Course description

­­­­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

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