academic program and the UB Curriculum on time. Your actual plan may vary depending on point of entry to the university, course placement and/or waivers based on standardized test scores, earned alternative credit and/or college transfer credit. All students are encouraged to use this plan in conjunction with other academic planning resources such as your academic advisor, the HUB Academic Advisement Report , My Planner and Path Finder tool. In addition to following this course roadmap, all other admission and academic requirements of this major as listed in the Undergraduate Catalog must be met in order to successfully complete this degree. YEAR 1
Fall Semester Spring Semester
YEAR 2
Fall Semester Spring Semester
YEAR 3
Fall Semester Spring Semester
YEAR 4
Fall Semester Spring Semester
TOTAL CREDITS REQUIRED: 120 Note: Some classes may count toward both a major and UB Curriculum requirement. Learning Outcomes
Society learning outcomes - Classifying and differentiating between major kinds of AI technologies and how they can be used to advance the social good in education, government, business, and other areas of society, while keeping in mind potential societal harms
- Acquiring knowledge of ethical issues surrounding the use and development of AI and applying that knowledge to determine how to use AI technologies ethically in a given context
- Situating new AI technologies with respect to earlier disruptive technologies in order to be able to compare and contrast AI with other major technological advances both in terms of societal good and harm.
- Assessing the impact of AI technologies on the development of policy proposals in both technological and nontechnological domains
- Determining how AI technologies interact with social structures and the ways that they can reinforce existing biases and structural inequities and developing ideas for how their use can mitigate such biases
- Identifying the impact of AI technologies on how people communicate with each other and assessing when these impacts are either harmful or beneficial to the social good
Technology learning outcomes - Mastering basic linear algebra skills such as defining vectors and matrices, computing their properties, and understating how they are used in AI computations
- Gaining an understanding of probability distributions, statistical properties of data, and their applications in AI
- Designing and implementing computational artifacts
- Applying foundational computational thinking skills and artifacts to analyze and interpret real-world data
- Understanding the social and ethical implications of AI computation
Experiential learning outcomes - Function effectively as a member or leader of a team engaged in activities appropriate to the program’s area of focus
- Apply concepts from linguistics and computer science to the application of human language technologies to real-world problems.
Integrative learning outcomes - Apply AI theory and methods to produce computationally-based solutions to problems related to geospatial analytics
Ontology Implementation Outcomes - Comprehend the core concepts of logic and applied ontology, as well as the basic literature that assumes such concepts.
- Understand the place of philosophy and logic in its broader cognitive and social context.
- Achieve awareness of standardized methods for ontology development.
- Master the ability to construct arguments for choosing between alternative analyses of architecture implementations and to identify relevant data bearing on such analyses.
- Achieve awareness of novel and emerging strategies for leveraging artificial intelligence methods in ontology implementations.
Ontology Modeling Outcomes - Analyze a complex domain-specific modeling problems and apply principles of applied ontology and other relevant disciplines to identify solutions.
- Design, implement and evaluate a computing-based solution to meet a given set of computing requirements in the context of the program’s discipline.
- Recognize professional responsibilities and make informed judgments in computing practice based on social, legal, and ethical principles.
- Function effectively as a member or leader of a team engaged in activities appropriate to the program’s discipline.
- Apply computer science theory and software development fundamentals to produce computing-based solutions.
General Learning Outcome - Communicate effectively in a variety of professional contexts.
Learning Outcome Specific to this Program - Apply concepts from formal logic, applied ontology, and computer science to the implementation of ontologies to address real-world problems.
(HEGIS: 07.99 COMPUTER and INFORMATION SCIENCES UNCLASSIFD, CIP: Artificial Intelligence and Robotics 11.0102) |
Return to: Majors and Combined Degrees
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