General description of this course
A fundamental
challenge in artificial intelligence is the construction of machines capable of
reasoning with common sense. Common sense reasoning methods can help machines
make more robust decisions, based on consistent assumptions about the real
world, and can significantly simplify human-machine communication.
This challenge
is one of the most difficult problems in building machines with human level intelligence.
The scientific community in artificial intelligence has proposed for several
decades partial solutions to build machines with common sense (e.g.,
considering extensions of classical first order logic, as well as other types
of approaches, such as qualitative representations, etc.). The recent
technological advances, such as big data, machine learning, natural language
processing, etc., have facilitated the proposal of new methods of
representation and extraction of common sense knowledge.
The goal of
this course is to present the main areas of common sense reasoning with special
attention to the recent advances of this field within artificial intelligence. First,
the course describes inference methods and algorithms that simulate common
sense reasoning (logic-based methods, physical reasoning, etc.). Then, the
course describes how to build common sense knowledge bases, reviewing different
approaches that cover both manual and automatic methods. Finally, the course
presents applications of common sense reasoning in areas such as
question-answering systems, natural language understanding, etc.
Learning outcomes
This course is aimed at postgraduates and researchers
in computer science who want to get a comprehensive understanding of artificial
intelligence and its fundamental problems and solutions in the particular field
of common sense reasoning. As learning outcomes of this course, students will
be able to:
1. List the main challenges of common sense
reasoning in artificial intelligence.
2. Describe both theoretical and practical
achievements of common sense reasoning in artificial intelligence.
3. Explain the current scientific and
technological limitations of simulating common sense reasoning.
4. Recognize the main contributors (e.g.,
scientists) and research centers in the area of common sense reasoning.
5. Formulate areas of applications of common
sense reasoning.
6. Find specialized bibliography about common
sense reasoning.
Course content
Part I: Introduction
1. Introduction to common sense reasoning
Part II: Common sense reasoning methods
2. Simulating common sense reasoning
3. Event calculus
4. Physical reasoning
5. Temporal and spatial reasoning
Part III: Common sense knowledge bases
6. Building common sense knowledge bases
7. Manual acquisition of common sense
knowledge bases
8. Collective acquisition of common sense
knowledge bases
9. Automatic generation of large scale data
bases
10. Learning common sense knowledge
11. Integrating common sense knowledge
Part IV: Applications
12. Using natural language to access to data
13. Challenges in natural language
understanding
14. Understanding user intentions
15. Other applications of common sense
reasoning
Requirements and prior knowledge
This material
of this course was created to be used in the postgraduate master’s degree in
Artificial Intelligence (Universidad
Politécnica de Madrid). It is assumed that students are familiar with
general methods of computer science (e.g., formalization of computer
algorithms) and basic concepts about artificial intelligence (e.g., knowledge
representation).
Teaching material
The material
of this course includes mainly sets of slides and selected publications (books
and scientific papers) that cover relevant content related to common sense
reasoning. Slides have references to publications where students can find more
detailed information.
Cite this course
This course
may be cited using the following format:
Molina,
M. (2019). Common sense reasoning [Lecture slides]. OpenCourseWare, Universidad Politécnica de Madrid. Retrieved
from http://ocw.upm.es/course